# Smooth Optimised A*-Guided DWA for Mobile Robot Path Planning
## 1 Introduction  
A mobile robot is an autonomous system that performs locomotion using onboard power (e.g., batteries) and integrates perception sensors (e.g., lidar, stereo cameras) with navigation algorithms [1, 2]. Under the control of the guidance system, it completes path planning and trajectory tracking control [3]. The specific steps of path planning include determining the starting point and target point; planning a reasonable driving route from the starting point to the target point; using an obstacle avoidance algorithm to ensure that there is no collision with obstacles during driving, while the optimal path is determinedbased on predefined optimisation objectives, such as minimising distance, energy consumption, or travel time [4,5]. This process not only requires considering the geometry of the path, but also needs to comprehensively evaluate multiple factors such as operating efficiency and energy consumption to achieve efficient, safe and environmentally friendly transportation of mobile robots [6,7]. At present, in the research on mobile robot path planning, scholars have proposed clear technical routes for different scenario requirements. In terms of global path planning, the research focuses on classic algorithms such as the A\({}^{\star}\) Algorithm [8,9], the Dijkstra Algorithm [10], and the RRT Algorithm [11,12], and further improves them by introducing intelligent algorithms such as machine learning [13] and deep reinforcement learning [14]. In terms of local path planning, the focus is mainly on the Dynamic Window Algorithm (DWA) [15], Ant Colony Algorithm [16], and Taboo Search Algorithm [17], which have shown superior performance in dynamic obstacle avoidance, real-time trajectory optimisation, etc. It is worth noting that in recent years, the trend of integrated application of the two types of algorithms has become increasingly obvious, and the overall planning effect can be improved by complementing each other's advantages. Scholars have conducted extensive research on the A\({}^{\star}\) algorithm and its improved algorithm in the field of mobile robots, achieving remarkable results. The authors of [18] significantly enhanced the efficiency of 3D path planning by optimising the heuristic function of the A\({}^{\star}\) algorithm, introducing a turning penalty strategy, and implementing search domain priority allocation. This approach effectively reduced computational load and search costs. The authors of [19] significantly enhanced path planning efficiency, reduced path cost, and improved trajectory smoothness through the implementation of limited jump point search and path optimisation strategies. The authors of [20] enhanced the path planning performance by implementing a dynamically weighted heuristic function and path corner reduction strategy, which improved search efficiency and minimised global path turns. The authors of [21] generated smoother and optimised paths by introducing heuristic functions with variable weights, penalties and fifth-order Bessel curves and optimising the paths by combining with the ant colony algorithm. The authors of [22] optimised the paths by adjusting the neighborhood search strategy, introducing dynamic weighting heuristic function and key node extraction strategy, which reduces the search time and redundant nodes, and optimises the efficiency of path planning. The authors of [8] proposed an improved approach by replacing the octal neighborhood with obstacle-free rectangular boundaries and incorporating bidirectional search with Euclidean distance estimation, which effectively reduced redundant nodes and search time. Furthermore, the algorithm introduced an adaptive cost function to enhance road safety and employed the Slide-Rail method to optimise path smoothness. However, a fundamental limitation persists, as conventional A\({}^{\star}\)-based algorithms, including most improved versions, cannot accommodate dynamic obstacles, consequently producing non-executable paths in dynamically changing environments.