Abstract
Requirement for high accuracy and speed of grasping operation for motion planning is very important. Motion planning algorithms for avoiding obstacles in narrow channels play a vital role for robotic arm effectively operating grasp tasks. The potential function-based RRT*-connect (P-RRT*-connect) algorithm for motion planning is presented by combining the bidirectional artificial potential field into the rapidly exploring random tree star (RRT*) in order to enhance the performance of the RRT*. The motion path is found out by exploring two path trees from the start node and destination node, respectively, with the rapidly exploring random tree star. Two trees advance each other at the same time according to the attractive potential field and the repulsion potential field generated by the artificial potential field method of sampled nodes until they meet. The P-RRT*-connect algorithm is especially suitable for solving the problem of narrow channels. The simulation results prove that the P-RRT*-connect algorithm is more efficient than potential Function-based RRT* (P-RRT*) regardless of the number of iterations or the running time. The experimental data show that the time for the P-RRT*-connect to find the optimal path from the starting node to the target node is half than that of the P-RRT*, and the number of iterations of the P-RRT*-connect is also about one-third less than that of the P-RRT* which is useful for real time.
Highlights
Robot grasping operation is an indispensable part in the process of robot performing tasks
After many years of development, motion planning has matured, and a series of planning methods have been derived, which are mainly divided into the following categories: graph-based search methods, artificial potential field-based methods, random sampling-based methods, intelligent optimization methods and many other different planning methods
In view of the problem that Rapidly Exploring Random Tree (RRT) ∗ algorithm lacks stability and has slow convergence speed, this paper uses the attractive potential field thought of traditional Artificial Potential Field method (APF) algorithm for reference to improve RRT ∗ algorithm, so that mobile robot can make normal planning, and avoid falling into the local minimum region, and its expansion process is biased towards the target point to accelerate the convergence speed
Summary
Robot grasping operation is an indispensable part in the process of robot performing tasks. The basic idea is to construct the artificial potential field under the influence of the attractive potential field at the target position and the repulsive potential field around the obstacle, and search the descending direction of the potential function to find the collision free path, so that the robotic arm will bypass the obstacle under the action of these two forces and move from the starting point to the target point This method has been widely used because of its simple structure, high computational efficiency and real-time control. In view of the problem that RRT ∗ algorithm lacks stability and has slow convergence speed, this paper uses the attractive potential field thought of traditional APF algorithm for reference to improve RRT ∗ algorithm, so that mobile robot can make normal planning, and avoid falling into the local minimum region, and its expansion process is biased towards the target point to accelerate the convergence speed.
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