首页|New Robotics Study Results from Deakin University Described (Minimal Operation Region Prediction for Networked Control Robotic Manipulators Subject To Time-varying Delays and Disturbances)
New Robotics Study Results from Deakin University Described (Minimal Operation Region Prediction for Networked Control Robotic Manipulators Subject To Time-varying Delays and Disturbances)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Researchers detail new data in Robotic s. According to news reporting originatingfrom Waurn Ponds, Australia, by NewsR x correspondents, research stated, “Due to the disturbances andvarying latency, a teleoperated robotic manipulator might not comply with the master control com mands.Although prior studies on minimising the impact of network latency and di sturbances on teleoperatedrobots were conducted, there has been very little res earch on the prediction of minimal operation regionsof robotic arms, especially in the worst-case scenarios when the disturbances and time delays still prevaileven after impact minimisation.”Financial support for this research came from Telematics Trust.Our news editors obtained a quote from the research from Deakin University, “Thi s study investigatesthe problem and proposes a novel solution to predicting min imal operation regions of networked controlrobotic manipulators. The proposed m ethod can be used to forecast safe operation regions in which themanipulators w ill certainly enter and exclude regions that the robots will never penetrate. Le veraging ona Lyanonov Krasovskii criterion, the method performs region predicti on by establishing minimal reachablebounding sets of the nonlinear, perturbed r obotic arm’s state vectors guided via a time-varying delaydominantnetwork. Tho ugh predominantly nonlinear, the entire prediction process is formulated as a tractable Linear Matrix Inequality (LMI) optimisation problem, which can be solved efficiently and effectively.Efficacy of the proposed method is validated with simulations where a simulated robotic arm is distortedwith time-varying delays and disturbances. This study investigates the nonlinear problem of predicting minimal operation regions of robotic arms subject to time-varying delays, uncertai nties and disturbances.”
Waurn PondsAustraliaAustralia and New ZealandEmerging TechnologiesMachine LearningNano-robotRoboticsRobotsDeakin University
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