Experimental study of path planning problem using EMCOA for a holonomic mobile robot

In this paper, a comparative study of the path planning problem using evolutionary algorithms, in comparison with classical methods such as A∗ algorithm, is presented for holonomic mobile robot. The configured navigation system, which consists integration sensors sources, map formatting, global and local planners, base controller, aims to enable robot follow shortest smooth delicately. Grid-based mapping used scoring paths efficiently, allowing determination collision-free trajectories from initial target position. This work considers mutated cuckoo optimization algorithm (MCOA) genetic (GA), planner find safe among others. A non-uniform motion coefficient introduced MCOA order increase performance algorithm. series experiments are accomplished analyzed confirm implemented on results show capacity framework respect under various obstacle layouts.