Work

End-Effector Cartesian Velocity Control for Redundant Loader Cranes Using Reinforcement learning

Year: 2024

Type: article

Abstract: Loader cranes with multiple actuated joints are complex systems to be operated by humans. Development of advanced assistance functions, such as end-effector velocity control in Cartesian space allows for utilizing the machine to its full speed and potential, wherein actuator limits, load balance, and singularities, as well as other complicated effects are handled by the automated function. To this end, this article provides a reinforcement learning-based policy optimization workflow for training and evaluating controllers using large-scale, parallelized invocations of forward kinematics. Monte Carlo evaluations of the closed-loop model are performed to inspect the stability and performance in the whole operational envelope of the loader crane for safe deployment on real machines. Our approach does not require any explicit inverse-kinematics model and is free from complex or hard-coded actuator limits or objectives. Results of simulations and experiments on a real loader crane are provided to showcase the performance of our approach in comparison to Jacobian inverse-based methods.

Source: IEEE Transactions on Robotics

DOI: 10.1109/tro.2024.3502252

Authors: Abdolreza Taheri, Amy Rankka, Pelle Gustafsson, Joni Pajarinen, Reza Ghabcheloo

Institutions: Aalto University, Tampere University, University of Tampere

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,495

Funder: European Commission