Publication
Learning Energy-Efficient Trajectory Planning for Robotic Manipulators using Bayesian Optimization
P. Holzmann; M. Maik Pfefferkorn; Jan Peters; R. Findeisen
In: Proceedings of the European Control Conference (ECC). European Control Conference (ECC-2024), Institute of Electrical and Electronics Engineers (IEEE), 2024.
Abstract
Energy-optimal operation of robotic systems has gained high interest in both industry and science. We propose to fuse model predictive control and Bayesian optimization to plan minimum-energy trajectories for industrial robots that guarantee successful executions of the primary task. Particularly, parts of the predictive planner are learned using Bayesian optimization to account for the secondary, higher-level objective – here energy minimization. The effectiveness of the proposed approach is underlined in simulation, where a reduction in energy consumption is observed while maintaining a high quality of task executions.