Safe Path Planning Algorithms for Mobile Robots Based on Probabilistic Foam.
The planning of safe paths is an important issue for autonomous robot systems. The Probabilistic Foam method (PFM) is a planner that guarantees safe paths bounded by a sequence of structures called bubbles that provides safe regions. This method performs the planning by covering the free configuration space with bubbles, an approach analogous to a breadth-first search. To improve the propagation process and keep the safety, we present three algorithms based on Probabilistic Foam: Goal-biased Probabilistic Foam (GBPF), Radius-biased Probabilistic Foam (RBPF), and Heuristic-guided Probabilistic Foam (HPF); the last two are proposed in this work. The variant GBPF is fast, HPF finds short paths, and RBPF finds high-clearance paths. Some simulations were performed using four different maps to analyze the behavior and performance of the methods. Besides, the safety was analyzed considering the new propagation strategies.
['Nascimento|Luís B P|LBP|0000-0002-1699-4535', 'Barrios-Aranibar|Dennis|D|0000-0001-7482-3390', 'Santos|Vitor G|VG|0000-0002-4530-9568', 'Pereira|Diego S|DS|0000-0001-6658-1854', 'Ribeiro|William C|WC|0000-0002-9753-4038', 'Alsina|Pablo J|PJ|0000-0002-2882-5237']
D000465:Algorithms D003198:Computer Simulation D012371:Robotics
path planning mobile robot probabilistic foam bubbles safety A* algorithm