| G.E. Hovland and B.J. McCarragher. Dynamic sensor selection for robotic systems. In Proceedings of the 1997. |
....45, 46, 51, 64, 75, 84, 87] Random motions have long been recognized as a good approach to solving small uncertainties in assembly tasks. Moreover, random motion is a simple way to implement active sensing, in order to (persistently) excite all uncertainties in the system. 3. Data fusion, [3, 24, 26, 48, 60]. Information from different sensors are combined into one single outcome, with (hopefully) better resolution and reliability than could be produced by the single sensors individually. 4. Classification, also known as (template pattern) matching, labelling or event object pattern ....
....controller has the option to choose between different alternative subsequent subtasks, the one with highest probability is chosen. Sequence planning is often done on the basis of a lower level classification module that assigns the probabilities to the different alternatives. 6. Sensor selection, [36, 48, 62]. Based on the current state of the system (model, parameters, past and current sensor readings) a decision is made as to which sensor to use next, and to measure which feature. Again, the choice is based on which sensing action has the highest probability for success. 7. Tracking, 7, 28, 34, ....
G. Hovland and B. J. McCarragher. Dynamic sensor selection for robotic systems. In IEEE Int. Conf. Robotics and Automation, pages 272--277, Albuquerque, NM, 1997.
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G.E. Hovland and B.J. McCarragher. Dynamic sensor selection for robotic systems. In Proceedings of the 1997.
No context found.
G. Hovland and B. McCarragher, "Dynamic sensor selection for robotic systems," in Proc. 1997.
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Geir E. Hovland and Brenan J. McCarragher. Dynamic sensor selection for robotic systems. In Proceedings of the 2002 IEEE International Conference on Robotics and Automation, pages 272--277. IEEE, 1997.
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