Sampling based path planning for high DoF manipulators without goal configuration

Abstract This paper presents new methods for motion planning for manipulators needing to move in a difficult environment with high probability of collisions. The paper uses the Rapidly exploring Random Trees (RRT) idea, Nd-Cuboid domains for reducing the exploration and produces new methods for path planning without having goal configuration. One method is based on pseudo-inverse Jacobian ( J + ) with weighted least-norm(WLN)(named here as J WLN ) method which is used instead of normal J + for better behavior of the robot arm around joint limits. The second algorithm uses inverse kinematics, which help to drive the manipulator to a goal pose. The third one generates neighbor cells in Cartesian space and selects the best one for random expansion. Inverse kinematics are used in order to create a configuration for a cell. All variations are forward directed trees, since goal configuration is not present. The experiment results, done in a 7 degree-of-freedom (DoF) robot arm, show that all variations provide significant results, with the advantage to use them in grasping scenarios.