Vision guided robots have become an important element in the manufacturing industry. In most current industrial applications, vision guided robots are controlled by a look-then-move method. This method cannot support many new emerging demands which require real-time vision guidance. Challenge comes from the speed of visual feedback. Due to cost limit, industrial robot vision systems are subject to considerable latency and limited sampling rate. This paper proposes new algorithms to address this challenge by compensating the latency and slow sampling of visual feedback so that real-time vision guided robot control can be realized with satisfactory performance. Statistical learning methods are developed to model the pattern of target's motion adaptively. The learned model is used to recover visual measurement from latency and slow sampling. The imaging geometry of the camera and all-dimensional motion of the target are fully considered. Tests are conducted to provide evaluation from different aspects.
Statistical Learning Algorithms to Compensate Slow Visual Feedback for Industrial Robots
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received December 16, 2013; final manuscript received June 12, 2014; published online October 21, 2014. Assoc. Editor: Jongeun Choi.
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Wang, C., Lin, C., and Tomizuka, M. (October 21, 2014). "Statistical Learning Algorithms to Compensate Slow Visual Feedback for Industrial Robots." ASME. J. Dyn. Sys., Meas., Control. March 2015; 137(3): 031011. https://doi.org/10.1115/1.4027853
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