Research on Path Planning of Adaptive A* Algorithm Based on Multi-Strategy Fusion

Addressing the limitations of existing path planning algorithms, such as excessive turning points and unsmooth trajectories, this paper introduces an enhanced A * algorithm incorporating an adaptive strategy. Initially, a jump pruning technique is integrated into the node search process to streamline pathfinding. Secondly, the search angle constraint optimization method is used to solve the disadvantage of only fixed angles in node exploration. Key nodes are extracted and undergo secondary processing during the search, allowing for the expansion of critical points, cost updates, and route adjustments, which collectively mitigate the risk posed by dangerous nodes and enhance path safety. Furthermore, penalty and environmental impact functions, derived from joint points and environmental data, are introduced to refine the optimization heuristic function. The improved algorithm is simulated and compared with other mainstream path planning algorithms. Results demonstrate that this path planning achieves smoother paths, fewer nodes, and reduced turning angles, thereby aligning more closely with the movement dynamics of mobile robots and demonstrating good global path planning capabilities.