Dadu

Abstract

Kinematics is the basis of robotic control, which manages the robots' movement, walking and balancing. As a critical part of Kinematics, the Inverse Kinematics (IK) will consume more time and energy to figure out the solution with the degrees of freedom increase. It goes beyond the ability of general-purpose processor based methods to provide real-time IK solver for manipulators with high degree of freedom. In this paper, we present a novel parallel algorithm, Quick-IK, based on the Jacobian transpose method. Via speculative searching in parallel, Quick-IK can reduce the number of iterations by 97% for the baseline Jacobian transpose method. In addition, we propose a novel specialized architecture, IKAcc, to boost the energy efficiency of Quick-IK through hardware acceleration. The evaluation shows that IKAcc can solve IK problem in 12 milliseconds for a 100 degrees of freedom manipulator. In addition, IKAcc can achieve 1700x performance speed-up over the CPU implementation of the original Jacobian transpose method and 30x speedup over the GPU implementation of Quick-IK. At same time, IKAcc achieves about 776x higher energy efficiency than the GPU implementation of Quick-IK.