Leveraging Contact Dynamics for Energy-and Torque-Optimized Inverse Kinematics

Abstract

Energy efficiency is a critical factor in the sustainable operation of industrial robotic systems, where reducing energy consumption can lead to cost savings and environmental benefits. Traditional inverse kinematics (IK) approaches prioritize collision avoidance and precision, often overlooking the potential benefits of intentional contact interactions, such as bracing or leaning, which can reduce actuator torques and energy usage. This paper presents a novel contact-aware IK framework that explicitly integrates energy efficiency into planning. The framework consists of two stages: In the first stage, kinematic optimization solves the IK problem using analytical gradients, minimizing both end-effector residuals and the distance to contact surfaces. In the second stage, dynamic optimization refines the solution, identifying stable configurations that minimize energy consumption via controller-in-the-loop optimization. A lightweight control strategy stabilizes the solution without relying on complex contact force models, enabling rapid IK computation and path parametrization. Experimental validation on a Kinova Gen3 robot demonstrates torque reductions of up to 85.3% and a 9.2% reduction in overall energy consumption, highlighting the potential of this method for energy-efficient manipulation in industrial settings.