Hybrid Path Planning Based on Improved Sparrow Search Algorithm and Dynamic Window Approach

To address the drawbacks of the sparrow search algorithm (SSA), including poor initial solution quality, slow convergence, and drop into the local optimal condition easily, this work proposed an upgraded SSA to handle the hybrid path planning issue. First of all, the Tent chaotic mapping approach is employed to increase its diversity. Subsequently, the producers' locations are adjusted with the adaptive weight strategy, which balances for exploration and exploitation. At last, the locations of the scroungers are revised via the differential evolution algorithm (DE), which can enhance the algorithm's capacity to break out the local ideal situation. Furthermore, the upgraded SSA (TADSSA) is coupled with the dynamic window approach (DWA) to avoid obstructions and smooth the route. The proposed method is known as TADSSA-DWA. The proposed TADSSA-DWA and other state-of-the-art techniques are tried to the mobile robot's hybrid path planning independently in order to confirm its effectiveness. While compared to other classical algorithms, the experimental findings demonstrated that the proposed TADSSA-DWA increased the path smoothness by 69.05% and reduced the path length and turns by around 14.12% and 71.42%, respectively. Not noly can the proposed TADSSA-DWA shortens the length of the planned route, but also has capabilities of path smoothing and real-time obstacle avoidance.