基于自适应金豺狼优化算法的巡检机器人路径规划

Inspection Robot Path Planning Based on Adaptive Golden Jackal Optimization

为提升核环境巡检机器人移动路径规划效果,提出了基于自适应金豺狼优化算法(AGJO)的核环境巡检机器人路径规划新方法,并进行了实例分析。首先,介绍了GJO算法基本原理和改进策略,给出了AGJO算法流程;其次,通过3个基准测试函数进行了AGJO与GJO性能对比分析;最后,构建了巡检机器人工作的两种仿真场景和一种真实场景,利用AGJO进行路径规划。结果表明,AGJO算法得到的移动路径最短,计算效率最高,具有一定的优势。

In order to improve the path planning effect of inspection robot in nuclear environment,a new path planning method based on adaptive golden jackal optimization(AGJO)was proposed,and an example was analyzed.Firstly,the basic principle and improvement strategy of GJO algorithm are introduced,and the flow of AGJO algorithm was given.Secondly,the performance of AGJO and GJO was compared by three benchmark test functions.Finally,two simulation scenario and a real scenario of the inspection robot are constructed,and AGJO was used for path planning.The results show that the AGJO algorithm has the advantages of the shortest moving path and the highest computing efficiency.