Simulation-Driven Machine Learning Control of a Forestry Crane Manipulator

Abstract: A forwarder is a forestry vehicle carrying felled logs from the forest harvesting site, thereby constituting an essential part of the modern forest harvesting cycle. Successful automation efforts can increase productivity and improve operator working conditions, but despite increasing levels of automation in industry today, forwarders have remained manually operated. In our work, the grasping motion of a hydraulic-actuated forestry crane manipulator is automated in a simulated environment using state-of-the-art deep reinforcement learning methods. Two approaches for single-log grasping are investigated; amulti-agent approach and a single-agent approach based on curriculum learning. We show that both approaches can yield a high grasping success rate. Given the position and orientation of the target log, the best control policy is able to successfully grasp 97.4% of target logs.Including incentive for energy optimization, we are able to reduce theaverage energy consumption by 58.4% compared to the non-energy optimized model, while maintaining 82.9% of the success rate. The energy optimized control policy results in an overall smoother crane motion andacceleration profile during grasping. The results are promising and provide a natural starting point for end-to-end automation of forestry crane manipulators in the real world.