Chen Xiaolei, You Bo, Dong Zheng
The Key Laboratory of Intelligent Technology for Cutting and Manufacturing Ministry of Education, Harbin University of Science and Technology, Harbin, China.
The Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin, China.
Front Neurorobot. 2024 Apr 5;18:1393738. doi: 10.3389/fnbot.2024.1393738. eCollection 2024.
Due to the heavy burden on human drivers when remotely controlling hexapod robots in complex terrain environments, there is a critical need for robot intelligence to assist in generating control commands. Therefore, this study proposes a mapping process framework that generates a combination of human-robot commands based on decision target values, focusing on the task of robot intelligence assisting drivers in generating human-robot command combinations. Furthermore, human-robot state constraints are quantified as geometric constraints on robot motion and driver fatigue constraints. By optimizing and filtering the feasible set of human-robot commands based on human-robot state constraints, instruction combinations are formed and recommended to the driver in real-time, thereby enhancing the efficiency and safety of human-machine coordination. To validate the effectiveness of the proposed method, a remote human-robot collaborative driving control system based on wearable devices is designed and implemented. Experimental results demonstrate that drivers utilizing the human-robot command recommendation system exhibit significantly improved robot walking stability and reduced collision rates compared to individual driving.
由于在复杂地形环境中远程控制六足机器人时人类驾驶员负担过重，因此迫切需要机器人智能来协助生成控制命令。为此，本研究提出了一种映射过程框架，该框架基于决策目标值生成人机命令组合，重点关注机器人智能协助驾驶员生成人机命令组合的任务。此外，将人机状态约束量化为机器人运动的几何约束和驾驶员疲劳约束。通过基于人机状态约束对人机命令的可行集进行优化和过滤，实时形成指令组合并推荐给驾驶员，从而提高人机协作的效率和安全性。为验证所提方法的有效性，设计并实现了一种基于可穿戴设备的远程人机协同驾驶控制系统。实验结果表明，与单独驾驶相比，使用人机命令推荐系统的驾驶员的机器人行走稳定性显著提高，碰撞率降低。
