Multi-Objective Robot Path Planning Using an Improved Hunter Prey Optimization Algorithm

摘要

This work considers the best path planning algorithm for a mobile robot that travels independently in an unknowable environment. To get around the limitations of unstable searches in the conventional Hunter-Prey Optimization Algorithm (HPO), the improved HPO optimization algorithm is used and introduces a new control parameter referred to as randomization adjustment in order to avoid stagnation and early convergence. The absence of the transfer parameter from exploration and exploitation is a significant flaw in the HPO algorithm, which results in an unstable search and additional time waste. Another new parameter called the changing parameter (CP) is used to address this flaw. It is used in an environment with erratic static and dynamic obstacles and a static and dynamic target. Finding a collision-free path that is also the objectively shortest path and the smoothest path can solve the pathplanning problem. The proposed algorithm attempts to mimic the real world by taking into account the actual size of the mobile robot, a kinematic model, and the robot's specifications. The proposed algorithm is evaluated by comparing it on 30 dimensions using 13 benchmark test functions. The performance of the proposed algorithm is evaluated against the results of five swarm optimization algorithms. According to the results of the standard deviation tests, the proposed algorithm performs the best in 92% of the 13 test functions. Furthermore, the average outcomes for three complex maps (1010) m in size demonstrate the potency of this approach for robot paths from the starting point to the target. The average distance over ten runs for maps 1, 2, and 3 is 12.6436 meters, 12.4961 meters, and 17.6547 meters, respectively. It demonstrated how quickly and easily it could avoid both stationary and moving obstacles.