Forestry forwarders play a central role in mechanized timber harvesting by picking up and moving logs from the felling site to a processing area or a secondary transport vehicle. Forwarder operation is challenging and physically and mentally exhausting for the operator who must control the machine in remote areas for prolonged periods of time. Therefore, even partial automation of the process may reduce stress on the operator. This study focuses on continuing previous research efforts in application of reinforcement learning agents in automating log handling process, extending the task from grasping which was studied in previous research to full log loading operation. The resulting agent will be capable to automate a full loading procedure from locating and grappling to transporting and delivering the log to a forestry forwarder bed. To train the agent, a trailer type forestry forwarder simulation model in NVIDIA's Isaac Gym and a virtual environment for a typical log loading scenario were developed. With reinforcement learning agents and a curriculum learning approach, the trained agent may be a stepping stone towards application of reinforcement learning agents in automation of the forestry forwarder. The agent learnt grasping a log in a random position from grapple's random position and transport it to the bed with 94% success rate of the best performing agent.

翻译：林业集材机在机械化木材采伐中扮演着核心角色，负责从采伐现场拾取并运输原木至加工区域或二次转运车辆。集材机操作对驾驶员具有挑战性，且需在偏远地区长时间操控设备，导致其身心俱疲。因此，即使实现该过程的部分自动化也可能减轻驾驶员负担。本研究聚焦于延续先前在应用强化学习智能体实现原木处理自动化方面的探索，将任务范围从已有研究涉及的抓取操作扩展至完整的原木装载流程。所开发的智能体将能够自动化执行从定位抓取到运输交付原木至林业集材机车斗的完整装载程序。为训练智能体，本研究基于NVIDIA Isaac Gym平台开发了拖挂式林业集材机仿真模型及典型原木装载场景的虚拟环境。通过强化学习智能体与课程学习方法，训练完成的智能体可成为强化学习智能体在林业集材机自动化应用中的重要基石。实验表明，最佳性能智能体能够从随机抓取位置成功抓取随机摆放的原木并运输至车斗，成功率可达94%。