Robot Path Planning in Unknown Environments with Multi-Objectives Using an Improved COOT Optimization Algorithm

Abstract

This paper discusses the best path planning algorithm for an autonomous mobile robot in an unknown environment with irregular static and dynamic obstacles and a static and dynamic target based on the improved COOT (ICOOT) optimization algorithm. The ICOOT overcomes the drawbacks of unstable searches in the conventional COOT. The path planning problem is solved by finding the collision-free path between multi-objective shortest path and smoothness. The ICOOT tries to imitate the real world by adding the mobile robot's actual size and the kinematic model with specifications for mobile robots. In order to evaluate the proposed algorithm, thirteen benchmark test functions are used to make a comparison with 30, 100, and 500 dimensions. To test the efficiency of the proposed technique, results are compared with five swarm optimization algorithms. The standard deviation results show that the proposed algorithm gets the best results in 84% of the thirteen test functions for 30 dimensions and 92% for 100 and 500 dimensions. Also, in four complex maps (10×10) m, the mean results show that this method is very useful for robot paths from the start to the target, and the mean distance for ten runs is 13.3797 m for map 1, 13.5164 m for map 2, and 11.9312 m for map 3, and 16.3937 m for map 4. It showed how well it could move quickly and easily around both fixed and moving obstacles.