Details

Autor(en) / Beteiligte

• Nascimento, Luís B. P.
• Barrios-Aranibar, Dennis
• Santos, Vitor G.
• Pereira, Diego S.
• Ribeiro, William C.
• Alsina, Pablo J.

Titel

Safe Path Planning Algorithms for Mobile Robots Based on Probabilistic Foam

Ist Teil von

• Sensors (Basel, Switzerland), 2021-06, Vol.21 (12), p.4156

Ort / Verlag

Basel: MDPI AG

Erscheinungsjahr

2021

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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.