Run-Time Optimization of Overall Energy Consumption in Lightweight Collaborative Arms for Repetitive Tasks

Abstract

Lightweight industrial robots are increasingly deployed alongside humans to perform diverse and intelligent industrial tasks. A major concern with these robots is energy efficiency, driven by rising operational costs and environmental impacts. A growing contributor to energy use is the heavy computational workload of their electronic components. Although motion configurations and computational load are often interdependent, current state-of-the-art energy optimization methods tend to address them separately, focusing on individual consumption. In this work, we demonstrate that computational energy is comparable to mechanical energy and show how their dependency affects overall consumption in a Franka Emika Panda robot equipped with a multi-core processing system and two depth cameras. Building on this understanding, we propose a Bayesian approach for the joint optimization of mechanical motion and computational frequency in a robotic arm. Experiments show that the proposed method enables the Franka arm to reduce energy use by 3.7% in pick-and-place tasks and 6.2% in sorting tasks, compared to methods that optimize locomotion and computation separately.