A comprehensive review of metaheuristic algorithms for mobile robot path planning

Path planning and optimization are essential topics in robotics because they directly affect the effectiveness and safety of robot navigation. The application of metaheuristic methods and algorithms in the field of robot motion planning has attracted the attention of researchers in the field of robotics, given the ease of use and efficiency of the methods in coordinating agents. Metaheuristic algorithms have attracted much attention in recent years due to their efficiency in solving complex optimization problems. This paper summarizes the mobile robot path planning with metaheuristic algorithms, along with their strengths and weaknesses. In this paper, we will focus on a few meta-algorithms: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Artificial Fish Swarm Algorithm (AFSA), Grey Wolf Optimizer (GWO), Bat Algorithm (BA), Firefly Algorithm (FA), and Cuckoo Algorithm (CA). In addition, this study reviews the status of path planning research and its major difficulties to be solved, along with the future trends of path planning.