Optimization of global path planning for asymmetric mobile robots

Abstract

 This research focuses on optimizing global path planning for asymmetric mobile robots (AMRs), which are characterized by an asymmetric structure and mobility capabilities commonly encountered in practical applications. The objective is to propose a method for optimizing global trajectory planning, enabling efficient movement of asymmetric AMRs in complex environments, reducing task execution time, and optimizing operational performance. The study employs the SPBL algorithm to search for an optimal path in an environment based on an initial trajectory. The path following process utilizing the Dynamic Window Approach (DWA) is optimized by adjusting cost parameters for basic maneuvers such as straight-line motion, rotations, sliding, and reversing. These parameters are designed to prioritize smooth trajectories while avoiding complex and inefficient maneuvers. The results of the research demonstrate that the proposed method minimizes trajectory complexity, leading to reduced task execution time and improved operational performance of asymmetric AMRs. The findings have potential applications in robotic transport systems in the industrial sector.