Research on mathematical optimization-driven A^* search, DWA improvement, and intelligent robot path planning

Mobile robots encounter issues like low global search efficiency and insufficient static path safety in path planning within complex dynamic environments. This paper proposes a fusion strategy integrating a mathematically optimized improved A^* algorithm (ImpA^*) and an enhanced Dynamic Window Approach (ImpDWA). At the global planning layer, path quality and efficiency are improved through optimizations such as obstacle ratio quantification and dynamic weighting of heuristic functions. At the local planning layer, the DWA evaluation system is optimized by adding a target point cost sub-function and dynamically adjusting weights. At the fusion layer, dual-algorithm collaboration is achieved via global path segmentation and key sub-target transmission. MATLAB simulations show that the ImpA^* algorithm significantly optimizes path length and runtime. The fusion algorithm (ImpA^*-ImpDWA) achieves an obstacle avoidance success rate exceeding 96.5% in dynamic environments, with comprehensive performance superior to other mainstream schemes. It realizes the coordinated balance of core indicators including safety and smoothness, providing reliable support for autonomous robot navigation.