基于多种群协同进化的多逃逸者围捕任务分配  

Task Allocation of Multi-escapee Roundup Based on Multi-populationCoevolution

作　　者：高子璇 张国富[1,2,3,4] 苏兆品 李磊[1] GAO Zi-xuan;ZHANG Guo-fu;SU Zhao-pin;LI Lei(School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230601,China;Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology),Ministry of Education,Hefei 230601,China;Intelligent Interconnected Systems Laboratory of Anhui Province(Hefei University of Technology),Hefei 230009,China;Anhui Province Key Laboratory of Industry Safety and Emergency Technology(Hefei University of Technology),Hefei 230601,China)

机构地区：[1]合肥工业大学计算机与信息学院,安徽合肥230601 [2]大数据知识工程教育部重点实验室(合肥工业大学),安徽合肥230601 [3]智能互联系统安徽省实验室(合肥工业大学),安徽合肥230009 [4]工业安全应急技术安徽省重点实验室(合肥工业大学),安徽合肥230601

出　　处：《计算机技术与发展》2023年第12期185-192,共8页Computer Technology and Development

基　　金：安徽省重点研究与开发计划(202004d07020011,202104d07020001);中央高校基本科研业务费专项资金项目(PA2020GDKC0015,PA2021GDSK0073,PA2021GDSK0074)。

摘　　要：群机器人逃逸围捕一直是人工智能和机器人领域的研究热点之一。在面向多逃逸者时,如何为每个逃逸者高效地分配合适的机器人以完成协同围捕是一个难点问题。已有研究大都采用距离优先分配的策略,为每个逃逸者选择离它最近的一组机器人进行围捕,在逃逸者数量较多的情况下,难以实现围捕任务的均衡分配,降低了系统围捕的效率。为此,提出了一种基于多种群协同进化的多逃逸者围捕任务分配算法。首先,构建了一种全方向的群机器人逃逸围捕任务分配数学模型;然后,基于遗传算法和多种群协同进化提出了一种多逃逸者围捕任务分配算法,设计了相应的编码方式、交叉和变异策略;最后,在开发的群机器人逃逸围捕仿真平台上测试了算法的有效性。对比实验结果表明,所提算法在完成围捕任务所耗费的步数上最多降低了20%,围捕效率最大提高了25%。Swarm robot escape roundup has been one of the research hotspots in the field of artificial intelligence and robotics.When facing multiple escapees,it is a difficult problem to efficiently assign the appropriate robots to each escapee to complete collaborative roundup.Most of the researches have adopted the distance-first allocation strategy to select the nearest group of robots for each escapee,which makes it difficult to achieve a balanced distribution of the fencing task when the number of escapee is large and reduces the efficiency of system fencing.To this end,a multi-escapee roundup task allocation algorithm based on the co-evolution of multiple swarms is proposed.Firstly,an all-directional swarm robot escape roundup task assignment mathematical model is constructed,and then a multi-escapee roundup task assignment algorithm is proposed based on genetic algorithm and multiple swarm co-evolution,and the corresponding coding methods,crossover and variation strategies are designed.Finally,the effectiveness of the proposed algorithm is tested on the developed swarm robot escape roundup simulation platform.Comparative experimental results show that the proposed algorithm reduces the number of steps consumed in completing the roundup task by up to 20%and improves the roundup efficiency by up to 25%.

关 键 词：群机器人逃逸围捕 多逃逸者任务分配 遗传算法 多种群协同进化 静态障碍物避障 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]