Research on path planning in complex environment based on improved A* algorithm - KANG Kaishen; HUANG Hailong (Jixie chuandong)
ObjectiveThe global path planning algorithm for mobile robots currently faces challenges such as excessive inflection points, prolonged computation time, and inefficiency in complex environments. To address these issues, an improved A* algorithm was proposed and experimentally validated under complex environmental conditions.MethodsFirstly, the traditional 8-neighborhood search of the A* algorithm was expanded to a 12-neighborhood search. Subsequently, based on the collision model derived from environmental heuristic information processing, the searched paths were categorized into four cost types, with the least-cost path selected as the optimal trajectory for the mobile robot. Finally, the optimal path obtained from the planning was smoothed using the cubic spline interpolation method.ResultsTest results demonstrate that, compared to the traditional A* algorithm, the improved A* algorithm achieves search speed improvements of 32.68%, 33.40% and 20.17% in simple, moderate and complex environments, respectively. Additionally, the number of severe path deflections is reduced by 35.71%, 43.67% and 47.58% in these environments. The obtained path has the advantages of fewer nodes, a shorter distance, and a smoother trajectory.