This paper addresses the optimal pre-cooling problem for air conditioners (AC) used in Internet of Things (IoT)-enabled smart homes while ensuring that user-defined thermal comfort can be achieved. The proposed strategy utilises renewable energy generation periods and moves some of the air conditioning loads to these periods to reduce the electricity demand. In particular, we propose a multi-stage approach which maximises the utilisation of renewable energy at the first stage to satisfy air conditioning loads, and then schedules residual energy consumption of these loads to low price periods at the second stage. The proposed approach is investigated for the temperature and renewable generation data of NSW, Australia, over the period 2012–2013. It is shown that the approach developed can significantly reduce the energy consumption and cost associated with AC operation for nearly all days in summer when cooling is required. Specifically, the proposed approach was found to achieve a 24% cost saving in comparison to the no pre-cooling case for the highest average temperature day in January, 2013. The analysis also demonstrated that the proposed scheme performed better when the thermal insulation levels in the smart home are higher. However, the optimal pre-cooling scheme can still achieve reduced energy costs under lower thermal insulation conditions compared to the no pre-cooling case. There is currently a global energy crisis, as energy consumption across the world is increasing at a significant rate every year. Residential consumers contribute significantly to this total global energy consumption, to seasonal and daily peak demands, and account for a total of 30% to 40% of energy consumption around the world . A signiExisting demand response initiatives rely on incentive-based programs where participants are offered participation credits, or credits based on performance. These programs include direct load control (DLC) , interruIn existing research, for effective residential demand management, the trade-off between the cost of energy and the inconvenience cost has been considered as a critical factor for the encouragement of customer commitment and satisfaction with demand response programs. If a customer has a higher budget, they may be less likely to be tolerant of inconvenience or discomfort with higher energy costs. There are several approaches that have been described in the literature to model customer preferences and convenience as an objective function, either considering these as a set of constraints, or attempting to model energy costs simultaneously with consumer inconvenience. For example, the authors in  considerRecently, a number of meta-heuristic optimisation algorithms, such as grey wolf optimisation (GWO) , teachinExisting demand management solutions available in the market often rely on DLC schemes for customer appliances, such as hot water systems, pool pumps and air conditioners . For exaEffective demand management solutions for heating and cooling appliances need to address the thermal comfort considerations of end-users. One of the widely used standards for designing and building air conditioner systems is the ANSI/ASHRAE  Standard 55 which takes into consideration all factors, including temperature, humidity, clothing, air speed, and metabolic rate, among others . The sta to +0.5 . Fanger Several studies have considered optimising the thermal setting of air-conditioners in commercial buildings. Hsiung et al. proposed the use of PMV as a more accurate representation of human feelings, and particle swarm optimization to find the optimal trade-off between thermal comfort and power consumption in office buildings . The autThe key research papers that consider optimum energy management for heating/cooling loads are summarised in The research papers discussed in the previous subsection focus mainly on reducing the energy cost by optimising the operation of air conditioners. Though many of these studies have considered customer comfort, they assume that users can respond flexibly to changes in their preferred temperature. However, there are user groups, such as elderly people and young children, who are vulnerable to changing temperatures. Very recently, pre-cooling technologies have been implemented while satisfying customer comfort to reduce stress on the distribution network. However, the fact that proper utilisation of renewable energy in determining the optimum pre-cooling period can further reduce energy consumption and cost has not received much attention. Given that a significant percentage of residential users have now installed rooftop solar panels, it is essential that optimum pre-cooling strategies are developed by harnessing the joint benefits of renewable generation and lower price periods. In this regard, we have developed an approach for optimum pre-cooling in IoT-enabled smart homes by considering both these factors. Moreover, we consider a fixed temperature as the comfortable temperature (which can be different for different houses or different set-points at different days for the same user) so that users’ comfort level is not sacrificed. To the best of our knowledge, the existing research has not considered such an approach for optimum pre-cooling.We develop a multi-stage approach to optimise pre-cooling for air conditioners (AC) in IoT-enabled smart homes which can achieve desired customer set-points while minimising energy costs by shifting the air conditioning load to renewable rich- or low-price hours. The first stage involves optimising the utilisation of renewable generation and can reduce overall electricity consumption. The second stage maximises the use of low-price time periods for pre-cooling the AC and can reduce electricity cost.The proposed approach is implemented to investigate the electricity consumption and cost savings using a real world residential energy consumption and generation dataset by considering pre-cooling for summer days during 2012–2013. Given that the impact of structural parameters, such as thermal resistance, is significant for the effectiveness of pre-cooling, the performance of the proposed approach under different thermal resistance settings is also considered.The analysis demonstrates that cost savings of at least 15% are achievable when either the daily peak or daily average temperature is the highest. The savings decrease to 6% when the thermal resistance drops by 50%, indicating that the proposed approach is more beneficial for smart homes with good insulation levels. Thus the proposed pre-cooling strategy can be integrated with thermal insulation upgrade initiatives to improve the energy efficiency of residential buildings.Based on the research gaps identified, this paper makes the following contributions:The paper is organised in the following manner: The heat gain in a house is closely related to the difference between the indoor and outdoor temperature and the equivalent thermal resistance of the house. Thus, in this section, we first describe a model to define the correlations among the energy consumption of an air conditioner, the indoor and outdoor temperature and the thermal resistance of the house. The energy consumption of the AC at a certain time instant can be represented as a function of the previous instant’s room temperature, the air temperature and the current room temperature, in the following manner :(1)PAC,tThe above equation shows that, if the room temperature at the previous time instant can be set equal to or slightly lower than the current (desired) room temperature, the energy consumption of the AC can be significantly reduced (even to zero). This clearly indicates that turning on the AC at the set-point or lower temperature beforehand can reduce energy consumption of the AC, especially during peak time. For example, to achieve a temperature of 21 C at 5 p.m., the AC can be turned on at 18 C at 3 p.m. and turned of at 4 p.m. We utilise this fact to optimise the energy consumption of the AC.Next, we discuss our approach to minimising energy consumption of the AC through pre-cooling. The pre-cooling can be optimised to achieve two goals: (i) reduced electricity consumption (through the use of solar energy), (ii) reduced electricity bills (through the use of a ToU tariff). In both cases, the energy consumption during peak hours is reduced and, as a result, the pre-cooling of the AC can provide a demand response to mitigate blackouts during critical peak demand events. Thus, the optimisation problem for scheduling pre-cooling can be represented as:Note that, in this study, The term PAC,t in  is reprePAC,t in . The rooNote that thermal comfort is indicated by whether the room temperature can be maintained at the desired set point or not. To achieve the desired temperature at Note that, when the renewable generation is not sufficient to meet the energy demand, the optimisation solution in  aims to Generation Optimisation and (ii) Stage 2: ToU Optimisation. The following subsections elaborate the objective functions as well as constraints associated with each stage.We adopt a multi-stage approach to solve the optimisation problem in  in a dayIn this stage, the use of excess energy from renewable generation is optimised to reduce energy consumption from the utility grid. Thus the optimisation problem at Stage 1 can be represented as:t so that the set-point temperature can be maintained throughout the time period t. The room temperature at time t is calculated based on the air temperature at time Here, the objective function aims to minimise the difference between total AC power consumption and the total excess solar generation over the operating time period. The first constraint ensures that the AC power consumption does not exceed the solar generation. The second constraint is used to calculate how much power consumption is required by the AC at time generation