Aiming to build upon the slow convergence speed and low search efficiency of the potential function-based rapidly exploring random tree star (RRT*) algorithm (P_RRT*), this paper proposes a path planning method for manipulators with an improved P_RRT* algorithm (defined as improved P_RRT*), which is used to solve the path planning problem for manipulators in three-dimensional space. This method first adopts a random sampling method based on a potential function. Second, based on a probability value, the nearest neighbour node is selected by the nearest Euclidean distance to the random sampling point and the minimum cost function, and in the expansion of new nodes, twice expansion methods are used to accelerate the search efficiency of the algorithm. The first expansion adopts the goal-biased expansion strategy, and the second expansion adopts the strategy of random sampling in a rectangular area. Then, the parent node of the new node is reselected, and the path is rerouted to obtain a clear path from the initial point to the target point. Redundant node deletion and the maximum curvature constraint are used to remove redundant nodes and minimize the curvature on the generated path to reduce the tortuosity of the path. The Bezier curve is used to fit the processed path and obtain the trajectory planning curve for the manipulator. Finally, the improved P_RRT* algorithm is verified experimentally in Python and the Robot Operating System (ROS) and compared with other algorithms. The experimental results verify the effectiveness and superiority of the improved algorithm.
To improve the path planning efficiency of a robotic arm in three-dimensional space and improve the obstacle avoidance ability, this paper proposes an improved artificial potential field and rapid expansion random tree (APF-RRT) hybrid algorithm for the mechanical arm path planning method. The improved APF algorithm (I-APF) introduces a heuristic method based on the number of adjacent obstacles to escape from local minima, which solves the local minimum problem of the APF method and improves the search speed. The improved RRT algorithm (I-RRT) changes the selection method of the nearest neighbor node by introducing a triangular nearest neighbor node selection method, adopts an adaptive step and generates a virtual new node strategy to explore the path, and removes redundant path nodes generated by the RRT algorithm, which effectively improves the obstacle avoidance ability and efficiency of the algorithm. Bezier curves are used to fit the final generated path. Finally, an experimental analysis based on Python shows that the search time of the hybrid algorithm in a multi-obstacle environment is reduced to 2.8 s from 37.8 s (classic RRT algorithm), 10.1 s (RRT* algorithm), and 7.4 s (P_RRT* algorithm), and the success rate and efficiency of the search are both significantly improved. Furthermore, the hybrid algorithm is simulated in a robot operating system (ROS) using the UR5 mechanical arm, and the results prove the effectiveness and reliability of the hybrid algorithm.
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