2D and 3D path planning for mobile robots based on improved SSA algorithm

Abstract

In recent years, mobile robots have been widely used in industrial automation, logistics, military, medical, and service industries. However, traditional mobile robots face difficulties in complex path planning. To solve this problem, a robot 2D and 3D path planning method has been proposed. Based on improved sparrow search algorithm (SSA), this method introduces a parameter adaptive update strategy to balance search and development. To enhance the algorithm’s path search capability in 3D environments, this study innovatively introduces ant colony algorithm to generate initial paths based on SSA. It also introduces local search mechanism to help the algorithm select the optimal path. Experiments have shown that the improved algorithm can quickly plan the shortest path, with a length reduction of 3.37% and 6.82%. It has reduced the average length by 6.09% and 5.54%, respectively. To verify its search performance, the improved algorithm significantly improved its convergence accuracy, and improved its mean fitness by about 20.5%, 24%, and 27.5%, respectively. The proposed technology has good application effects and will provide important technical references for mobile robots’ path planning in load environments.