2017 Intelligent Systems Conference (IntelliSys) 2017
DOI: 10.1109/intellisys.2017.8324255
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Using confidence factors to share control between a mobile robot tele-operater and ultrasonic sensors

Abstract: A system is presented that shares control between ultrasonic sensors, a tele-operator and a mobile robot. The mobile robot can be directed by the tele-operator, or by ultrasonic sensors, or they can share control. The mobile robot system can change direction if there are obstacles ahead or if it is helpful. Sharing control allows a human tele-operator to drive efficiently and safely. Controller gains are set automatically for a human tele-operator and the ultrasonic sensor system by calculating a confidence fa… Show more

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Cited by 8 publications
(5 citation statements)
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“…Prior works related to robot confidence have focused on the allocation of control between human and robot [39], influencing operator behavior [38], or otherwise directly communicating the robot's self-assessed state [40][41][42][43]. Other work is aimed at intrinsic motivations of the robot [49,50] or understanding of its environment [44].…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Prior works related to robot confidence have focused on the allocation of control between human and robot [39], influencing operator behavior [38], or otherwise directly communicating the robot's self-assessed state [40][41][42][43]. Other work is aimed at intrinsic motivations of the robot [49,50] or understanding of its environment [44].…”
Section: Discussionmentioning
confidence: 99%
“…For example, a robot may provide visual feedback indicating its self-confidence to influence the operator's trust [38]. Alternatively, a model of robot confidence might be used to directly distribute authority, such as setting shared-controller gains to amplify or attenuate inputs from a teleoperator and ultrasonic sensors [39]. Other research includes a robot expressing its certainty in performing policy learned from a human teacher [40][41][42] and modeling a robot's confidence in a human co-worker [43] or its ability to predict human actions in a shared environment [44].…”
Section: Background 21 Robot Confidencementioning
confidence: 99%
“…Difficulties of managing inventory in large complex operational environments originate from randomness and various intertwining business processes [9]. It is hard to monitor, collect, and process necessary information and to make realtime decisions based on seemingly random information [10].…”
Section: Figure 1 Traditional Warehouse Management Systemmentioning
confidence: 99%
“…Steps were: a) Dynamic modelling component, for forecasting reaction to obstacles and veer due to surface changes [21,22]. b) Data assimilation system to ingest data collected from the user joystick and ultrasonic sensors [23][24][25][26][27][28]. c) Prediction System based on systems being developed for routing ships [29,30] to estimate reliability of optimized routing solutions.…”
Section: A Forecastingmentioning
confidence: 99%