首页|University of Texas Arlington Researcher Illuminates Research in Robotics (Model ing of Cooperative Robotic Systems and Predictive Control Applied to Biped Robot s and UAV-UGV Docking with Task Prioritization)
University of Texas Arlington Researcher Illuminates Research in Robotics (Model ing of Cooperative Robotic Systems and Predictive Control Applied to Biped Robot s and UAV-UGV Docking with Task Prioritization)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ro botics. According to news reporting out of Arlington, Texas, by NewsRx editors, research stated, “This paper studies a cooperative modeling framework to reduce the complexity in deriving the governing dynamical equations of complex systems composed of multiple bodies such as biped robots and unmanned aerial and ground vehicles.” Funders for this research include Office of Naval Research. Our news journalists obtained a quote from the research from University of Texas Arlington: “The approach also allows for an optimization-based trajectory gener ation for the complex system. This work also studies a fast-slow model predictiv e control strategy with task prioritization to perform docking maneuvers on coop erative systems. The method allows agents and a single agent to perform a dockin g maneuver. In addition, agents give different priorities to a specific subset o f shared states. In this way, overall degrees of freedom to achieve the docking task are distributed among various subsets of the task space. The fast-slow mode l predictive control strategy uses non-linear and linear model predictive contro l formulations such that docking is handled as a non-linear problem until agents are close enough, where direct transcription is calculated using the Euler disc retization method.”
University of Texas ArlingtonArlingtonTexasUnited StatesNorth and Central AmericaEmerging TechnologiesMachin e LearningNano-robotRoboticsRobots
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