optimisation problem, the residual energy consumption requirement of the AC to achieve the desired set-point temperature is obtained. If the residual energy consumption is more than zero, then Stage 2 optimisation is performed as described in the following subsection.After solving the In this stage, the electricity consumption is optimised to reduce the consumption during peak (high price) hours. Thus the optimisation problem at Stage 2 can be represented as:Here, the objective function minimises the electricity cost by considering the different pricing for peak and off-peak intervals. The three constraints are similar to those in Stage 1, as explained in the previous subsection. The solution approach combining the two optimisation stages is explained below.At the beginning, the temperature set-points for the next day are requested from the user. For a specific set-point at generation optimisation stage will be considered for a certain pre-cooling time instant. Here, the available renewable generation is used to achieve the target pre-cooling temperature. For each pre-cooling time instant, the electricity consumption of the AC to achieve the pre-cooling temperature goal is calculated using  for the subsequent time instants are updated. Once the energy consumption and energy cost are calculated for all the possible pre-cooling time instants, the energy cost of the solutions is compared with the previous best solution. Initially, the best solution is the energy cost with no pre-cooling. If pre-cooling improves the energy cost, the temperature values are updated based on the solutions. The process is repeated for each time instant for which the temperature set-point is defined by the user.The approach is illustrated in the flowchart in In this section, we evaluate the implementation of the proposed optimum scheduling approach. The following subsection provides details of the case study we considered in this paper.We used the Ausgrid solar home electricity dataset which defines the energy consumption and generation values for 300 houses in New South Wales, Australia, at 30 min intervals between 1 July 2012 and 30 June 2013 . The datlocation . We consWe consider a set-point temperature of 25 C from 5 p.m. to 9 p.m. for all houses in the dataset. There are 41 days in the dataset when the ambient temperature is high enough to use the AC for cooling. From our analysis, we found that, for 37 days, most of the houses in the dataset achieved improvements in energy consumption and cost when the proposed optimisation scheme was implemented. The houses which did not achieve improvements, had sufficient renewable generation and lower energy demand, which allowed them to achieve the desired set-point temperature without consuming from the utility grid.We also implemented the algorithm using an Itron Riva board, which is an embedded Linux computer board with memory and storage. The algorithm is written in Python and run in the board using shell scripts. A general purpose input output (GPIO) pin was used to run a small 5V fan during optimal times. The implementation setup is demonstrated in In this paper, we demonstrate the improvements for a specific house, ID 165, for days with highest average (8 January 2013) and highest peak temperature (18 January 2013). In addition, the impact of thermal resistance on the energy consumption for the AC is investigated. Finally, the reduction in peak kW consumption over the 41 high temperature days during December, 2012 to February, 2013 is also demonstrated. House ID 165 had a solar panel capacity of 1.02 kW. The thermal resistance of the house was considered to be Based on the temperature data from , it was The indoor temperature when optimum pre-cooling is applied is shown in We also investigated the reduction in energy consumption for the day with the highest peak temperature, which was 18 January 2013. The additional cooling requirements for this date would have been higher as the peak temperature during the day was much higher In this section, we consider the reduction in thermal resistance of the house ID 165 by 25% and 50%, and evaluate the performance of the proposed scheme. When the thermal resistance is reduced by In this subsection, the PAR and electricity cost savings for house 165 under the different scenarios considered in the previous subsections are investigated to understand the impact of pre-cooling. Similar trends can be observed from the daily electricity costs shown in Here we investigate the daily consumption of the AC during peak hours for House ID 165 over the days when cooling was required and evaluate the savings in daily peak hour consumption with the optimum pre-cooling scheme. Peak hours of use in NSW occur between 2 p.m. and 9 p.m. and the analysis was performed for the energy consumption of the AC during these hours. This paper proposes a new approach to optimise the pre-cooling operation for ACs in IoT-enabled smart homes. The approach maximises the utilisation of renewable generation, as well as the benefits of consuming energy at a low price period. The proposed optimum pre-cooling approach was applied to compute the energy cost savings based on renewable generation, weather and energy price data for NSW, Australia. The analysis showed cost savings of 24% and 15% for the day with highest average temperature and highest peak temperature in the 2012–2013 summer period. Moreover, the peak energy consumption of the AC could be reduced for 38 days of the 41 days when cooling was required. Future work will focus on extending the analysis to accommodate other types of thermostatically controlled loads, such as electric heaters and water heaters.


The article presents the analysis of the impact point dispersion reduction using lateral correction thrusters. Two types of control algorithms are used and four sources of uncertainties are taken into account: aerodynamic parameters, thrust curve, initial conditions and IMU errors. The Monte Carlo approach was used for simulations and Circular Error Probable was used as a measure of dispersion. Generic rocket mathematical and simulation model was created in MATLAB/Simulink 2020b environment. Results show that the use of control algorithms greatly reduces the impact point dispersion. Determining and lowering the impact point dispersion of an artillery projectile is an important factor when determining its usefulness and effectiveness. Due to the imperfections of modelling of the flight of such objects, unknowns, simplifications, and uncertainties in model parameters, it is important to take into account the various possibilities of scenarios and determine and quantify the dispersion of possible landing points. Key factors influencing the flight of the object, and the hardest to obtain accurately for modelling, are the aerodynamic characteristics, thrust curve, and initial conditions. To maximize the accuracy of the projectile, various types of control methods, algorithms, and target detection methods are utilized, such as Hance law , proportance law ,3, varioance law ,5,6,7 orance law .In ,10 the iIn this paper, the analysis of the impact point dispersion caused by model uncertainties is presented. The uncertainties in the aerodynamic parameters, thrust curve, and initial conditions, as well as the uncertainties caused by the Inertial Measurement Unit model and its errors are analyzed. Two types of control algorithms were tested, Multi-Condition Control Algorithm (MCCA) and modified Proportional Navigation Guidance (mPNG). The analysis was performed using the Monte Carlo approach.The prepared article is organised as follows: in The novelty in this paper is the comparison of the IMU errors’ influence on the accuracy of the two control algorithms utilizing lateral pulse jet control.The developed numerical simulation might be used in design process of new sensors intended for projectile navigation. This tool might be used for fast prototyping of control schemes that reduces the overall system design process and costs.For creating a mathematical and simulational model of a generic rocket, several assumptions were made. The rocket is modelled as a rigid body with six degrees of freedom and variable inertial parameters. It is controlled by a set of solid rocket motor thrusters, which use does not change the inertial and aerodynamic parameters of the rocket. Atmosphere model is taken from the International Standard Atmosphere . Earth rThe navigational coordinate system The gravitational coordinate system The body coordinate system The measuring coordinate system The aerodynamic coordinate system The coordinate systems used are presented in m is the instantaneous mass of the rocket, For developing the dynamic equations of motion, the linear and angular momentum change theorems for the rigid body were used. In the non-inertial frame given as ,27:(1)δ˜body are :(3)Π=mVbFor determining object orientation, the quaternion algebra was used. Quaternion describes the orientation of the object in terms of rotation around a specific axis and is written as ,29:(9)e=quations –.(10)k is the feedback coefficient and E is the bounding equation violation coefficient The kinematic equation for the rate of change of the quaternion is given as ,30:(14)eystem as :(15)Λ=e0given as ,30: (16)Using the transformation matrix from  can be uThe initial quaternion can be determined from the initial orientation angles by means of equations ,30,31: Fb=S is the rocket’s cross section area and d is the rocket’s diameter. Aerodynamic incidence angles are given as [a is the local speed of sound. The aerodynamic coefficients are [Aerodynamic force and moment vectors are given with respect to point Ma,Oe=12ρgiven as : (30)α=aα,β,Ma)2Ma,Oe=12ρcsinV|Vb|Ma=|Vb|aGravitational acceleration vector in the gravitational coordinate system is given as ce model , i.e., gThrust vector, which can deviate from the rocket’s longitudinal axis by angle given as :(37)Fs=FFor the gasodynamic control system comprised of a set of identical correction thrusters placed radially in a set of parallel layers, the thrust and torque from the thrusters are given as:iven as:  and 42)(39)Fski,For the simulation purposes it was assumed that the projectile is equipped in a modular unit  composedThe instantaneous mass of the rocket is given as:The air density, temperature, and the speed of sound are calculated according to the International Standard Atmosphere model .