用强化学习实现伐木机自动装运，成功率94%。
Towards Reinforcement Learning Based Log Loading Automation
- 基于强化学习与课程学习，训练智能体完成定位、抓取、运输全流程。
- 在模拟环境中实现94%的成功率，可自动完成从抓取到卸载的全过程。
- 适合对林业自动化、强化学习应用感兴趣的科研与工程人员。
林业前移装载机在机械化采伐中负责将原木从伐木现场搬运至加工区或次级运输车辆。其操作对操作员要求高，长期远程作业易导致身心疲劳。因此，部分自动化可显著减轻操作负担。本研究延续此前强化学习应用于原木抓取的研究，将任务拓展至完整的装运流程。开发了基于NVIDIA Isaac Gym的拖车式前移装载机仿真模型及典型装运场景虚拟环境。通过强化学习与课程学习方法训练智能体，使其能自动完成从定位、抓取到运输并放置于装载床的全过程。最佳智能体在随机位置抓取原木并运送到指定位置的成功率达94%，为未来强化学习在林业机械自动化中的应用奠定基础。
原文摘要 · Abstract (English)
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.
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