摘要
针对未知环境下多无人车协同自主探索分配任务不合理、执行效率较低的问题,提出一种融合波前算法的分布式多无人车协同探索方法。首先,设计基于市场机制任务分配方法的先验判决函数,结合波前算法对边界点进行相似度最大化预处理,使边界导引点脱离局部最优,同时引入min-max评估函数对边界点进行评估;其次,通过波前算法对Dijkstra路径规划算法进行改进,使无人车移动轨迹更精确,减少路径拐点。最后,在多种仿真环境下将所提方法和文献[12]方法进行了实验对比,在环境全覆盖时,所提方法探索时长平均减少了35.69%;实验结果表明,所提方法可有效提升探索覆盖率,减少重复路径、缩短探索时间,提高了多无人车协同探索的效率。
In order to solve the problems of unreasonable task assignment and low efficiency of multi-unmanned vehicle collaborative autonomous exploration in unknown environment,a distributed multi-unmanned vehicle collaborative exploration method based on wave-front algorithm is proposed.Firstly,a priori decision function based on the task assignment method of market mechanism is designed,and the boundary points are preprocessed by maximizing similarity with wavefront algorithm,so that the boundary guiding points are separated from the local optimization,and the min-max evaluation function is introduced to evaluate the boundary points.Secondly,the Dijkstra path planning algorithm is improved through the wave-front algorithm,which makes the moving trajectory of the unmanned vehicle more accurate and reduces the path inflection points.Finally,the method in this paper is compared with the method in literature[12]in various simulation environments,and the average exploration time of this method is reduced by 35.69%when the environment is fully covered.The experimental results show that the method can effectively improve the exploration coverage,reduce repeated paths and shorten exploration time,and improve the efficiency of multi-unmanned vehicle collaborative exploration.
作者
周思达
李丁奎
唐嘉宁
刘雨晴
李成阳
ZHOU Sida;LI Dingkui;TANG Jianing;LIU Yuqing;LI Chengyang(School of Electrical Information Engineering,Yunnan Minzu University,Kunming 650000,China)
出处
《电光与控制》
CSCD
北大核心
2023年第5期73-78,共6页
Electronics Optics & Control
基金
国家自然科学基金(61963038,62063035)。
关键词
波前算法
协同任务分配
边界导引点
路径规划
wave-front algorithm
cooperative task allocation
boundary guidance point
path planning
