Inverse Kinematics with Forward Dynamics Solvers for Sampled Motion Tracking 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 robotu0027s actuators operate in a continuous domain, requiring smooth transitions between individual states. In this work, we present a boost to the wellknown 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 dynamicsbased solver against the plain Jacobian method. Our algorithm is straightforward to implement as a controller, using common robotics libraries.

Predictive Inverse Kinematics: optimizing Future Trajectory through Implicit Time Integration and Future Jacobian Estimation This paper presents an inverse kinematics (IK) method which can control future velocities and accelerations for multibody systems. The proposed IK method is formulated as a quadratic programing (QP) that optimizes future joint trajectories. The features of the proposed IK are: (1) the evaluation of accelerations at future time instances, (2) the trajectory representation that can implicitly integrate the time integral formula into QP, (3) the computation of future Jacobian matrices based on the comprehensive theory of differential kinematics proposed in our previous work. Those features enable a stable and fast IK computation while evaluating the future accelerations. We also conducted thorough numerical studies to show the efficiency of the proposed method.