The utilization of teleoperation is a crucial aspect of the construction industry, as it enables operators to control machines safely from a distance. However, remote operation of these machines at a joint level using individual joysticks necessitates extensive training for operators to achieve proficiency due to their multiple degrees of freedom. Additionally, verifying the machine resulting motion is only possible after execution, making optimal control challenging. In addressing this issue, this study proposes a reinforcement learning-based approach to optimize task performance. The control policy acquired through learning is used to provide instructions on efficiently controlling and coordinating multiple joints. To evaluate the effectiveness of the proposed framework, a user study is conducted with a Brokk 170 construction machine by assessing its performance in a typical construction task involving inserting a chisel into a borehole. The effectiveness of the proposed framework is evaluated by comparing the performance of participants in the presence and absence of virtual fixtures. This study results demonstrate the proposed framework potential in enhancing the teleoperation process in the construction industry.

翻译：遥操作是建筑业的关键技术之一，可使操作员安全地在远端控制机械。然而，对于具有多自由度的液压工程机械，使用独立操纵杆进行关节级远程操作要求操作员经过大量培训方能达到熟练水平。此外，机械运动结果只能在执行后验证，这使得最优控制变得困难。针对这一问题，本研究提出一种基于强化学习的方法以优化任务性能。通过学习获得的控制策略用于提供高效控制与协调多关节的指令。为评估该框架的有效性，以Brokk 170工程机械为对象开展用户研究，通过评估其在典型建筑任务（将凿子插入钻孔）中的表现进行验证。通过对比受试者在有无虚拟夹具下的操作性能，评估了所提框架的有效性。研究结果表明，该框架具有提升建筑业遥操作流程的潜力。