Safe Path Planning Algorithms for Mobile Robots Based on Probabilistic Foam

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 12; p. 4156
• Main Authors: Nascimento, Luís B. P., Barrios-Aranibar, Dennis, Santos, Vitor G., Pereira, Diego S., Ribeiro, William C., Alsina, Pablo J.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 17.06.2021; MDPI
• Subjects: A algorithm; Algorithms; Bubbles; Methods; mobile robot; path planning; Planning; probabilistic foam; Propagation; Robots; safety
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21124156

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.