(47)ρ=ρThe reference values of these thermodynamic parameters are taken for the troposphere: It was assumed that the rocket’s is equipped with the strapdown Inertial Measurement Unit with the three-axis accelerometer and three-axis gyroscope, and these are the only sources of information about rocket position, velocity and orientation. Abovementioned design requirements are quite difficult to fullfill due to errors. Pure inertial navigation has a tendency due to drift. These drift errors might be reduced using integration with GPS receivers. Also, additional sensors like might be used to improve the system acccuracy. Magnetometers measurement are imprecise because the projectile and launcher structure are made from steel alloys. The IMU is intended for a projectile that spins about the longitudinal axis of symmetry. Photodiode sensors could be used to measure the projectile roll rate.An example of IMU that is suitable for the considered application is Micro-Electro-Mechanical-Systems based HG1930. The mass of this device is approximately 0.16 kg and power consumption less than 3 W. The operating temperature range is from Accelerometers modelThe acceleration of the rocket’s center of mass in In the general case, the accelerometer position need not to coincide with the center of mass of the rocket. Therefore, the center of mass acceleration must be recalculated to the point of accelerometers’ mounting:Accelerometer is treated as a second-order dynamic system:The gyroscope output does not depend on the gyroscopes’ position inside the rocket. Therefore, there is no need to transform its output to the center of mass. The gyroscope errors’ model includes scale factor, cross-coupling, bias, and sensitivity to accelerations, given by the gyroscopes’ sensitivity matrix Gyroscope is also treated as a second-order system:In order to solve the mentioned earlier navigation equations the initial attitude, velocity and position of the projectile must be known. It was assumed that these parameters are perfectly estimated using initial alignment procedure before the flight.Correction thruster was not used already (solid motor thrusters are single-use motors)The time between the last thruster ignition The correction thruster must be ignited so that the resultant thrust force was in the direction of the desired lateral displacement ,36, whicThe rocket’s pitch angle must be lower or equal the threshold value Projectiles equipped in solid propellant lateral motors often have very low control authority. It means that the maneuverability of such objects is small. This fact makes the guidance process challenging. For the tests of landing dispersion analysis, two algorithms were created: Multi-Condition Control Algorithm (MCCA) and modified Proportional Navigation Guidance (mPNG). The guidance algorithm used in the MCCA is based on the reference trajectory tracking. This algorithm is discussed in detail in . The maiThe distance between the rocket’s center of mass and the reference trajectory Additional conditions for the MCCA algorithm:the norm of the commanded value of the lateral acceleration Additional conditions for the mPNG algorithm:The parameters of the control laws were determined using the expert method and parametric study: The mathematical model described in The program simulates the flight of the gasodynamically controlled rocket, calculates the loads from gravity, aerodynamics, thrust, and correction thrusters. It solves the set of ordinary differential equations for the rigid body with 6 degrees of freedom and variable mass. It includes the models of International Standard Atmosphere and Inertial Measurement Unit as well as the inertial navigation equation for determining the rocket’s position, orientation, and velocity. The equations of motion of the projectile were integrated using fixed step, third order Bogacki-Shampine method. The step size was set to 0.0001 s. Simulations were realized using Simulink build in option “Accelerator mode”. Marsenne-Twister algorithm  was usedA generic rocket model was used for the simulations, which general data are provided in The missile is stabilized with four trapezoidal fins. The maximum flight velocity of this projectile is 605 m/s and maximum roll rate 4700 deg/s . The missile was fired at elevation angle 25 deg. Initial velocity was set to 42 m/s and initial roll rate 1073 deg/s. Projectile was fired from the initial position  m. The reference trajectory of the projectile (unguided flight) is presented in x and y components of the rocket’s and reference trajectories at the impact point, and the distance between the rocket’s landing point and the target position given as As a first step, five deterministic cases were evaluated to test if the prepared control algorithms work as intended: no control, mPNG algorithm with and without IMU model, and MCCA algorithm with and without IMU model. To intentionally introduce aiming error it was assumed that the launch tube is not perfectly aligned with the demanded shoot direction. The initial heading error angle equaled 2 degrees. The position coordinates of the stationary target were set to The results shown in Next, to test the influence of various uncertainties on the performance of control algorithms and the resulting landing dispersion, a few sets of simulations were performed. Tested were uncertainties in aerodynamic data, thrust parameters, and initial conditions. For every uncertainty, again five cases were simulated: no control, mPNG algorithm with and without IMU model, and MCCA algorithm with and without IMU model. Every case took 1000 runs, using the Monte Carlo method, giving a total of 15 thousand runs. As a merit of accuracy, the Circular Error Probable CEP was used. It gives information about the radius of a circle inside which 50% of landing points are located.The first set of Monte Carlo simulations consisted of uncertainties in aerodynamic data. It was assumed that the normal distribution standard deviation of all aerodynamic parameters was equal to In the not controlled case, the CEP was equal to The next set of simulations consisted of uncertainties in the thrust data. For simplicity, it was assumed that the thrust curve can be approximated by the quadrilateral comprised of four characteristic pairs of points, time-thrust, presented in The The last set of simulations consisted of uncertainties in the initial conditions. The values of the initial linear velocity vector, angular velocity vector, and orientation angles were chosen randomly, using a normal distribution, using set values of standard deviation. The Several new aspects brought by the paper might be mentioned. First, the influence of measurement errors on the resulting projectile miss distance was investigated. Second, two different guidance methods intended for lateral thrusters controlled missiles were compared for idealized and realistic case. From the obtained results it might be concluded that it is possible to achieve CEP order of several meters but to realize this goal the missile must be equipped in high-accuracy IMU.Precision guided munition become more and more important in modern military conflicts. To achieve a high direct hit probability the influence of various factors on the resulting dispersion must be understood in detail. In the article, the impact point dispersion, caused by the uncertainties in aerodynamic parameters, thrust curve, initial conditions, and on-board measuring devices, for two types of control algorithms, was presented. Monte Carlo approach was used in the simulations and as the merit of dispersion the CEP was utilized.The results showed that the use of control algorithms greatly reduces the miss distance by more than 80% in most simulated cases. From the simulations it might be concluded that there is possible to achieve CEP smaller than 8 m. For modern guided munition this is quite a realistic result . It means that equipping the projectile with control module composed from lateral thrusters allows effectively reduce the impact points dispersion. The uncontrolled projectile might land even 200 m from the intended location in the worst-case scenario. This issue is very important in modern military applications due to the requirement of minimizing the collateral damage.The IMU model causes a slight increase in the dispersion of about 3% in every case. The mPNG algorithm proved to be better for all simulated cases, the greatest difference between the two algorithms was observed in the initial condition case dispersion.The developed numerical simulation might be used in the design of new measurement systems intended for missile navigation. Parametric model allows on rapid implementation of data for other missiles and IMU-s and investigate the dispersion as a function of measurement uncertainties. In this way the overall time and cost of the projectile design might be reduced.Further works might concentrate on flight tests of the real ground-to-ground projectile and validating the model. Also wind tunnel measurements of the missile could be evaluated to obtain the aerodynamic data for a wide range of flight conditions. The influence of wind on the projectile dispersion might be also explored in detail. Hardware-in-the-loop simulation might be also considered to investigate the influence of sensor errors on the projectile hitting accuracy.


