Goal-biased probabilistic foam method for robot path planning

Nascimento, Luís; Pereira, Diego S.; Alsina, Pablo Javier; Silva, Maurício R.; Fernandes, Daniel H. S.; Roza, Valber Cesar Cavalcanti; Sanca, Armando S.

doi:10.1109/icarsc.2018.8374183

Cited by 6 publications

(7 citation statements)

References 9 publications

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“…In previous studies [ 29 , 31 , 34 ], the constant K was determined by empirical analysis. In this paper, we propose a new approach to determine the value of K according to the dimension of the configuration space.…”

Section: The Probabilistic Foam Methodsmentioning

confidence: 99%

“…Goal-Biased Probabilistic Foam (GBPF) is a variant of the original PFM proposed in [ 34 ]. In this algorithm, foam propagation is based on the strategy of expanding the search tree of the RRT-GoalBias algorithm [ 14 ], a variant of the classic path planner Rapidly-Exploring Random Tree [ 13 ].…”

Section: Variants Of Probabilistic Foammentioning

confidence: 99%

“…First, an approach to set the value of the constant K was formalized, which facilitates understanding of how the method works. Next, we presented three variants of the original PFM: Goal-biased Probabilistic Foam (GBPF), first proposed in [ 34 ]; Radius-Biased Probabilistic Foam (RBPF); and Heuristic-Guided Probabilistic Foam (HPF). The last two methods were proposed in this paper.…”

Section: Conclusion and Future Workmentioning

confidence: 99%

“…The main contribution of [ 31 ] was the modeling of new bubbles inspired by the concept of Bubble of free space , proposed in [ 33 ], which enabled PFM to solve robotic problems with many degrees of freedom. In [ 34 ], we presented a variant of PFM called Goal-Biased Probabilistic Foam (GBPF). In this variant, we proposed a new foam propagation approach inspired by goal-biased RRT tree growing, which returns paths with the convenient trade-off between finding short paths through narrow passages in the map and keeping the clearance from obstacles.…”

Section: Introductionmentioning

confidence: 99%

“…The remainder of the paper is organized as follows: Section 2 describes the original PFM, and a new approach to determine one of its parameters is proposed. In Section 3 , we present the GBPF algorithm [ 34 ] and propose two new variants of PFM—RBPF and HPF. Section 5 presents simulation results and discussions.…”

Section: Introductionmentioning

confidence: 99%

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Safe Path Planning Algorithms for Mobile Robots Based on Probabilistic Foam

Nascimento

Barrios-Aranibar

Santos

et al. 2021

Sensors

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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.

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Section: The Probabilistic Foam Methodsmentioning

confidence: 99%

Section: Variants Of Probabilistic Foammentioning

confidence: 99%

Section: Conclusion and Future Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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