Path planning of six-DOF serial robots based on improved artificial potential field method
Ning Zhang, Yong Zhang, Chao Ma, Bin Wang
Abstract
Ning Zhang, Yong Zhang, Chao Ma, Bin Wang
Abstract
The robot obstacle avoidance path planning is to find a path that can reach the target point from the start point without collision in the actual working environment of robot. In this paper, RBT-6T/S03S manipulator is taken as the research object, which is the Six-DOF Serial Robots. Based on the traditional artificial potential field method, a new potential energy function is used to replace the function of the traditional artificial potential field method to guide the path planning of the manipulator, at the same time by adding virtual obstacles to avoid manipulator into the local minimum point. This method avoids the cumbersome inverse kinematics calculation in the process of path planning, which greatly reduces the complexity degree of algorithm operation. Experiments show that the improved artificial potential field method can effectively carry out obstacle avoidance path planning.
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The robot obstacle avoidance path planning is to find a path that can reach the target point from the start point without collision in the actual working environment of robot. In this paper, RBT-6T/S03S manipulator is taken as the research object, which is the Six-DOF Serial Robots. Based on the traditional artificial potential field method, a new potential energy function is used to replace the function of the traditional artificial potential field method to guide the path planning of the manipulator, at the same time by adding virtual obstacles to avoid manipulator into the local minimum point. This method avoids the cumbersome inverse kinematics calculation in the process of path planning, which greatly reduces the complexity degree of algorithm operation. Experiments show that the improved artificial potential field method can effectively carry out obstacle avoidance path planning.
Key concepts: Motion planning, Obstacle avoidance, Obstacle, Potential field, Computer science, Path (computing), Robot, Start point