Industrial contexts, typically characterized by highly unstructured environments, where task sequences are difficult to hard-code and unforeseen events occur daily  cannot completely rely upon automation to substitute the human dexterity and judgment skills. Robots operating in these conditions have the common requirement of being able to deploy appropriate behaviours in highly dynamic and unpredictable environments, while aiming to achieve a more natural human-robot interaction and a broad range of acceptability in providing useful and efficient services. The goal of this paper is to introduce a deliberative framework able to acquire, reuse and instantiate a collection of behaviours that promote an extension of the autonomy periods of mobile robotic platforms, with a focus on maintenance, repairing and overhaul applications. Behavior trees are employed to design the robotic system’s high-level deliberative intelligence, which integrates: social behaviors, aiming to capture the human’s emotional state and intention; the ability to either perform or support various process tasks; seamless planning and execution of human-robot shared work plans. In particular, the modularity, reactiveness and deliberation capacity that characterize the behaviour tree formalism are leveraged to interpret the human’s health and cognitive load for supporting her/him, and to complete a shared mission by collaboration or complete take-over. By enabling mobile robotic platforms to take-over risky jobs which the human cannot, should not or do not want to perform the proposed framework bears high potential to significantly improve the safety, productivity and efficiency in harsh working environments. The sector of Maintenance, Repair and Overhaul (MRO) has reached a value of 686.6 Billion USD in 2020, and is expected to grow with a CAGR of 2.3% until 2027, reaching a total value of 787.2 USD . In thisThe recent advances of robotics and artificial intelligence are suggesting that, in this kind of contexts, the introduction of a new generation of service and maintenance robots could be the ideal way to safeguard the health of workers, if they could achieve a comparable quality in performing maintenance operations. Nonetheless, this would hardly be enough to substitute a human worker, as the human decision capacity, especially in unstructured environments, will still be unreachable for years by AI. The best trade-off between safety, reliability and decision capacity is nowadays widely accepted as the human-robot interaction, as testified by the increasing number of collaborative and environment-aware robots proposed by manufacturers .artificial empathy (AE)  and their relation to their actual context, to be able to work with them and to preserve their health. This leads to the necessity of developing robot assistants that implement some notion of thy (AE) . In thisIn addition to understanding some notion of worker status , of course a mobile service robot should provide a service in the context of an unstructured environment, like an offshore oil platform, a wind farm or a gas pipeline. Such operations may be for example the inspection of a difficult-to-reach surface to identify damages, cleaning and de-rusting, re-coating, closure of leaks by welding or additive manufacturing, logistics, and others. Examples of robots with such capabilities are gradually appearing both in research  and as cStarting from these premises, this work is motivated by the need of a holistic approach to study the HRI in harsh environments with the focus on the formulation of adapted robotic behaviors directed towards the alleviation of the perceived stress factors and human exposure to dangerous situations.The main novelty is the use of Behavior Trees (BT) to design a comprehensive HRI framework that enables the robot to select and perform actions adapted to the constraints imposed by the presence of humans with different health and emotional states, requirements and preferences. The framework leverages an extended set of functional robotic capabilities  to address the complexity of human-robot joint decision making dynamics during operation in harsh contexts. The management of the human robot interaction is considered as an integral part of the high-level robot’s control architecture aiming to identify human key behavioral factors affecting the robot’s decisional process.These factors are tackled in various contexts: the robot ascertains the current working situation involving the human “through the eye of an external observer”; the robot grasps psychosocial factors affecting human health and behavior ; the robot takes a task-based perspective to identify, assess and mitigate potential risks with focus on the manufacturing human-robot collaborative process; the robot assigns roles and responsibilities and proposes alternative courses of actions to alleviate the difficulty of tasks to be handled by the humans .Safety HRI behavior tree has the aim to support a human agent by observing her/his psycho-physical status, processing the information and deliberating an immediate decision.• The MRO HRI behavior tree determines the main environmental and situational risk conditions for the operator, generating an empathy map, and based on an interdependence analysis processes a task sequence to propose feasible alternative workload distributions between human and robot, with the aim of minimizing hazards and task failures.• The In order to meet teammates’ needs and requirements in HRI, the proposed framework provides a BT interface facilitating the authoring of robot’s behaviors grouped into two main HRI phases:The paper is structured as follows. This section introduces the main methodological concepts exploited to develop the framework described in behavior trees, which are briefly presented in the following.The main tool used to structure the proposed deliberative framework is the formalism of Behavior trees (BTs) have been introduced in AI programming for the videogame industry, obtaining great success in the last 10 years because of their flexibility and readability. Nowadays, many of the main environments dedicated to videogame programming integrate BTs , offerinroot node, splits into several branches through control flow nodes and is terminated by execution nodes, i.e., are graph leaves. Cyclically, the root node generates a tick, i.e., an enabling signal that follows a different path along the tree depending on the states and types of traversed control flow nodes. Eventually, the tick reaches execution nodes, which are dedicated to performing actual operations. When they are reached by the tick, execution nodes return to the parent node a state variable, which can have value Success, Running or Failure. In turn, control flow nodes collect the states of child nodes, and return their own state depending on them and on their own type. The whole BT is supported by a set of global variables, called Blackboard (BB), which are accessible to all the nodes and are dynamically modified by them.A BT is a directed acyclic graph, which origins from a The node types are described in For a comprehensive introduction on BT, we refer readers to ontinue Task = = false) or the Surface inspection sequence fails.The BT root (possibly a parent node from a BT of higher hierarchy level) ticks at a specific frequency the main inspection task node 2, which will pass the tick to one of its children, depending on the type and policy of the root node. In this example, the evaluation of the children is run sequentially with a first check on task execution clearance 3 followed by the actual surface inspection operation 4. The sequence node 2 fails if either of its children returns failure, e.g., an external signal interrupts the task . If the robot is unable to reach the target position  the Move to position will return a Failure state. Once the position is reached  the evaluation of the Surface inspection tree moves to the next child and the robot checks if a cleaning pre-treatment is required for that kind of surface (pre-condition 7—Cleaning Required?); in case it is required (cleaningRequired = = true), it ticks the Clean action 8, which upon completion will return Success. If no cleaning is required, the sequence node 6 will return failure, which will be converted in success by the Return Success decorator 9. After the optional cleaning, all the available surface inspection methods  are tried by the Scan Methods selector 10, until one succeeds.The modularity, reactiveness and deliberative structure.Some of the most prominent features justifying the use of a BT representation are its Modularity can be easily achieved in BTs by implementing one activity through an independent BT, i.e., a BT that has no interactions with other nodes of the parent tree, other than receiving the tick. This feature of BTs is very important in our framework, as it allows the reuse of subtrees in other trees  to sequeIntuitively, the reactiveness of a system means the formulation of an appropriate response to the various changes in the environment. In the BT case this is achieved through a repeated ticking which frequently polls the environment for its current state to better support the decision making and to make the behavior transparent and predictable .Deliberate action is a central issue in robotics mainly motivated by the need of endowing autonomous robot with critical capabilities to cope with a variety of environments, tasks and interactions . One sucAs anticipated, the