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 configurati...

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Published inSensors (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
LanguageEnglish
Published Basel MDPI AG 17.06.2021
MDPI
Subjects
A algorithm
Algorithms
Bubbles
Methods
mobile robot
path planning
Planning
probabilistic foam
Propagation
Robots
safety
Online AccessGet full text
ISSN1424-8220
1424-8220
DOI10.3390/s21124156

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Abstract 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.
AbstractList 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.
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.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.
Author Nascimento, Luís B. P.
Ribeiro, William C.
Barrios-Aranibar, Dennis
Pereira, Diego S.
Alsina, Pablo J.
Santos, Vitor G.
AuthorAffiliation 1 Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil; diego.pereira@ifrn.edu.br (D.S.P.); willcr@ieee.org (W.C.R.)
2 Federal Institute of Rio Grande do Norte, Parnamirim 59143-455, Brazil; vitor.gaboardi@ifrn.edu.br
3 Electrical and Electronics Engineering Department, Universidad Católica San Pablo, Arequipa 04001, Peru; dbarrios@ucsp.edu.pe
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– name: 3 Electrical and Electronics Engineering Department, Universidad Católica San Pablo, Arequipa 04001, Peru; dbarrios@ucsp.edu.pe
– name: 2 Federal Institute of Rio Grande do Norte, Parnamirim 59143-455, Brazil; vitor.gaboardi@ifrn.edu.br
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Snippet 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...
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StartPage 4156
SubjectTerms A algorithm
Algorithms
Bubbles
Methods
mobile robot
path planning
Planning
probabilistic foam
Propagation
Robots
safety
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Title Safe Path Planning Algorithms for Mobile Robots Based on Probabilistic Foam
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