期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 52, 期 8, 页码 7362-7376出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2020.3042511
关键词
Task analysis; Robots; Robot kinematics; Dynamic scheduling; Routing; Optimization; Heuristic algorithms; Ant colony optimization (ACO); multipoint dynamic aggregation (MPDA); multirobot system; task allocation
类别
资金
- National Natural Science Foundation of China [61673058, 62088101]
- National Outstanding Youth Talents Support Program [61822304]
- National Key Research and Development Program of China [2018YFB1308000]
- Consulting Research Project of the Chinese Academy of Engineering [2019XZ-7]
- Projects of Major International (Regional) Joint Research Program of NSFC [61720106011]
- Beijing Advanced Innovation Center for Intelligent Robots and Systems
- Peng Cheng Laboratory
The problem of multipoint dynamic aggregation involves designing an optimal plan for multiple robots to collaboratively execute tasks while considering the changing task demands and abilities of the robots. To effectively address this challenge, a new metaheuristic algorithm called adaptive coordination ant colony optimization (ACO) is proposed, which significantly outperforms existing methods in terms of effectiveness and efficiency.
Multipoint dynamic aggregation is a meaningful optimization problem due to its important real-world applications, such as post-disaster relief, medical resource scheduling, and bushfire elimination. The problem aims to design the optimal plan for a set of robots to execute geographically distributed tasks. Unlike the majority of scheduling and routing problems, the tasks in this problem can be executed by multiple robots collaboratively. Meanwhile, the demand of each task changes over time at an incremental rate and is affected by the abilities of the robots executing it. This poses extra challenges to the problem, as it has to consider complex coupled relationships among robots and tasks. To effectively solve the problem, this article develops a new metaheuristic algorithm, called adaptive coordination ant colony optimization (ACO). We develop a novel coordinated solution construction process using multiple ants and pheromone matrices (each robot/ant forages a path according to its own pheromone matrix) to effectively handle the collaborations between robots. We also propose adaptive heuristic information based on domain knowledge to promote efficiency, a pheromone-based repair mechanism to tackle the tight constraints of the problem, and an elaborate local search to enhance the exploitation ability of the algorithm. The experimental results show that the proposed adaptive coordination ACO significantly outperforms the state-of-the-art methods in terms of both effectiveness and efficiency.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据