proposed behavioral framework has been developed with a focus on unstructured work places, where mobile service and maintenance robots can be effectively used to assist human work activities. The main assumptions regarding these elements are described in the following.2), potentially laid out on more than one floor, comprising a multitude of fixed and moving obstacles. The plant map is known and available to the robot, although variations from nominal can be present at any time because of moving obstacles  or changed landscape. We consider the environment reachable by the robot in navigation mode as bidimensional, or at least easily representable by a multi-layer 2D map, to support multiple decks and irregular but easily traversable terrains. More general cases, where considering environments as intrinsically 3D may be mandatory also during navigation , could be also accommodated by the proposed approach, but they are not treated for simplicity.The target applications are large work plants  to convey commands to a robot in a general, possibly harsh environment is an open research branch, but it is considered out of scope for this work.The facial expression of a human is considered as an important indicator of the worker’s health and emotional status, although it may not be always available to the robot, not only because of visual impairing, but also due to helmets, masks etc. For this reason, it is important to address multimodal agent status recognition, possibly acquiring additional information from voice tone, physiological sensors and body language.The human is considered to have a much superior judgement capacity, compared to the robot, when in a favorable emotional condition , and can be put into discussion only when high danger is associated to a heavily altered emotional or health state .The considered robot is mobile and autonomous, i.e. it is able to navigate between two points on the available plant map without human guidance, automatically avoiding unexpected and moving obstacles. To do this, the robot is equipped with a locomotion system  and proper sensors.The robot is equipped with a vision system, which serves multiple functions. Other than supporting the navigation, as mentioned before, it is used to detect humans, identify them and classify their emotional and health state. Moreover, it is used in inspection tasks, which consist in commanding the robot to reach a location, search for damages, leaks or other visual features that require intervention, and to perform the actual inspection . This can happen either in an unsupervised or supervised manner, with the operator looking through the robot’s eyes by a screen and giving directions about where to move. In addition to the vision system the robot can carry other inspection systems .The robot is able to understand human vocal commands, at least within a pre-defined set of phrases, and to speak itself, at least by a pre-defined set of phrases. If available and allowed by factory policies, the robot may also connect to health-tracking devices worn by a nearby operator and read signals like heartbeat frequency or electrodermal activity.Internally, the robot stores important information to operate in an MRO environment: plant map, worker identifiers and associated features, manufacturing operations programs, and others.base, i.e., a plant operations control center should always be available: primarily, to send emergency signals for injured workers; secondly, to update and validate shared work plans, receive new instructions, ask for equipment, etc.A remote connection to a Finally, the considered robot is able to perform one or more MRO activities. This term is used here to describe any maintenance, repair or worker support operation that requires a tool and a physical interaction of the robot with the environment, other than navigation. In this respect, the robot configuration consists of a mobile platform equipped with a robotic arm, tool changer and is able to carry several manufacturing tools.Trends in the MRO industry suggest a growing need for enhanced robotic automation in order to conduct operations in harsh environments, as well as to improve the safety and efficiency of workers in existing facilities . The aimHuman communication is naturally multimodal, with voice, facial expression and gestures considered as key channels supporting its realization. By analogy, these modes can be considered extremely relevant to achieve a similar natural exchange between the robot and the human teammates. On one hand, the robot must be endowed with the capability to not only accurately capture, analyze and understand the human states and requests, but also to reply adequately. On the other hand, the human teammate should provide information with a language that the robot can parse, and should be fully aware of the reactions and capabilities of the robot. Furthermore, in order to avoid the buildup of mistrust or frustration, he/she must be aware of any sudden change in the autonomy level of the robot and the associated behaviors.One of the main contributions of this work is to elaborate on the generation of shared working plans relying upon a multi-modal human robot interaction and on empathy-relevant behaviors. In this interaction, the actors can have two types of roles: supervisor/control base and teammate. The human operator can assume both roles while the robot can assume only the latter one. As a supervisor, the human is responsible to define and adapt the ultimate goal of the mission as well as its main tasks. Furthermore, in this role the human also monitors and validates the team’s performance towards the team’s goals.The teammate role implies that both human operator and robot execute actions and collaborate locally towards the completion of a mission through the multimodal interaction. The robot is autonomous in the realization of specific tasks by leveraging its capabilities to carry out MRO activities, but requires input from the human teammate and supervisor for the update of the goals and the planning of the mission. By design, the robot is not driven by intrinsic motivations and goals, and in this respect the human will be considered as the final authority responsible to specify the degree of automation expected for each task of the mission. However, the robot is capable of exhibiting empathy-like behaviors by leveraging its manufacturing capabilities to support the human teammate’s goal.These capabilities have parallel representations as behavior tree leaf nodes across two main behavior subtrees of the HRI tree: the Safety HRI subtree and the MRO HRI subtree, as described in In the proposed framework we draw upon sophisticated levels of robotic perception and control to create an artificial sense of the environment and self-awareness, supporting the instantiation of intelligent robotic behaviors during the interaction with other entities in the environment. In particular, during the interaction with the humans the robot will firstly play the role of an observer, meant to assess the agent’s health status well before any further interaction . If thisempathy gap that characterizes social human interactions, i.e. the human tendency to underestimate the influence of varying mental states on our own behavior and to make decisions that only satisfy our current emotion, feeling, or state of being  to carry out an MRO activity, the first behavior to be deployed by the robot is to establish the contact  followed by the implementation of a preliminary behavioral sequence.Starting from the premise the robot should support a  contact . This wi• Initialize TM: instantiates the teammate’s identity (ID property) and Position vector.• Approach TM: assume a socially cognizant trajectory with adapted posture and slowing down as it nears the human.• Maintain distance: the robot approaches the teammate always adhering to common conventions of what constitutes people personal space .• Enhance Attention: the robot’s operative system focuses resources on the sensors required to evaluate the health state of the human.This sequence, assuming the availability of autonomous navigation and the sensitivity to strategic affordances of interaction with humans, includes the following steps:HR Contact tree is to initiate the HRI sequence. The whole HRI behavior tree exploits information coming from the robot’s own sensors and from the control base by wireless connection. It includes the Safety HRI sequence followed by the check of a set of preconditions and finally the MRO HRI sequence, which will be described in detail in the subsequent sections.The last action of the safety concept is addressed across different dimensions, which extend beyond the boundaries of the Safety BT. A first dimension is related to risks that have a physical impact on a person’s safety, such as collision risks. These risks are kept at bay through the execution of the Approach TM and Maintain distance sequence of activities under HR Contact BT. Secondly, the consideration of psychosocial measurements within the Safety HRI subtree contributes to the in-the-moment assessment of the health state and cognitive overload of the human teammate. It is assumed that if the robot is perceived to be dangerous, then the TM affective state would be tense. This can be addressed by the robot with an adapted questionnaire during the verbal interaction with the TM (i.e. Safety HRI/Interaction subtree). Overall, the robot promotes a safer workspace by indicating or preempting a danger either for the human health, for the success of a task or for the plant functioning and formulating a proper reaction to them . Furthermore, the integration of the human behavioral factors into the planning of the human-robot shared activities aims to safeguard the human health status by assigning high-risk tasks to the robot (i.e. MRO HRI subtree) if potential dangers are detected in the current plan.Within our framework, the Grounding: determines the teammate’s health state by processing information acquired by sensors.