Dynamic path planning of UAV with least inflection point based on adaptive neighborhood A* algorithm and multi-strategy fusion.

Planning a safe and efficient global path in a complex three-dimensional environment is a complex and challenging optimization task. Existing planning algorithms are faced with problems such as lengthy path, too many inflection points and insufficient dynamic obstacle avoidance performance. In order to solve these challenges, this paper proposes a dynamic obstacle avoidance algorithm (MSF-MTPO) with multi-strategy fusion to achieve the least inflection point path optimization. Initially, an adaptive extended neighborhood A* algorithm is designed, which dynamically adjusts the neighborhood extension range according to the distribution of obstacles around the current location, selecting the optimal travel direction and step size each time to reduce redundant paths and unnecessary extended nodes. Then, combined with the two-way search mechanism, starting from the original starting point and the end point, the opposite current node is searched as the target point, respectively, so as to reduce the number of search nodes and reduce the search time. In order to further improve the path efficiency, an inflection point trajectory correction method is designed to eliminate redundant inflection points on the premise of ensuring path safety. Fourthly, in order to solve the problem of path deviation or excessive softening caused by the limited path control points in existing smoothing methods, a local tangent circle smoothing method is proposed, which effectively improves the smoothness of the trajectory on the basis of retaining the superiority of the original path. Finally, the global optimization path is used as the guiding trajectory of artificial potential field method to avoid falling into local optimum and realize dynamic obstacle avoidance. In addition, the performance is compared with several advanced algorithms in different environments, and the MSF-MTPO algorithm has the lowest path cost in different complex scenarios, which proves the effectiveness and stability of MSF-MTPO in UAV 3D path planning.