Quantitative Performance Review of Wheeled Mobile Robot Path Planning Algorithms

Authors : Oluwaseun MARTİNS, Adefemi Adeyemi ADEKUNLE, Samuel Babatope ADEJUYIGBE, Hezekiah Oluwole ADEYEMİ

Pages : 765-784

Doi:10.35378/gujs.792682

View : 42 | Download : 9

Publication Date : 2021-09-01

Article Type : Review Paper

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

ORIGINAL ARTICLE URL