Stabilization of Dynamic Systems for Multiple Omni-Directional Mobile Robots

本文考虑了由2个全方位移动机器人组成的混合动力学系统的协调拟镇定问题.利用机器人位置之间的向量与机器人目标之间向量的内积,设计了多步拟镇定律,该控制律能够在避碰后按指数速率运动到目标点,且在整个过程中两机器人之间的距离不小于避碰的安全距离.最后对2个全方位移动机器人进行了仿真,验证了所给方法的有效性。

The effect of the product cost factor on error handling in industrial robots

N.W. Hardy and M.H. Lee. The effect of the product cost factor on error handling in industrial robots. In Maria Gini, editor, Detecting and Resolving Errors in Manufacturing Systems. Papers from the 1994 AAAI Spring Symposium Series, pages 59-64, Menlo Park, CA, March 1994. The AAAI Press. Technical Report SS-94-04, ISBN 0-929280-60-1.

Research into error recovery for sensory robots

Lee, M., Barnes, D. P., Hardy, N. (1985). Research into error recovery for sensory robots. Sensor Review, 5 (4), 194-197.

Knowledge based error recovery in industrial robots

M. H. Lee, D. P. Barnes, and N. W. Hardy. Knowledge based error recovery in industrial robots. In Proc. 8th. Int. Joint Conf. Artificial Intelligence, pages 824-826, Karlsruhe, FDR., 1983.

A control and monitoring system for multiple-sensor industrial robots

Barnes, D. P., Lee, M. H., Hardy, N. W. (1983). A control and monitoring system for multiple-sensor industrial robots. In Proc. 3rd. Int. Conf. Robot Vision and Sensory Controls, Cambridge, MA. USA., 471-479.

Learning and Reuse of Experience in Behavior-Based Service Robots

M.H.Lee, Q. Meng and H. Holstein, ?Learning and Reuse of Experience in Behavior-Based Service Robots?, Seventh International Conference on Control, Automation, Robotics and Vision (ICARCV2002), pp1019-24, December 2002, Singapore

Automatic Error Recovery in Behaviour-Based Assistive Robots with Learning from Experience

Meng Q. and Lee M.H., Automatic Error Recovery in Behaviour-Based Assistive Robots with Learning from Experience, in Proc. INES 2001, 5th IEEE Int. Conf. on Intelligent Engineering Systems, Helsinki, Finland, Sept 2001, pp291-296.

Evolving Controllers for Real Robots: A Survey of the Literature

Walker,J. and Garrett,S. and Wilson,M.S., 'Evolving Controllers for Real Robots: A Survey of the Literature', Adaptive Behavior, 2003, volume 11, number 3, pp 179--203, Sage

Flocking in Simulation and Robots - A Review

Pattison,T. and Wilson,M.S., 'Flocking in Simulation and Robots - A Review', Towards Intelligent Mobile Robots; Proceedings of the 4th annual British conference on autonomous mobile robotics and autonomous systems (TIMR'03), 2003, pp 90-99

Lifelong Evolution for Adaptive Robots

Walker,J. and Wilson,M.S., 'Lifelong Evolution for Adaptive Robots', Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2002, October, pp 984--989

Developmental Learning for Autonomous Robots

M.H. Lee, Q. Meng and F. Chao, 'Developmental Learning for Autonomous Robots', Robotics and Autonomous Systems, 55(9), pp 750-759, 2007.

ROSSI: Emergence of communication in Robots through Sensorimotor and Social Interaction

ROSSI: Emergence of communication in Robots through Sensorimotor and Social Interaction, T. Ziemke, A. Borghi, F. Anelli, C. Gianelli, F. Binkovski, G. Buccino, V. Gallese, M. Huelse, M. Lee, R. Nicoletti, D. Parisi, L. Riggio, A. Tessari, E. Sahin, International Conference on Cognitive Systems (CogSys 2008), University of Karlsruhe, Karlsruhe, Germany, 2008 Sponsorship: EU-FP7

An Active Pattern Recognition Architecture for Mobile Robots

An active, attentionally-modulated recognition architecture is proposed for object recognition and scene analysis. The proposed architecture forms part of navigation and trajectory planning modules for mobile robots. Key characteristics of the system include movement planning and execution based on environmental factors and internal goal definitions. Real-time implementation of the system is based on space-variant representation of the visual field, as well as an optimal visual processing scheme utilizing separate and parallel channels for the extraction of boundaries and stimulus qualities. A spatial and temporal grouping module (VWM) allows for scene scanning, multi-object segmentation, and featural/object priming. VWM is used to modulate a tn~ectory formation module capable of redirecting the focus of spatial attention. Finally, an object recognition module based on adaptive resonance theory is interfaced through VWM to the visual processing module. The system is capable of using information from different modalities to disambiguate sensory input.

Predicting intention to work with social robots

The growing number of robotic solutions geared to interact socially with humans, social robots, urge the study of the factors that will facilitate or hinder future human robot collaboration. Hence the research question: what are the factors that predict intention to work with a social robot in the near future. To answer this question the following socio-cognitive models were studied, the theory of reasoned action, the theory of planned behavior and the model of goal directed behavior. These models purport that all the other variables will only have an indirect effect on behavior. That is, through the variables of the model. Based on the research on robotics and social perception/ cognition, social robot appearance, belief in human nature uniqueness, perceived warmth, perceived competence, anthropomorphism, negative attitude towards robots with human traits and negative attitudes towards interactions with robots were studied for their effects on attitude towards working with a social robot, perceived behavioral control, positive anticipated emotions and negative anticipated emotions. Study 1 identified the social representation of robot. Studies 2 to 5 investigated the psychometric properties of the Portuguese version of the negative attitude towards robots scale. Study 6 investigated the psychometric properties of the belief in human nature uniqueness scale. Study 7 tested the theory of reasoned action and the theory of planned behavior. Study 8 tested the model of goal directed behavior. Studies 7 and 8 also tested the role of the external variables. Study 9 tested and compared the predictive power of the three socio-cognitive models. Finally conclusion are drawn from the research results, and future research suggestions are offered.