Global Path Planning for Differential Drive Mobile Robots Based on Improved BSGA* Algorithm

Abstract

The global path planner is an important part of the navigation system for autonomous differential drive mobile robots (DDMRs). Aiming at the problems such as long calculation time, large number of search nodes, and poor smoothness of path when A* is applied to global path planning, this study proposes an improved bidirectional search Gaussian-A* (BSGA*) algorithm. First, the Gaussian function is introduced to realize the dynamic weighting of the heuristic function, which reduces the calculation time. Secondly, the bidirectional search (BS) structure is adopted to solve the problem of nodes’ repeated search when there are large obstacles between the starting point and the target point. Finally, a multi-layer turning point filter strategy is proposed to further smooth the path. In order to verify the performance of the improved BSGA* algorithm, experiments are carried out in simulation environments with the size of 15 × 15 and 30 × 30, respectively, and compared with the five common global path planning algorithms including ant colony optimization (ACO), D* lite algorithm, and genetic algorithm (GA). The results show that the improved BSGA* algorithm has the lowest calculation time and generates the shortest and smoothest path in the same environment. Finally, the program of the improved BSGA* algorithm is embedded into the LEO ROS mobile robot and two different real environments were built for experimental verification. By comparing with the A* algorithm, Dijkstra algorithm, ACO, D* lite algorithm, and GA, the results show that the improved BSGA* algorithm not only outperforms the above five algorithms in terms of calculation time, length, and total turning angle of the generated paths, but also consumes the least time when DDMR drives along the generated paths.