摘要
变电站巡检机器人的路径规划是一个复杂的组合优化问题。与经典的TSP问题不同,变电站巡检线路中各坐标之间并不具备完全的连通性,传统的优化方法难以解决此类问题。为此,文中提出一种改进遗传算法用于巡检路径规划,采用拓扑图对机器人工作环境进行建模,然后采用特殊的交叉算子、自适应变异算子和淘汰算子,对每一代被淘汰的个体进行逆转变异并将产生的新个体重新加入种群,随迭代次数调整变异概率,从而对连续的规划空间直接进行寻优。仿真结果表明,该算法在巡检机器人路径规划中与模拟退火算法、传统遗传算法和基于个体相似度改进的自适应遗传算法(ISAGA)相比,得到的路径平均长度分别缩短了4.9%、8.3%和3.1%,并且具有更好的收敛性和稳定性,在实际的巡检任务中能够起到更好的效果。
Path planning of patrol robot in substation is a complex combinatorial optimization problem.Unlike the classical TSP problem,there is no complete connectivity between the coordinates of inspection route in substation.Conventional optimization methods are difficult to solve such problems.Therefore,an improved genetic algorithm is proposed for the inspection route planning.The working environment of the robot is modeled by using topological graph.Then,the special crossover operator,adaptive mutation operator and elimination operator are used to reverse mutation of the eliminated individuals in each generation,and the new individuals are re-added to the population.The mutation probability is adjusted with the number of iterations,thus,the continuous planning space is directly optimized.The simulation results show that compared with the simulated annealing algorithm,the traditional genetic algorithm and the improved adaptive genetic algorithm based on individual similarity(ISAGA),the average path length of the proposed algorithm is shortened by 4.9%,8.3% and 3.1% respectively,and it has better convergence and stability,which can play a better role in the actual inspection task.
作者
柯清派
史训涛
袁智勇
雷金勇
刘迎澍
任超
Ke Qingpai;Shi Xuntao;Yuan Zhiyong;Lei Jinyong;Liu Yingshu;Ren Chao(Electric Power Research Institute,CSG,Guangzhou 510080,China;School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China)
出处
《电测与仪表》
北大核心
2023年第8期144-149,156,共7页
Electrical Measurement & Instrumentation
基金
中国南方电网有限责任公司科技项目(ZBKJXM2 0170086)
国家自然科学基金资助项目(61603270)。
关键词
变电站巡检机器人
路径规划
改进遗传算法
寻优
substation patrol robot
path planning
improved genetic algorithm
optimization
