Human stochastic closed-loop behavior for master-slave teleoperation using multi-leap-motion sensor
- 83 Downloads
Teleoperation has a wide range of applications that have been under development over the past two decades. Previous researches have focused on the control design of teleoperation machine systems to deal with obstacles such as time-delayed stability and transparency. Recent researches have shown that the inclusion of human closed-loop dynamics in control design can improve the performance of robot telemanipulation. The complexity of human behavior arises from the uncertainty of both human physiology and psychology; hence, the investigation can benefit from empirical studies. This study develops a type of statistical learning method to model and evaluate human stochastic closed-loop behavior, which is considered as a hand motion during the direct incremental control process of master-slave teleoperation. The hand trajectory is empirically considered as having a binary linear regression relationship with the error and error rate between the demanded and simulated teleoperator trajectories, while random movements with zero error and error rate are discovered. Hand movement tracking is achieved using a multi-leap-motion sensor (MLM), which is a markerless and natural infrared vision-based manner for motion capture. The established behavior model and statistical learning results reveal certain human properties of operational activities including visual perception, decision making, and robot telemanipulation. The properties indicate some probable system enhancements for future work.
Keywordshuman behavior teleoperation statistical learning leap motion
Unable to display preview. Download preview PDF.
- 4.Norris J S, Powell M W, Vona M A, et al. Mars exploration rover operations with the science activity planner. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005. Barcelona: IEEE, 2005. 4618–4623Google Scholar
- 11.Lii N Y, Chen Z P, Pleintinger B, et al. Toward understanding the effects of visual- and force-feedback on robotic hand grasping performance for space teleoperation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2010. Taipei: IEEE, 2010. 3745–3752CrossRefGoogle Scholar
- 12.Thompson R L, Daniel R W, Murray D W. Experiments on operator matching in visual teleoperation. In: 2000 IEEE International Conference on Systems, Man, and Cybernetics, 2000. Nashville: IEEE, 2000. 931–936Google Scholar
- 13.Cong S, Wang J N. Internet-based and visual feedback networked robot arm teleoperation system. In: International Conference on Networking, Sensing and Control. Chicago: IEEE, 2010. 452–457Google Scholar
- 24.Chopra N, Spong M W, Hirche S, et al. Bilateral teleoperation over the internet: the time varying delay problem. In: Proceedings of the 2003 American Control Conference. Denver, Colorado, USA. 2003. 155–160Google Scholar
- 30.Bachmann E R, McGhee R B, Yun Z, et al. Inertial and magnetic posture tracking for inserting humans into networked virtual environments. In: Proceedings of the ACM Symposium on Virtual Reality Software and Technology. Banff, Canada, 2001. 9–16Google Scholar
- 39.Matlin M W. Cognitive psychology. 2013, http://www.islib.com/ecommerce/resources/online-read-resource/276403/Google Scholar