基于改进Quick-RRT*算法的机器人路径规划研究OA

To address the limitations of the traditional Quick-RRT* algorithm in mobile robot path planning,including high randomness,low accuracy,and slow convergence to the optimal solution,an improved Quick-RRT* path planning algorithm was proposed.Before finding an initial path,the improved algorithm adopted the target bias sampling strategy of variable sampling area to quickly bypass the obstacles and guide the random tree to grow directly toward the target,which shortened the time to find the initial path.After the initial path was found,the ellipsoid subset sampling strategy combined with beacons was used to continuously optimize the path by performing biased sampling in the ellipsoid subset constraint sampling area and near the obstacles.A hierarchical collision detection method was used to avoid repeated invalid collision detection.Then,the reconnection radius was adaptively adjusted according to the obstacles in the neighborhood of the node and the node density to improve the convergence speed.The experimental data in three two-dimensional simulation environments,that is two-dimensional obstacle-dense environment,simple maze environment,and transverse H-shaped special environment show that compared with RRT*,Quick-RRT*,RRT*-Smart,A* algorithm,genetic algorithm and particle swarm optimization algorithm,the improved algorithm achieves shorter path length,higher planning success rate and less planning time.Finally,in the three-dimensional environment simulation experiment,the practicability and reliability of the algorithm for path planning were further verified.