2021/03/03 by Jennifer Andersson, Kenneth Bodin, Andersson, Jennifer +7 · 2 citations
Biochemistry, Genetics and Molecular Biology · Engineering · #FOS: Computer and information sciences #Forest Biomass Utilization and Management #I.2.9 #Robotics (cs.RO) #Viral Infectious Diseases and Gene Expression in Insects
paper · pdf · doi:10.48550/arxiv.2103.02315
openalex publication_date 2021/03/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Forestry machines are heavy vehicles performing complex manipulation tasks in unstructured production forest environments. Together with the complex dynamics of the on-board hydraulically actuated cranes, the rough forest terrains have posed a particular challenge in forestry automation. In this study, the feasibility of applying reinforcement learning control to forestry crane manipulators is investigated in a simulated environment. Our results show that it is possible to learn successful actuator-space control policies for energy efficient log grasping by invoking a simple curriculum in a deep reinforcement learning setup. Given the pose of the selected logs, our best control policy reaches a grasping success rate of 97%. Including an energy-optimization goal in the reward function, the energy consumption is significantly reduced compared to control policies learned without incentive for energy optimization, while the increase in cycle time is marginal. The energy-optimization effects can be observed in the overall smoother motion and acceleration profiles during crane manipulation.