Three-Dimensional Path Planning Optimization for Length Reduction of Optimal Path Applied to Robotic Systems

Abstract

Path planning is an intertemporal problem in the robotics industry. Over the years, several algorithms have been proposed to solve it, but weaknesses are constantly identified by researchers, especially in creating an optimal path in a three-dimensional (3D) environment with obstacles. In this paper, a method to reduce the lengths of optimal 3D paths and correct errors in path planning algorithms is proposed. Optimization is achieved by combining the information of a generated two-dimensional (2D) path with the input 3D path. The 2D path is created by a proposed improved artificial fish swarm algorithm (AFSA) that contains several improvements, such as replacing the random behavior of the fish with a proposed one incorporating the model of the 24 possible movement points and utilizing an introduced model to assist the agent’s navigation called obstacles heatmap. Moreover, a simplified ray casting algorithm is integrated with the improved AFSA to further reduce the length of the final path. The improved algorithm effectually managed to find the optimal path in complex environments and significantly reduce the length of the formed path compared with other state-of-the-art methods. The path was implemented in real-world scenarios of drone and industrial robotic arm applications.