Whole-Body Inverse Kinematics and Operation-Oriented Motion Planning for Robot Mobile Manipulation

High DoF mobile manipulation of robots is a nonlinear, nonchain redundant problem. In this article, we focus on two subissues of robot mobile manipulation: whole-body inverse kinematics (whole-body IK) and operation-oriented motion planning (OOMP). Whole-body IK solves the robot arm joint configuration and the mobile base position configuration according to the target pose. OOMP generates a feasible trajectory from the current pose to the target pose. The trajectory can avoid obstacles and touch operated objects. We introduce neural network optimization (NNO) methods with two variations to solve whole-body IK and OOMP, respectively. For whole-body IK, we design a fully connected network (FCN) to predict ten DoF of position and joint configurations based on the target pose. We use these ten DoF configurations to derive the predicted pose for online optimization. For OOMP, we design a GRU-based network to generate trajectories based on the initial and goal states. We mainly adopt sphere masks to modify the point cloud properties of the target object dynamically. During optimization, the trajectory keeps away from point clouds but approaches sphere masks. Finally, we conduct extensive experiments both on a Franka Panda robot and a mobile dual-arm robot. The results demonstrate the superior performance of our NNO method on whole body IK and OOMP, and implement mobile manipulation in different environments successfully.