2023
DOI: 10.1541/ieejjia.22004524
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Obstacle Avoidance during Teleoperation by Model Predictive Control with Time-varying Delay

Abstract: This paper proposes a method for autonomously replanning the optimal trajectory of a teleoperated mobile robot to avoid obstacles during teleoperation with time-varying delay. Model predictive control (MPC) is applied to compensate for the time-varying delay to ensure the mobile robot autonomously avoids obstacles. An operator operates the robot while viewing the predicted model. The state of the predicted model is sequentially sent to the teleoperation system by the local system including the predicted model.… Show more

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Cited by 1 publication
(2 citation statements)
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“…[70] use a random time delay based on a Gaussian distribution in the range [0.1, 1] s. Hatori and Uchimura [71] and Nagakura et al [72] vary the delay between 3.5 and 4.0 s and 3.0 and 4.0 s. Other studies that implemented time-varying delays include [73][74][75][76][77][78][79][80][81][82][83][84].…”
Section: Modelling Network Delaymentioning
confidence: 99%
See 1 more Smart Citation
“…[70] use a random time delay based on a Gaussian distribution in the range [0.1, 1] s. Hatori and Uchimura [71] and Nagakura et al [72] vary the delay between 3.5 and 4.0 s and 3.0 and 4.0 s. Other studies that implemented time-varying delays include [73][74][75][76][77][78][79][80][81][82][83][84].…”
Section: Modelling Network Delaymentioning
confidence: 99%
“…Likewise, Zhou et al [ 69 ] model delays as a sine wave distribution. Bacha et al [ 70 ] use a random time delay based on a Gaussian distribution in the range [0.1, 1] s. Hatori and Uchimura [ 71 ] and Nagakura et al [ 72 ] vary the delay between 3.5 and 4.0 s and 3.0 and 4.0 s. Other studies that implemented time-varying delays include [ 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 ].…”
Section: Impact Of Network Latency On Teleoperationmentioning
confidence: 99%