RRT Based Obstacle Avoidance Path Planning for 6-DOF Manipulator
Ben Han, Shan Liu · 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) · 2020
Obstacle avoidance path planning is an important research topic in robot operation. As a complex system with multiple inputs and multiple outputs, highly nonlinear and strong coupling, the manipulator cannot be directly regarded as a particle in Cartesian space, so many path planning algorithms for mobile robots cannot be directly extended to manipulators. In this issue, based on the rapidly-exploring random tree algorithm, this paper proposed an improved path planning method. The path is searched in the joint space of the manipulator, collision detection is performed in Cartesian space by solving forward kinematics, and then the Bezier curve is used to smooth the path. The experimental results indicate that the proposed method can effectively plan a smooth, collision-free and less expensive path.