Inverse Kinematics with Forward Dynamics Solvers for Sampled Motion Tracking
This work addresses the need for smooth, fast inverse kinematics solutions in robot control for sampled motion, representing an incremental improvement over existing methods.
The paper tackled the problem of tracking sampled Cartesian motion with robot end-effectors by enhancing the Jacobian transpose method using a virtual mass matrix, resulting in superior convergence and quality compared to the plain method on the UR10 robot.
Tracking Cartesian motion with end~effectors is a fundamental task in robot control. For motion that is not known in advance, the solvers must find fast solutions to the inverse kinematics (IK) problem for discretely sampled target poses. On joint control level, however, the robot's actuators operate in a continuous domain, requiring smooth transitions between individual states. In this work, we present a boost to the well-known Jacobian transpose method to address this goal, using the mass matrix of a virtually conditioned twin of the manipulator. Results on the UR10 show superior convergence and quality of our dynamics-based solver against the plain Jacobian method. Our algorithm is straightforward to implement as a controller, using common robotics libraries.