Task-Space Admittance Controller with Adaptive Inertia Matrix Conditioning

Abstract Whenrobots physically interact with the environment, compliant behaviors should be imposed to prevent damages all entities involved in interaction. Moreover, during physical interactions, appropriate pose controllers are usually based on robot dynamics, which ill-conditioning of joint-space inertia matrix may lead poor performance or even instability. When control is not precise, large interaction forces appear due disturbed end-effector poses, resulting unsafe interactions. To overcome these problems, we propose a task-space admittance controller conditioning adapted online. this end, architecture consists an outer loop, changes reference trajectory achieve desired behavior; and adaptive inner loop track improve closed-loop performance. We evaluated proposed KUKA LWR4+ compared it, via rigorous statistical analyses, motion was replaced by two widely used ones. The presents better than inverse dynamics feedback linearization, similar results when PID gravity compensation loop.