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

IF 5.8 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Integrated Computer-Aided Engineering Pub Date : 2022-11-24 DOI:10.3233/ica-220695
Enol García González, J. Villar, Qing Tan, J. Sedano, Camelia Chira
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引用次数: 2

Abstract

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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基于A*和协同进化算法的高效多机器人路径规划解
多机器人路径规划已经从研究发展到仓库等领域的实际应用;这方面的知识反映在近年来国际期刊上发表的大量相关研究中。现有的研究主要集中在有效路径的生成、依赖于局部感知系统的碰撞检测以及基于局部搜索方法的解决方案。这种方法意味着机器人具有良好的感觉系统和计算能力,可以在飞行中做出决定。在一些受控环境中,如虚拟实验室或工业工厂,这些限制超过了实际需求,因为更简单的机器人就足够了。因此,多机器人路径规划必须事先解决碰撞问题。本研究的重点是在这种受控环境下生成有效的无碰撞多机器人路径规划解决方案,扩展了我们之前的研究。该方案将A*算法的优化能力与协同进化算法的搜索能力相结合。结果是一组无碰撞的路线,要么来自a *,要么来自共同进化过程;该集合是实时生成的,使得其在边缘计算设备上的实现是可行的。虽然需要进一步的研究来减少计算时间,但在本研究中进行的计算实验证实了所提出的方法在解决众所周知的替代方法(如M*或WHCA)无法找到合适解的复杂情况时的良好性能。
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来源期刊
Integrated Computer-Aided Engineering
Integrated Computer-Aided Engineering 工程技术-工程:综合
CiteScore
9.90
自引率
21.50%
发文量
21
审稿时长
>12 weeks
期刊介绍: Integrated Computer-Aided Engineering (ICAE) was founded in 1993. "Based on the premise that interdisciplinary thinking and synergistic collaboration of disciplines can solve complex problems, open new frontiers, and lead to true innovations and breakthroughs, the cornerstone of industrial competitiveness and advancement of the society" as noted in the inaugural issue of the journal. The focus of ICAE is the integration of leading edge and emerging computer and information technologies for innovative solution of engineering problems. The journal fosters interdisciplinary research and presents a unique forum for innovative computer-aided engineering. It also publishes novel industrial applications of CAE, thus helping to bring new computational paradigms from research labs and classrooms to reality. Areas covered by the journal include (but are not limited to) artificial intelligence, advanced signal processing, biologically inspired computing, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, intelligent and adaptive systems, internet-based technologies, knowledge discovery and engineering, machine learning, mechatronics, mobile computing, multimedia technologies, networking, neural network computing, object-oriented systems, optimization and search, parallel processing, robotics virtual reality, and visualization techniques.
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