A*-Based Co-Evolutionary Approach for Multi-Robot Path Planning with Collision Avoidance

Kiadi, Morteza; García, Eduardo Abad; Villar, José R.; Tan, Qing

doi:10.1080/01969722.2022.2030009

Cited by 13 publications

(14 citation statements)

References 15 publications

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“…The comparison replicates the two scenarios proposed in [45]: the first one with a single room and fixed obstacles; the second one with 4 connected rooms. In both cases, the number of robots is varied to evaluate the collision-avoidance capabilities and the time consumption in finding a solution.…”

Section: Experimentation Setupmentioning

confidence: 82%

“…For the third scenario, which is the most reliable representation of a warehouse environment, our method has the shortest execution time. The proposal in [45] was directly dismissed due to its poor performance in the previous scenarios. M* only found solutions for the smallest number of robots while WHCA proposed solutions for some number of robots, but in those cases the computation time was higher than our proposal.…”

Section: Resultsmentioning

confidence: 99%

“…As mentioned before, this research represents a step forward with respect to our previous study in [45]. Several enhancements have been included, which have introduced a great improvement in the performance -as will be shown in the experimentation results-: With all these changes, the new solution shows as more competitive than before and finding solutions where other proposals fail as it will be shown in the next section.…”

Section: Differences With Previous Researchmentioning

confidence: 89%

“…In this research, a metaheuristic based solution for the MPP problem is proposed, enhancing our previous research results reported in [45]. The solution construction is split in three stages: firstly, an A* initial optimal route finding process is performed, followed by an A*-based alternatives search, and finalizing with a co-evolutionary optimization process that examines and modifies the available pool of routes among all the conflicting robots until a feasible solution is found.…”

Section: Introductionmentioning

confidence: 94%

“…The goal of the experiments is to establish whether the enhancements show better performance or not. To do so, we compare our solution with the original one in [45] -referred to as Kiadi2022. Moreover, we also compare our proposal with some well-known algorithms in the MPP problem domain: M* [19,20] and WHCA [40].…”

Section: Experimentation Setupmentioning

confidence: 99%

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An efficient multi-robot path planning solution using A* and coevolutionary algorithms

García

Villar

Tan

et al. 2022

ICA

Self Cite

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Multi-robot path planning has evolved from research to real applications in warehouses and other domains; the knowledge on this topic is reflected in the large amount of related research published in recent years on international journals. The main focus of existing research relates to the generation of efficient routes, relying the collision detection to the local sensory system and creating a solution based on local search methods. This approach implies the robots having a good sensory system and also the computation capabilities to take decisions on the fly. In some controlled environments, such as virtual labs or industrial plants, these restrictions overtake the actual needs as simpler robots are sufficient. Therefore, the multi-robot path planning must solve the collisions beforehand. This study focuses on the generation of efficient collision-free multi-robot path planning solutions for such controlled environments, extending our previous research. The proposal combines the optimization capabilities of the A* algorithm with the search capabilities of co-evolutionary algorithms. The outcome is a set of routes, either from A* or from the co-evolutionary process, that are collision-free; this set is generated in real-time and makes its implementation on edge-computing devices feasible. Although further research is needed to reduce the computational time, the computational experiments performed in this study confirm a good performance of the proposed approach in solving complex cases where well-known alternatives, such as M* or WHCA, fail in finding suitable solutions.

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Section: Experimentation Setupmentioning

confidence: 82%

Section: Resultsmentioning

confidence: 99%

Section: Differences With Previous Researchmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 94%

Section: Experimentation Setupmentioning

confidence: 99%

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An efficient multi-robot path planning solution using A* and coevolutionary algorithms

García

Villar

Tan

et al. 2022

ICA

Self Cite

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Path Planning Approaches in Multi‐robot System: A Review

Banik,

Mahmud

2024

Engineering Reports

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The essential factor in developing multi‐robot systems is the generation of an optimal path for task completion by multiple robots. To ensure effective path planning, this paper studies the recent publications and provides a detailed review of the path planning approaches to avoid collisions in uncertain environments. In this article, path‐planning approaches for multiple robots are categorized primarily into classical, heuristic, and artificial intelligence‐based methods. Among the heuristic approaches, bio‐inspired approaches are mostly employed to optimize the classical approaches to enhance their adaptability. The articles are analyzed based on static and dynamic scenarios, real‐time experiments, and simulations involving hybrid solutions. The increasing focus on using hybrid approaches in dynamic environments is found mostly in the papers employing heuristic and AI‐based approaches. In real‐time applications, AI‐based approaches are highly implemented in comparison to heuristic and classical approaches. Moreover, the findings from this review, highlighting the strengths and drawbacks of each algorithm, can help researchers select the appropriate approach to overcome the limitations in designing efficient multi‐robot systems.

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Multi-objective Optimization for Multi-Robot Path Planning on Warehouse Environments

García González,

R. Villar,

Chira

et al. 2023

Lecture Notes in Networks and Systems

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A*-Based Co-Evolutionary Approach for Multi-Robot Path Planning with Collision Avoidance

Cited by 13 publications

References 15 publications

An efficient multi-robot path planning solution using A* and coevolutionary algorithms

An efficient multi-robot path planning solution using A* and coevolutionary algorithms

Path Planning Approaches in Multi‐robot System: A Review

Multi-objective Optimization for Multi-Robot Path Planning on Warehouse Environments

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