A Multi-Robot Path Planning Approach Based on Probabilistic Foam

Nascimento, Luís; Morais, Daniel Silva de; Barrios-Aranibar, Dennis; Santos, Vitor Gaboardi dos; Pereira, Diego S.; Alsina, Pablo Javier; Medeiros, Adelardo Adelino Dantas de

doi:10.1109/lars-sbr-wre48964.2019.00064

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…To evaluate the performance of the proposal, we use two metrics taken from the work in [ 64 ]. The first metric is based on the ratio of lengths of two paths (

).…”

Section: Social Navigation System: Implementation and Resultsmentioning

confidence: 99%

An Approach of Social Navigation Based on Proxemics for Crowded Environments of Humans and Robots

et al. 2021

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Nowadays, mobile robots are playing an important role in different areas of science, industry, academia and even in everyday life. In this sense, their abilities and behaviours become increasingly complex. In particular, in indoor environments, such as hospitals, schools, banks and museums, where the robot coincides with people and other robots, its movement and navigation must be programmed and adapted to robot–robot and human–robot interactions. However, existing approaches are focused either on multi-robot navigation (robot–robot interaction) or social navigation with human presence (human–robot interaction), neglecting the integration of both approaches. Proxemic interaction is recently being used in this domain of research, to improve Human–Robot Interaction (HRI). In this context, we propose an autonomous navigation approach for mobile robots in indoor environments, based on the principles of proxemic theory, integrated with classical navigation algorithms, such as ORCA, Social Momentum, and A*. With this novel approach, the mobile robot adapts its behaviour, by analysing the proximity of people to each other, with respect to it, and with respect to other robots to decide and plan its respective navigation, while showing acceptable social behaviours in presence of humans. We describe our proposed approach and show how proxemics and the classical navigation algorithms are combined to provide an effective navigation, while respecting social human distances. To show the suitability of our approach, we simulate several situations of coexistence of robots and humans, demonstrating an effective social navigation.

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“…To evaluate the performance of the proposal, we use two metrics taken from the work in [ 64 ]. The first metric is based on the ratio of lengths of two paths (

).…”

Section: Social Navigation System: Implementation and Resultsmentioning

confidence: 99%

An Approach of Social Navigation Based on Proxemics for Crowded Environments of Humans and Robots

et al. 2021

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“…Path planning is one of the most researched problems in robotics [1][2][3]. Any path planning algorithm's primary goal is to provide a collision-free path from a start state to an end state within the robot's configuration space [4,5].…”

Section: Introductionmentioning

confidence: 99%

Efficient Sampling-based for Mobile Robot Path Planning in a Dynamic Environment Based on the Rapidly-exploring Random Tree and a Rule-template Sets

Pohan

Utama

2023

IJE

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This study presents an efficient path planning method for mobile robots in a dynamic environment. The method is based on the rapidly-exploring random tree (RRT) algorithm. The two primary processes in mobile robot path planning in a dynamic environment are initial path planning and path re-planning. In order to generate a feasible initial path with fast convergence speed, we used a hybridization of rapidlyexploring random tree star and ant colony systems (RRT-ACS). When an obstacle obstructs the initial path, the path re-planner must be executed. In addition to the RRT-ACS algorithm, we proposed using a rule-template set based on the mobile robot in dynamic environment scenes during the path re-planner process. This novel algorithm is called RRT-ACS with Rule-Template Sets (RRT-ACS+RT). We conducted many benchmark simulations to validate the proposed method in a real dynamic environment. The performance of the proposed method is compared to the state-of-the-art path planning algorithms: RRT*FND and MOD-RRT*. Numerous experimental results demonstrate that the proposed method outperforms other comparison algorithms. The results show that the proposed method is suitable for the use on robots that need to navigate in a dynamic environment, such as self-driving cars.

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A Multi-Robot Path Planning Approach Based on Probabilistic Foam

Cited by 2 publications

References 16 publications

An Approach of Social Navigation Based on Proxemics for Crowded Environments of Humans and Robots

An Approach of Social Navigation Based on Proxemics for Crowded Environments of Humans and Robots

Efficient Sampling-based for Mobile Robot Path Planning in a Dynamic Environment Based on the Rapidly-exploring Random Tree and a Rule-template Sets

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