基于机器学习的人机协调操作意图感知与控制方法研究
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摘要
为了实现人机协作所需要的柔顺控制,需要机器人具有一定的合作经验。通过分析人进行意图识别的方法与过程,提出一种基于机器学习识别操作者意图的方法。建立了BP神经网络模型,对机器人进行意图识别离线训练,使其具有一定的合作技能,以便在合作过程中能够根据合作者的作用力和机器人的运动特征在线估计人的意图。该方法的优势在于克服了传统方法中建立人机合作模型困难,尤其复杂多变的人体运动模型,人体阻抗参数难以估计等缺点。实验结果证明:该方法减小了合作者作用力的同时,提升了人机协作的运动同步性,机器人柔顺性得以提高。
Endowing the robot with a certain amount of cooperation experience before tasks is essential to realize its active compliance control. Through analyzing the process of shaping the human experience and observing judgment strategies of an organism, a new intention estimation method for the collaborative robot was proposed. After offline training based on radial basis function BP neural network, the robot could obtain some cooperative skills by the prediction model. During the online execution, the robot could estimate the cooperator's intention according to the force information from human. The method can overcome the question in establishing the model of the human robot cooperation based on traditional methods,especially for the complex and dynamic real-time movement model of human and the uncertainty impedance parameters values. Experiment results show that the cooperator' force is reduced while human robot synchronism motion is developed, so that the compliance of the robot is improved greatly.
Endowing the robot with a certain amount of cooperation experience before tasks is essential to realize its active compliance control. Through analyzing the process of shaping the human experience and observing judgment strategies of an organism, a new intention estimation method for the collaborative robot was proposed. After offline training based on radial basis function BP neural network, the robot could obtain some cooperative skills by the prediction model. During the online execution, the robot could estimate the cooperator's intention according to the force information from human. The method can overcome the question in establishing the model of the human robot cooperation based on traditional methods,especially for the complex and dynamic real-time movement model of human and the uncertainty impedance parameters values. Experiment results show that the cooperator' force is reduced while human robot synchronism motion is developed, so that the compliance of the robot is improved greatly.
引文
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[2]CORTEVILLE B,AERTBELIEN E,BRUYNINCKX H,et al.Human-inspired Robot Assistant for Fast Point-to-point Movements[C]//Proceedings of IEEE International Conference on Robotics and Automation.IEEE,2007:3639-3644.
[3]RAHMAN S M M,IKEURA R.A Human-characteristics-based Novel Control Method for Harmonic Manipulation of Objects with a Power Assist Robot[C]//Proceedings of Ro-man.IEEE,2012:113-119.
[4]IKEURA R,INOOKA H.Cooperative Force Control in Carrying an Object by Two Humans[C]//Proceedings of IEEE International Conference on Systems,Man and Cybernetics.IEEE,1995:2307-2311.
[5]IKEURA R.Variable Impedance Control of Robot for Cooperation with a Human[C]//Proceedings of IEEE International Conference on Robotics and Automation,1995:3097-3102.
[6]MAEDA Y,HARA T,ARAI T.Human-robot Cooperative Manipulation with Motion Estimation[C]//Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems.IEEE,2002:2240-2245.
[7]LI Y,GE S S.Human-robot Collaboration Based on Motion Intention Estimation[J].IEEE/ASME Transactions on Mechatronics,2014,19(3):1007-1014.
[8]THOBBI A,GU Y,SHENG W.Using Human Motion Estimation for Human-robot Cooperative Manipulation[C]//Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems.IEEE,2011:2873-2878.
[9]LUO R,BERENSON D.A Framework for Unsupervised Online Human Reaching Motion Recognition and Early Prediction[C]//Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems.IEEE,2015:2426-2433.
[10]GE S S,LI Y,HE H.Neural-network-based Human Intention Estimation for Physical Human-robot Interaction[C]//Proceedings of International Conference on Ubiquitous Robots and Ambient Intelligence.IEEE,2012:390-395.