基于技能匹配、学习、拓展的遥操作机器人控制研究

项目名称： 基于技能匹配、学习、拓展的遥操作机器人控制研究

项目编号： No.61473120

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 自动化学科

项目作者： 杨辰光

作者单位： 华南理工大学

项目金额： 83万元

中文摘要： 遥操作机器人在海空探测、制造装配、抢险除害等传统领域发挥着重要作用。随着其在医疗康复、个人护理、家政服务和教育娱乐等新领域应用的不断增长，遥操作机器人控制器在稳定性、鲁棒性、可靠性和高精度等基础指标上更被要求具备可与人媲美的智能。为了增强遥操作员用户体验、提高人机合作效率，机器人控制器还需要智能调节来保证其响应与操作员个性化特征达到最优匹配。本项目将利用智能学习算法和先进控制理论对人的运动神经控制机理建模。在深刻理解神经运动元控制和自调节原理的基础上，开发超越传统模仿示教的技能学习手段，将操作员灵巧多用技能有效传递给机器人。进一步，项目研究将使机器人在时间、空间上对所学技能进行拓展，实现性能提升。为实现人机匹配，本项目拟根据不同操作员动作特征建立个性化神经控制模型，并据此对机器人控制器在线优化，实现最佳人机合作。本项目将有利于新一代遥操作机器人技术的发展，并能有力促进人机合作技术的深入研究。

中文关键词： 机器人控制；智能控制；神经网络；自适应控制

英文摘要： Telerobots play an important role in traditional application fields such as space and subsea exploration, advanced manufacturing and assembly, dangerous and hazardous objects manipulation, etc. With emerging applications in medical operation, health care, rehabilitation, domestic services and edutainment, a telerobot controller nowadays is required to own human-like dexterity and versatility, in addition to stability, robustness and high precision. To improve teleoperation efficiency and to enhance the tele-operator's user experience, it is expected that the telerobot controller can make intelligent adaptation to individual users according to their motion characteristics, so that a better match between the human operator and the robot can be achieved. Based on the thorough understanding and solid research background we have built in neuromotor control and adaptation mechanism, this project aims at developing effective and efficient methods to transfer dexterous and versatile skills from human operator to telerobots, which is viewed superior to traditional learning-by-demonstration approach. To ensure an optimal match between robot response and human operation, a personalized human motor model will be built to capture the user-specific neuromotor characteristics by employing advanced control theories and intelligent learning techniques. We will further enable the telerobot to generalize the learned skills both temporally and spaciously, producing super-human performance. The telerobot controller can then adjust to achieve optimal human-robot collaborations. We expect the matching, learning and generalization approach of telerobot control proposeed in this project will bring positive impacts on the development of next-generation telerobot, and push forward the study on human-robot collaboration.

英文关键词： Robot Control;Intelligent Control;Neural Networks;Adaptive Control