Quantitative Performance Review of Wheeled Mobile Robot Path Planning Algorithms

Path planning evaluates and identifies an obstacle free path for a wheeled mobile robot (WMR) to traverse within its workspace. It emphasizes metric like, start goal coordinate, static or dynamic workspace, obstacles, computational time local minimum problem. play significant role toward WMR effective it workspace like industrial, military, hospital, school office. In this is optimal method increase the productivity of achieve specific task. Hence, in paper, we present review algorithms (classical algorithms, heuristics intelligent machine learning algorithm) using statistical method. Regarding our objective, use evaluate success these base on following metrics: architecture (hybrid standalone), algorithm sub-category (global combine), (static dynamic), type number (≤ 2, ≤ 5, > 5) test (virtual real-world). Research materials are sourced from recognized databases where relevant research articles obtained analyzed. Result shows hybrid approach with heuristic has superior performance they applied compare other hybrid. Also, complex Q-learning outperforms algorithms. To conclude future discussed provide reference Cuckoo Search, Shuffled Frog Leaping Artificial Bee Colony improve