Path planning of a manipulator based on an improved P_RRT* algorithm

Abstract

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.

Combined improved A* and greedy algorithm for path planning of multi-objective mobile robot

“…In recent years, warehouse storage 1 , 2 has gradually developed in the direction of intelligence and systematization by the "Made in China 2025" strategy 3 and the rapid development of the logistics industry.…”

“…The path planning of AMR is a constrained optimization problem. The algorithms include Genetic algorithm 4 , Probabilistic Roadmap 5 , Rapidly-exploring-random Tree 3 , 6 , Dijkstra algorithm 7 , A* algorithm 8 – 10 , Machine learning algorithm 11 – 13 , Ant Colony algorithm 14 , Particle Swarm Optimization 15 , Artificial potential field algorithm 16 , 17 and Breath First Search algorithm 18 , and so on.…”

“…The algorithm can effectively ensure high safety and low energy consumption of AGVs in the logistics and storage environment. Yi et al 3 used a random sampling method based on potential function to improve the RRT* algorithm. Also remove the redundant nodes to make the path follow smoothly.…”

Investigations on Speed Planning Algorithm and Trajectory Tracking Control of Intersection Scenarios Without Traffic Signs

“…Under normal circumstances, the path planned by the blue vehicle cannot pass through these areas under the influence of the structured road. At this time, it can consider Yi et al's improved P_RRT * algorithm [22], Xiang et al's improved A * algorithm [23] or Wang et al's PGI-RRT * algorithm [24]. These algorithms are effective algorithms in the face of complex static obstacles.…”

Research on informed-RRT* algorithm for multi-strategy optimization in mobile robot path planning

“…Haifang W [11] proposed a locally optimal path planning based on goal offset expansion and Cantmull -Rom spline interpolation of two-way fast exploration random tree star Bi-RRT* path planning algorithm. Yi J [12] proposed an improved P_RRT* algorithm for robotic path planning to solve the problems of slow convergence speed and low search efficiency of the P_RRT* algorithm.…”