• Interaction: tries to obtain further information by explicit verbal interaction with the teammate.• Deliberation: takes an action consequent to the determined HealthState of the teammate.• In particular, the Safety BT contains the conditions and actions carried out by the robot to assess the health state of the actively engaged human teammate and subsequently to deliberate on alternative courses of action. The Safety HRI sequence consists of three subtrees :• Ground• The data structure TM (Teammate) of type Human, which collects information about the human worker presently involved in the human-robot team.• The MROrequired Boolean variable, which enables the execution of the MRO HRI subtree, based on the mission goal.• The MROenabled Boolean variable, which is turned off if the conditions for proceeding to the MRO HRI after the Safety HRI are not satisfied.Moreover, the tree’s blackboard includes the following objects:Grounding sequence .o Posture .o Gesture .o Physiological signals .• Feature extraction: uses all the available sensors to extract features related to health state expression. The following options exemplify commonly analyzed aspects, but others can be included within the same framework:In this case, the action nodes that execute feature extractions return success at the end of the operation, both in case of successful and unsuccessful feature recognition. The availability of features to analyze is managed by the following sequence:Features found? condition), the health state classifier uses all the available ones to determine two labels: the HealthState of the teammate and whether he/she is paying attention (isAttentive variable) to the robot or not.• Multimodal state classification: if at least one feature has been found in the feature extraction phase , the robot tries to obtain her/his attention, for example by calling her/him by TM. ID.• Dialogue: a series of questions and answer collection phases aims at narrowing the HealthState uncertainty by direct comments .• VoiceTone analysis: another pre-trained classifier, focused on voice tone recognition, enriches the multimodal information.• Answer analysis: the answers received in the Dialogue phase are parsed by Natural Language Processing methods, potentially modifying or adjusting the provisional HealthState.• After the first phase of teammate . This isHealthState, and determines the action to take. The proposed criterion is a proof of concept, easily customizable to the policies that the employer implements in the target plant. In this example, three cases are managed :M.HealthState≥Injured: if the HealthState corresponds to Injured or a more severe estimated condition, rescue is directly called for the human worker in danger.• TM.HealthState == Fatigued: if the is able to proceed but her/his state is evaluated as fatigued, the situation is signaled to the control base, and the robot asks directly to the teammate if he/she feels that the manufacturing phase (if required) can be undertaken.• TTM.HealthState == Normal: if the worker is healthy, the clearance is signaled to base and if there is a request to support MRO activities then the variable MROenabled is set to true. In case no request for such activity is needed the robot can, for instance, sets the target position towards which to navigate next and the Safety HRI subtree can be exited with a Success status.• The Deliberation node  evaluateMRO HRI subtree, whose main phases are illustrated in Safety HRI subtree only if a number of pre-conditions evaluate to true  and capturing the experiences and opinions of workers in terms of feelings and risks .• Sense—information collected on-the-job by the robot, to enrich the active context .• Say—collects contextual information provided by the human teammate during the actual human-robot interaction .• Feel—captures the human teammate feelings about the job at hand during the actual human-robot interaction . The emotional measures can be based on the circumplex model of emotions, which is a widely used bidimensional model used to capture a wide range of positive and negative affect encountered in common HRI scenarios  and pleasantness/unpleasantness . The focus can be directed towards fundamental emotions like happy, stressed, relaxed, sad, alert prior to the definition of the shared plan.• cenarios . AccordiEmpathy maps are tools that are neither chronological nor sequential . NeverthHealth Risk• Operational Risk• Equipment Risk• These five dimensions contribute, for each task Demanding cognitive and physical tasks are more prone to error-producing conditions, and an inadequate psycho-physical state of the human could increase the likelihood of risk modes. The aim of the proposed framework is then to mitigate these risks thanks to the support of human-aware robotics.human errors and at-risk behaviors, which could possibly lead to accidents, or affect reliability, productivity and efficiency of operations and equipment.Before engaging into direct interaction with the human teammate, the robot plays the role of an external observer. The starting point is the identifications of possible endogenous and exogenous. Endogenous errors are defined as caused by negative personal performance shaping factors, such as anxiety, stress, fatigue, fear, sensory processing disorders and other psychosocial factors. Performance in the workplace is also challenged by the role of the endogenous circadian rhythm . Moreover, it senses a form of hesitation in the teammate’s voice with respect to the execution of the blade inspection via rope access, due to the windy weather Example 1. Initially, the robot is transported by car to the wind farm location; after its start routine and the execution of the Safety HRI subtree it evaluates the health state of the designated teammate as normal. Next, through the execution of the Build EMAP subtree, the robot engages into a dialogue with the teammate, during which it receives the mission information . In particular, subtask k, reaching a damaged blade by climbing down a rope, has consequence C = 3 (the worker oscillates on the rope and gets injured by hitting it) with likelihood .Some additional examples are proposed in Humans and automated systems have been traditionally assigned to separate functions, but recent advances permit a more fluid relationship approximating human-robot teaming paradigms . IdeallyEvaluate Risk sequence, i.e. the ones with a high intrinsic health risk. The Interdependence Analysis relies upon a child sequence subtree designed to iteratively assess the robot’s ability to perform for each task from the candidate list. Firstly, a hierarchical decomposition based on the human-aware task planning formalism (Performer role), 2) the robot needs support to solve the task  and 3) the robot cannot provide any form of assistance to solve the task . By default, a task considered critical for the human health can be fully subsumed by the robot only if the required capabilities are available, i.e., the robot has the sensing, locomotion and working skills required to fully perform the task reliably. In other cases, the human assistance might be required due to either a lack of robot capability or to simply enhance the reliability of the process. In this respect, the Design Shared Task activity aims to identify a suitable distribution of the subtasks of any shared human-robot task by defining the structure, process and nature of the potential interactions between them.The tasks considered for a different human-robot workload distribution are mainly the potential candidates defined in the ormalism  is carriExample 2. Before starting any inspection or repair operations, the robot needs teammate assistance to reach the blade surface. The conventional setup approach used by human workers, containing the subtasks ST1-Climb inside the windmill tower, ST2-Access the nacelle, ST3-Lock the rotor and ST4-Hang the rope, cannot be executed autonomously by the robot. This setup task is redesigned for a human-robot interaction, considering two additional subtasks: ST5-Hoist the robot (teammate) and ST6-Attach robot to the blade (robot and teammate). For the last subtask, the teammate operating the rope attached to the robot gets access to the images transmitted by the robot’s camera, to facilitate its precise positioning on the blade .Observer role, to enable a learning from demonstration process based on the observable human’s routines and working environment.If the robot is unable to provide any form of support towards the completion of a task, it can assume an The completion of a subtask can involve a particular sequence of actions which promote interactions between the teammates at various levels of abstraction. At this stage, additional metrics suitable for the manufacturing environments need to be defined. These metrics are supposed to complement the risk values calculated previously for each task, to support the definition of appropriate cost functions for a holistic