智能机器人可变参数群体控制模型的多目标优化方法  

A multi-objective optimization method for intelligent swarm robotic control model with changeable parameters

作　　者：王原 马力[1] 王凌[2] 何永明 齐伟华 邢立宁[1] WANG Yuan;MA Li;WANG Ling;HE YongMing;QI WeiHua;XING LiNing(College of Systems Engineering,National University of Defense Technology,Changsha 410073,China;Department of Automation,Tsinghua University,Beijing 100084,China)

机构地区：[1]国防科技大学系统工程学院,长沙410073 [2]清华大学自动化系,北京100084

出　　处：《中国科学：技术科学》2020年第5期526-537,共12页Scientia Sinica(Technologica)

基　　金：国家自然科学基金(批准号:61773120,61873328);国家杰出青年科学基金(批准号:61525304)资助项目。

摘　　要：近年来,针对自组织群体智能机器人的研究一直是智能机器人研究的热点问题.在这些研究中,提升群体机器人控制模型的泛用性一直是一个热点问题.为了提升群体机器人控制模型的泛用性,提高群体机器人在不同类型的环境下的功能表现,使用可配置的控制模型,利用仿真的方法求解控制模型在不同环境下的参数配置,是一种较为常见的方法.本研究提出了一种基于规则系统模型的多目标群体智能机器人控制模型优化方法,并针对该方法测试了多种不同的多目标优化方法的表现.使用包含凸障碍、非凸障碍以及通道型障碍的多个不同的仿真场景验证了该模型的效能.仿真验证结果表明该方法在各种环境下均具有较好的表现.In recent years, research into self-organized swarm robotics has received much attention. Within this research, development of a general control model that functions under different conditions has been a hot topic. One significant approach is to use a control model with tunable parameters determined through simulation. In this article, we propose a rule-based swarm robotics control model with tunable parameters. To improve the performance, a modified non-dominated sorting genetic algorithm(NSGA II) is embedded into this control model. Multiple scenarios including convex obstacles, square obstacles, and tunnels are used to test the performances of the proposed control model. Comparison is made with three widely applied population-based multi-objective optimization algorithms. Experimental results show that our control model has good performance and is robust under different scenarios.

关 键 词：群体智能机器人 控制模型 多目标优化 进化算法 粒子群优化 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]