Browsing by Author "Alsina, P.J."

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    Safe path planning algorithms for mobile robots based on probabilistic foam
    (MDPI AG, 2021) Nascimento, L.B.P.; Barrios-Aranibar, D.; Santos, V.G.; Ribeiro, W.C.; Alsina, P.J.
    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 cov-ering 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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.