assessment of the possible task plan alternatives and eventually for the selection of the best fit plan for a specific scenario. While in the manufacturing field time efficiency and quality are crucial expectations, other metrics capable of giving an indication of the human wellbeing based on ergonomic considerations are recently gaining momentum .Interdependence Map will be generated, reporting the role of each teammate together with the adequate timeline, context-dependent cost function and associated risk. This centralizes the role of each teammate, the capability to either directly perform the work or to ensure a supportive function, and the nature of interdependences, with indication of possible limitations and reliability levels across all tasks.Once all tasks from the candidate list are processed, an Alternative Workflows sequence .The last activity within the Example 3. As a result of the successful completion of the Interdependence Analysis and Alternative Workflow subtrees, the operation-to-tick queue for the automated leading-edge repair of the turbine consists of the following sequence of behaviors: BT1-Setup (as previously described in Example2), BT2-Inspection (detailed blade inspection before repair), BT3-MRO Prep (surface preparatory operations before repair), BT4-Setup , BT5-MRO Repair , BT6-Inspection (detailed blade inspection after repair), BT7-Setup (descend to the ground once the repairing is complete). The robot and the human teammate are deploying either individual or collaborative behaviors to complete the mission.Alternative Workflows subtree, the MRO Shared Plan subtree, depicted in In contrast to the MRO Shared Plan sequence), the operations-to-tick queue is loaded from the blackboard and with the support of a repeater node the corresponding operations are initialized and executed in the given order. Further down, a selector defines which behavior subtree needs to be expanded and instantiated for execution. The tree exploits logical pre- and post-conditions as well as sensory data to monitor the execution of each behavior.Starting from the root : 1) the first inspection (BT2) consists of a laser scanning, used to generate a 3D model to assess the blade condition and to adjust the tools required before the repair operation; 2) the second inspection (BT6) consists of a laser scan of the blade after the repair, to validate the results and to guarantee the highest quality. Both inspection behaviors employ the same BT structure but with differently parametrized templates based on teammate’s input.Execute Inspection subtree is proposed in A possible configuration of an inspection behavior with the support of robot’s capabilities to navigate to a specific location and to deploy an inspection method that can function independently of lighting conditions, contrast, reflection and position. A list of checkpoints to be visited consecutively by the robot can be provided initially by the teammate. As long as the next checkpoint is not reached, the behavior of the robot is driven by a parallel sequence controlling the navigation along the predefined trajectory, while continuously taking images of the surface and checking for defects. If there are any signs of surface defects, a Deliberation sequence is triggered, since the robot needs to decide to either carry out further investigations or to continue its navigation. In the former case, an adapted form of communication with the teammate will accompany the unfolding plan, leading to the translation of her/his feedback into a sequence of additional tasks to be performed in place . This sequence will fail if at least one of the child conditions is not satisfied, i.e., if no further operation is required by the human at the present position; in this case, the robot will resume the navigation towards the next checkpoint. As soon as this is reached, the planned inspection operation can take place. The robot employs adequate operation templates selected from a predefined library based on the outcomes of the previous phases of the MRO HRI subtree, including human teammate’s feedback.We define the Practically, an inspection template collects the minimum set of requirements needed to organize, plan and keep track of the entire sequence of activities to implement the inspection operation . These will be followed by data processing , and a documentation activity addressing the reporting and transfer of the acquired data to the control base.Deliberation node and can be implemented exclusively after a dialogue with the teammate, i.e. execute the Ask TM behavior.The priority shift from a sequence of detailed inspections to an unplanned task is handled by the Example 5. While navigating towards the next inspection checkpoint, the robot detects the presence of a damage on the blade surface. It stops and establishes a communication channel with the human teammate, which confirms the need to perform a detailed inspection and diagnosis. Next to inspection, the diagnosis task is deployed across the following behaviors: BT1-Identify damage scenario (robot): leading edge erosion through laminate; BT2-Assess damage criticality (robot): critical damage, an immediate action is required to prevent turbine failure; BT3-Deliberation (robot): the confidence level with respect to the damage assessment is not high, therefore the robot ask human support; BT4-Validation: the teammate analyzes the robot’s report and recommends immediate repairing of the damage , to ensure safe turbine operation; at the end of this subtree, the robot sets the variable MROenabled to true (teammate and robot). This validation outcome puts on hold the current navigation towards the next target checkpoint until the damage situation is cleared by the MRO behavior of the robot.The diagnosis supports also the selection of the appropriate MRO scenario considering both preparatory and repairing operations .Example 6. In order to prepare the damaged surface for repair, the robot instantiates and executes sequentially three preparatory MRO behaviors: BT1-Sanding to remove the damaged layers (sander tool); BT2-Cleaning to remove any remaining dust from the sanding (brush tool); BT3- Degreasing to remove all surface contaminants (spray tool). After each preparatory step, a tool change is required .Example 7. Based on the diagnosis outcome and teammate’s feedback, the robot selects the surface coating behavior to carry out the repair of the leading edge of the blade. The following subtrees will be sequentially deployed: BT1-Sense environmental conditions on the blade surface (robot): wind, temperature and humidity; BT2-Ask teammate (robot and teammate): the robot provides a report on the environmental conditions and checks the status of the repairing material and tool, while the human uploads the correct process recipe (robot and teammate); BT3-Filler application (robot): apply the exact amount of filler material; BT4-Profiling (robot): create a smooth finish by following the leading-edge contour with a dedicated tool.in situ , well known for their high capital investment and long service life, rely on various postproduction treatments  to sustain their businesses. Most of the time, due to scale constraints, it is imperative to perform the postproduction operations in situ . The conSafety and MRO HRI behavior trees: ascertaining the current working situation involving the human, in particular to identify potential hazards; grasping the emotional state of the human ; reasoning and proposing alternative routes to alleviate the difficulty of tasks to be handled by the human, through shared human-robot plans.Three underlying behavioral dimensions could be further addressed when considering the functional capabilities instantiated with the support of the Theory Of Mind  the robot is able to receive mission goals and to automatically sequence skills to achieve them, and 2) the robot is able to update its initial plans in the presence of unknown events based on its contextual perception.as-is plan, i.e., the conventional approach of completing an MRO task, exclusively performed by the humans. Next, the identification of potential candidates relies upon the assessment of the task risk level imposed on three different categories in the perspective of a partial or complete transfer of the risk from the human to the robot. However, the planning of shared activities is not trivial, since the intrinsic task hazard reduction through robot takeover could lead to a complete procedural change of the working routine. For instance, supporting preparation and/or safety surveillance activities related to repair and maintenance processes, as well as the human resources required to support their realization, could become redundant  centered on the provision of adapted behavioral responses to improve the occupational health and human welfare.The concept and the initial formulation of the robotic behaviors are greatly simplified, as many of them are indeed challenging research problems in their own right. Any complete solution would need to handle a wider search space of behaviors as well as a larger collection of robotic platforms. The elaboration of a first framework for the MRO sector and the employment of the behavior tree formalism allows us to start with simpler initial implementations of the individual tasks and then gradually replace them with more complex models  and extend them to other platforms, without changing the structure of the initial tree, or the implementation of the prior tasks.


