The involvement of Meta-heuristic algorithms in robot motion planning has attracted the attention of researchers in the robotics community due to the simplicity of the approaches and their effectiveness in the coordination of the agents. This study explores the implementation of many meta-heuristic algorithms, e.g. Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) in multiple motion planning scenarios. The study provides comparison between multiple meta-heuristic approaches against a set of well-known conventional motion planning and navigation techniques such as Dijkstra’s Algorithm (DA), Probabilistic Road Map (PRM), Rapidly Random Tree (RRT) and Potential Field (PF). Two experimental environments with difficult to manipulate layouts are used to examine the feasibility of the methods listed. several performance measures such as total travel time, number of collisions, travel distances, energy consumption and displacement errors are considered for assessing feasibility of the motion planning algorithms considered in the study. The results show the competitiveness of meta-heuristic approaches against conventional methods. Dijkstra ’s Algorithm (DA) is considered a benchmark solution and Constricted Particle Swarm Optimization (CPSO) is found performing better than other meta-heuristic approaches in unknown environments.

由于方法的简单性及其在代理协调中的有效性，元启发式算法在机器人运动计划中的参与引起了机器人界研究人员的关注。这项研究探索了多种元启发式算法的实现，例如遗传算法（GA），差分进化（DE），粒子群优化（PSO）和布谷鸟搜索算法（CSA）在多种运动计划方案中的实现。这项研究提供了多种元启发式方法与一组著名的常规运动规划和导航技术之间的比较，这些方法包括迪杰斯特拉算法（DA），概率路线图（PRM），快速随机树（RRT）和势场（PF） 。具有难以操作的布局的两个实验环境用于检查列出的方法的可行性。为了评估研究中考虑的运动计划算法的可行性，考虑了多种性能指标，例如总行驶时间，碰撞次数，行驶距离，能耗和位移误差。结果表明，元启发式方法相对于传统方法具有竞争力。Dijkstra的算法（DA）被认为是基准解决方案，并且在未知环境中，约束粒子群优化（CPSO）的性能优于其他元启发式方法。考虑能耗和位移误差，以评估研究中考虑的运动计划算法的可行性。结果表明，元启发式方法相对于传统方法具有竞争力。Dijkstra的算法（DA）被认为是基准解决方案，并且在未知环境中，约束粒子群优化（CPSO）的性能优于其他元启发式方法。考虑能耗和位移误差，以评估研究中考虑的运动计划算法的可行性。结果表明，元启发式方法相对于传统方法具有竞争力。Dijkstra的算法（DA）被认为是基准解决方案，并且在未知环境中，约束粒子群优化（CPSO）的性能优于其他元启发式方法。