# Smooth Optimised A*-Guided DWA for Mobile Robot Path Planning
## 1 Introduction
The Dynamic Window Approach (DWA) specializes in local dynamic obstacle avoidance, demonstrating robust capability in handling real-time environmental changes. The authors of [23] significantly improved path planning efficiency and safety for service robots in dense crowds through several key innovations: pedestrian and crowd interaction space modeling, dynamic pedestrian azimuth constraints, classification-based decision making, and adaptive velocity weighting adjustment. Compared to conventional DWA, their social-DWA achieves superior performance in pedestrian/group avoidance, motion velocity optimisation, and travel time reduction while maintaining effective safety distances, thereby enhancing overall path planning flexibility and adaptability. However, the improved algorithm may encounter elevated computational complexity in high-density environments, particularly when processing multiple dynamic obstacles, which could increase system computational load. The authors of [15] proposed an enhanced DWA to address the inherent limitation of fixed parameters in balancing safety and speed in complex environments. By refining the velocity cost function and incorporating a target-point distance cost function, their approach improves path smoothness and inspection robots' navigation capability. While the modified algorithm demonstrates significantly better obstacle avoidance performance in complex settings, it introduces higher computational demands, necessitating further optimisation of the fuzzy control module to enhance real-time performance. A fundamental limitation persists, as locally planned paths may deviate from global optima, and the obstacle avoidance efficiency remains inadequate for complex scenarios with numerous dynamic obstacles. In summary, path planning for mobile robots faces several persistent challenges: the traditional A* algorithm often produces paths with high redundancy and poor smoothness; integrating the DWA with global planners can cause the robot to deviate from the optimal path; and the efficiency of dynamic obstacle avoidance remains limited. To overcome these issues, this paper presents a Smooth-Optimised A*-Guided DWA (SOA-DWA) algorithm. The proposed solution implements two key innovations. First, it implements an enhanced A* algorithm incorporating path smoothing and redundant node elimination to generate kinematically feasible global optimum paths. Second, the DWA evaluation function is enhanced by incorporating three subcomponents: a trajectory distance metric to the global path, an evaluation of directional alignment, and a dynamic obstacle avoidance metric. These additions significantly improve both real-time obstacle avoidance and adherence to the global path. Comprehensive simulation experiments on simple and complex 2D grid maps as well as on real-world scenarios demonstrate the effectiveness of the algorithm in different operational scenarios. The main contributions and uniqueness of this study include the following four areas. (1) A path pruning strategy and path smoothing strategy are added to the traditional A* algorithm to optimise the search path and reduce the number of redundant nodes and turns. (2) A global path constraint strategy is proposed to solve the problem of deviating from the global optimal path when the DWA algorithm incorporates the global path planning algorithm. (3) An obstacle avoidance efficiency optimisation strategy is proposed to improve the efficiency when avoiding obstacles. (4) A fusion algorithm for smooth optimisation of A*-guided DWA is proposed, which makes the mobile robot return to the global optimal path more efficiently while ensuring safe obstacle avoidance. The remainder of this paper is organised as follows. The improved A* algorithm is presented in Section 2. Section 3 presents the improved DWA algorithm. Section 4 presents the fusion algorithm for smooth optimised A*-guided DWA. Section 5 analyzes the performance of the fusion algorithm. Section 6 summarizes the paper.