Intraosseous infusion (IO) remains an underutilized technique for obtaining vascular access in adults, despite its potentially life-saving benefits in trauma patients. In rural and remote areas, shortage of training equipment and human capacity  are the main contributors to the shortage of local training courses aiming at the development and maintenance of IO skills. Specifically, current training equipment options available for trainees include commercially available simulators, which are often expensive, or animal tissues, which lack human anatomical features that are necessary for optimal learning and pose logistical and ethical issues related to practice on live animals. Three-dimensional (3D) printing provides the means to create cost-effective, anatomically correct simulators for practicing IO where existing simulators may be difficult to access, especially in remote areas. This technical report aims to describe the development of maxSIMIO, a 3D-printed adult proximal tibia IO simulator, and present feedback on the design features from a clinical co-design team consisting of 18 end-point users. Overall, the majority of the feedback was positive and highlighted that the maxSIMIO simulator was helpful for learning and developing the IO technique. The majority of the clinical team responders also agreed that the simulator was more anatomically accurate compared to other simulators they have used in the past. Finally, the survey results indicated that on average, the simulator is acceptable as a training tool. Notable suggestions for improvement included increasing the stability of the individual parts of the model (such as tightening the skin and securing the bones), enhancing the anatomical accuracy of the experience (such as adding a fibula), making the bones harder, increasing the size of the patella, making it more modular (to minimize costs related to maintenance), and improving the anatomical positioning of the knee joint . In summary, the clinical team, located in rural and remote areas in Canada, found the 3D-printed simulator to be a functional tool for practicing the intraosseous technique. The outcome of this report supports the use of this cost-effective simulator for simulation-based medical education for remote and rural areas anywhere in the world. Simulation-based medical education (SBME) is a rapidly evolving discipline that allows healthcare providers in practice and training to learn and sustain clinical processes without endangering patients . Three-dThe IO procedure injects fluid directly into the bone marrow, where it is quickly absorbed by the body. When peripheral intravenous (IV) access is difficult or not possible, IO access provides a viable route to vascular circulation . It is aIn rural and remote areas, a shortage of training equipment  and human capacity  are the main contributors to the shortage of local training courses aiming at the development and maintenance of IO skills . SpecifiThe purpose of this technical report is to describe the development and initial end-point user feedback of maxSIMIO, a 3D-printed adult proximal tibia IO simulator that could offer a more accessible means. Using a modified context, input, process, and product structure, we aim to provide a recipe for other users to optimize the applicability of the developed simulator to various learning environments, educational contexts, inputs, processes, and expected outcomes .ContextmaxSIMIO was made with the intent to train medical students and advanced care paramedics who have limited experience with the IO technique in a remote setting. Three collaborative groups with complementary areas of expertise collaborated to design this simulator: technical designers and graduate students from maxSIMhealth laboratory, a research laboratory located at Ontario Tech University in Oshawa, Ontario, Canada (hereafter referred to as the development team); a group of practicing rural doctors who are members of the Society for Rural Physicians of Canada; and advanced care paramedics at the Durham Region Health Department, Region of Durham Paramedic Services, The Central East Prehospital Care Program, Lakeridge Health, and Durham College .th Annual Rural and Remote Medicine Course conference on April 22, 2022, hosted by the Society of Rural Physicians of Canada in Ottawa, Ontario, Canada. Co-design and initial feedback from the clinical team were gathered iteratively during multiple hands-on agile design sessions to improve the simulator. The feedback on the final design was provided by a group of remote and rural doctors during the 29Inputs and design processArchitecture of maxSIMIOThe development and clinical teams brainstormed design ideas for the simulator initially. The designs of the simulator were created by the development team using publicly available medical images or digital models licensed by Creative Commons  and finalized using subsequent feedback from the clinical team. The designs were saved as stereolithography (STL) files in SolidWorks  and then prepared for 3D printing using Ultimaker Cura  3D slicing software. The 3D-rendered files were then transferred to an Ultimaker S5 3D printer  using a secure digital (SD) card.There are two versions of the maxSIMIO simulator: the first version was developed before the workshop and used to gather feedback, and the second version was developed after receiving feedback from the workshop. The first model (straight leg) has the knee orientated in its fully extended position resulting in a straight leg. This version consists of four parts Figure ; A) the https://github.com/maxSIMhealth/maxSIMIOv1.0.The design for the straight leg model was created by manipulating surface scans of the femur, tibia, fibula, and patella bones obtained through a Creative Commons license . These files were adjusted to the correct orientation using the SolidWorks Assembly feature. Then, by using a series of plane cuts, the model was cut [into/down] to a more manageable size. All of the solids were then merged into one single solid using the combine feature. To obtain the replaceable cartridge, three plane cuts were made on the main model with the merge setting turned off, which resulted in two solids. The cartridge was then exported as an STL file. The remaining solid was then manipulated further by sketching and extruding thin cylinders at the ends to cap off the model. This remaining solid body of the bones was then also exported as an STL file. These STL files were then finalized in Autodesk Meshmixer , using its hollow feature to make the cartridge and bones hollow. In addition to these parts, a mold was required for the manufacturing of the skin. This mold was created in SolidWorks by sketching two rectangles and extruding them to create a simple box shape. All of the digital files developed for this project are publicly available on GitHub and can be found at the following link: To make the muscle layer of the model, a mold was created out of recycled cylindrical storage tins. This served as an affordable and environmentally friendly method to make the muscle since the containers were being reused for this purpose. The muscle was created by cutting containers in half longitudinally. Then, the 3D printed model of the bones was placed inside the containers, and the ends of the model were taped together with the mold using duct tape. Lastly, Dragon Skin™ 10 mixed with red Silc-Pig™ silicone pigment was poured into the mold, and once it cured after approximately 75 minutes, the mold was removed, and the muscle layer was complete.To make the skin layer, a piece of power mesh fabric was placed into the skin mold, improving the strength of the skin layer. Then, skin-coloured Silc-Pig™ silicone pigment was mixed into the Dragon Skin™ 10 NV silicone and poured into the skin mold. After curing for approximately 75 minutes, five vertical slits were cut into the ends of the skin. The replacement cartridge was installed on the model, and the skin was stretched around to enclose it. Lastly, straps made from Velcro were used to secure the skin and keep it in place, which provides an easy method to replace the skin and cartilage.In the second model of the maxSIMIO simulator, feedback was used to improve the first model, resulting in a larger bent-knee design. The new model consisted of four parts Figure , A) the This design was derived from the same models of the bone as the straight leg model; however, they were arranged so that the knee would be in a 90-degree position. This was done using the SolidWorks assembly feature. Next, a freehand sketch was created around the bones using the spline feature to create the shape of a leg. This sketch was then extruded around the bones using the revolve feature to create two solids, the bones and the leg. Next, the hollow feature was used to hollow out the leg solid, then followed by a series of plane cuts which resulted in splitting up both solids at the femur, knee joint, and lower tibia. A disk was then extruded at the location of the plane cuts to provide an interface for the three components to attach to. These components were then exported as STL files, sliced in Ultimaker Cura, then 3D-printed.To attach the skin to the model, skin-coloured Silc-Pig™ silicone pigment was mixed into the Dragon Skin™ 10 NV silicone and poured into the different molds that already contained the 3D-printed bones. Once the silicone had cured after approximately 75 minutes, the four separate parts were removed from their molds and attached. This method resulted in an anatomically correct human leg model.Materials for the IO StationDuring the final co-design feedback loop, the rural and remote doctors and trainees were able to attempt IO insertion at a training station. The equipment provided included: the maxSIMIO simulator, an Arrow® EZ-IO® Power Driver , and an EZ-IO® Needle Set Figure . https://github.com/maxSIMhealth/maxSIMIOv1.0), and, a live instructor as well as members of the development team were present to provide additional instructions and consultations about the technical aspects of the simulator , and improving the anatomical positioning of the knee joint . The educational value section of the assessment survey had the highest average rating (4.67/5), indicating that the clinical team believed that the intended trainees  will be able to gain skills through practice on the simulator. The lowest scoring question revolved around the realism of the anatomical feature of the maxSIMIO (3.88/5). The clinical team suggested stabilizing specific parts of the simulator, such as the bones inside and the entire simulator. Only eight of the 18 clinical team members had used an existing, commercially available IO simulator, and most agreed that this model was comparable except for the fact that the commercial simulators used in the past provided superior stability. Overall, the results show that the simulator allows for practice that is representative of the IO procedure to trainees with any level of experience but that it may require improvements to be a better training tool for more experienced learners. In comparison to existing simulators, ten tibial cartridge replacements are USD 180 from Laerdal®   or USD 5In conclusion, maxSIMIO demonstrated to be a cost-effective and useful tool for training medical professionals on the IO technique. The general impressions of medical experts who used our simulator were positive, and it was highlighted that the tool allowed them to learn the rare procedure, especially those new to the skill. The clinical team provided valuable feedback to improve the simulator, such as increasing the stability and realism of the structure. We believe our simulator, after implementation of these improvements, could provide a more accessible means for rural and remote trainees and practitioners to acquire and maintain the IO technique, both in scheduled practice sessions and training sessions at home.