Motion (physics)

Function (biology)

Robot

Algorithm

Engineering

Pure mathematics

Biology

Motion planning

Node (physics)

Field (mathematics)

Random tree

Computer science

Mathematical optimization

Evolutionary biology

Computer network

Physics

Simulation

Mathematics

Potential field

GRASP

Mathematical analysis

Path (computing)

Artificial intelligence

Tree (set theory)

Structural engineering

Programming language

Geophysics

IEEE Access

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.

Bidirectional Potential Guided RRT* for Motion Planning