# Global Path Planning for Differential Drive Mobile Robots Based on Improved BSGA* Algorithm
[22] introduced a cosine factor into the heuristic function of the A* algorithm to optimize the search direction, and adopted a search strategy that synchronizes the starting point and the target point to realize the path planning for the electric disinfection vehicle, which has higher computational efficiency, but does not consider the kinematic model. For the problem of poor path smoothness, scholars optimize the A* algorithm by improving the heuristic function or adjusting the search step size. [PERSON] et al. [23] designed a global path yaw angle based on the relationship between the real-time position and global path, then introduced it into the heuristic function of the A* algorithm to improve the path smoothness. [PERSON] et al. [9] filter the nodes in the closed list to avoid the irregular path. Meanwhile, a cubic B-spline curve is adopted to smooth the path and improve the stability in turning. [PERSON] et al. [24] introduced the jump-point search strategy into A* algorithm and effectively reduced the number of turning points by ignoring unnecessary nodes. For the problem of low security, some scholars have introduced the influence of obstacles into the A* algorithm. [PERSON] et al. [25] converted the distance between the mobile robot and the obstacles into the time cost, so as to optimize the cost function and improve the safety. [PERSON] et al. [26] narrow the search scope by imposing constraints of maximum search distance and maximum path length, then maintain a safe distance by reducing the search points near the obstacles. [PERSON] [27] predicted the future movement of the target using Bernstein basis polynomials combined with the information of the obstacle distribution around the target, then utilized the A* algorithm to search for a safe tracking path. Finally, in the case of dynamic constraints, a quadratic programming method is used to optimize the tracking path to improve the smoothness. [PERSON] [28] proposed an Adaptive Back-stepping Hierarchical Sliding Mode Control (ABHSMC) scheme for three-wheeled mobile robots (3 WMRs) based on RBF neural networks. By aggregating all uncertain components in specific vectors and estimating using an RBF neural network, the effect of uncertainties will be minimized. Then, combining it with the TEB local planner and A* global planner improves the navigation and obstacle avoidance performance. Although these studies have optimized the performance of the A* algorithm to some extent, the direct application to DDMRs are still one-sided, because, in addition to the factors mentioned above, the global path planning of DDMRs also needs to consider the total driving time. The former is related to the setting of the heuristic function, algorithm structure, and processor performance, while the latter is related to the motion state, length, and total turning angle of the path. Therefore, the kinematic model of DDMR is established to analyze the motion state on the turning point. On this basis, Gaussian functions are firstly used to dynamically adjust the weight ratio of the heuristic functions, which effectively reduces the calculation time. Secondly, the BS structure is introduced into the A* algorithm, which solves the problems of repeated node search when there is a large area of obstacles between the starting point and the target point. Finally, a multi-layer turning nodes filtering strategy is proposed to reduce the total turning angle and improve the smoothness of the path. The simulation results show that the improved BSGA* algorithm has higher efficiency than the A* algorithm, GA, and ACO algorithm, and can plan a more reasonable driving route. The LEO ROS robot is used for experimental verification. The experimental results show that it has better results than the five common global path planning algorithms.