基于鸽群优化改进动态窗的多无人车协同编队避障控制  

Multi-vehicle formation and obstacle avoidance control based onpigeon-inspired optimization and dynamic window approach

作　　者：李兆博 孙双蕾 LI Zhaobo;SUN Shuanglei(School of Automation Science and Electrical Engineering,Beihang University,Beijing 100083,China;Beijing Special Machinery Research Institute,Beijing 100143,China)

机构地区：[1]北京航空航天大学自动化科学与电气工程学院,北京100083 [2]北京特种机械研究所,北京100143

出　　处：《工程科学学报》2024年第7期1279-1285,共7页Chinese Journal of Engineering

基　　金：国家自然科学基金资助项目(U19B2033)。

摘　　要：在复杂未知的战场环境中,无人车集群较单台无人车可承担更为复杂的任务,无人车集群协同编队避障行驶是群体智能领域研究热点之一.针对未知环境下无人车集群在规避障碍物时容易出现动态位置与预期队形偏差较大问题,本文提出了一种基于改进动态窗的无人车编队协同避障控制方法,在基本动态窗路径评价函数的方位角评价因子、障碍物评价因子、速度评价因子基础上,增加了无人车编队的方向协同因子和队形保持因子.同时,基于变权重鸽群优化算法对改进动态窗的路径评价函数各系数进行优化.当无人车集群感知到障碍物时,通过改进动态窗算法进行相对位置及速度的自适应协同调整,更好地保证无人车编队避障行驶过程中队形位置的精确性.最后,以3台无人车构成三角形编队避障行驶为例进行仿真验证.仿真结果表明基于改进动态窗和变权重鸽群优化算法的无人车编队在规避障碍物时,队形位置偏差相对较小.可见,本文提出的改进算法能够使得无人车集群在规避障碍物过程中提高动态编队的稳定性和精确性.In a complex and unknown battlefield environment,unmanned vehicle clusters can perform more complex tasks than a single unmanned ground vehicle(UGV).The formation and obstacle avoidance of unmanned vehicle clusters is one of the research hotspots in the field of swarm intelligence.To reduce the deviation between the actual formation position and expected formation when unmanned vehicle clusters avoid obstacles in an unknown environment,this paper proposes a cooperative formation and obstacle avoidance control method for unmanned vehicle clusters based on an improved dynamic window approach(DWA).In addition to the azimuth evaluation,obstacle evaluation,and speed evaluation factors of the path evaluation function in DWA,the direction coordination and formation maintenance factors are added.We established a mathematical model of unmanned vehicle cooperative formation and calculated in real time the expected position,direction,and speed of each following vehicle according to the expected formation.The sum of the deviation between the real driving and expected directions of each following vehicle was taken as the direction cooperation factor,and the sum of the absolute deviation between the real and expected positions of each following vehicle was taken as the formation-keeping factor.When unmanned vehicles approach unknown obstacles,the adaptive collaborative adjustment of the relative position and speed is performed based on the improved DWA,which can improve the precision of the formation positions during the obstacle avoidance process.In this paper,a simulation is conducted using a scenario of three unmanned vehicles forming a triangular formation to avoid obstacles and three obstacles.The simulation results show that the control adjustment based on the improved dynamic window and variable weight optimization algorithm is more timely than the traditional formation control based on the artificial potential field.Additionally,the formation position deviation is relatively small when avoiding obstacles.The average de

关 键 词：无人车 鸽群优化 动态窗口法 协同编队 避障控制 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]