首页|New Robotics Data Have Been Reported by Investigators at Huazhong University of Science and Technology (Discrete-time Flocking Control In Multi-robot Systems Wi th Random Link Failures)

New Robotics Data Have Been Reported by Investigators at Huazhong University of Science and Technology (Discrete-time Flocking Control In Multi-robot Systems Wi th Random Link Failures)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Robotics are presented i n a new report. According to news reporting originating in Wuhan, People’s Repub lic of China, by NewsRx journalists, research stated, “The stability of multi-ro bot flocking is closely related to the control model and the quality of wireless communications. In this study, we delve into the discrete-time flocking control problem for multi-robot systems (MRS) operating under an ad hoc network with ra ndom link failures.” The news reporters obtained a quote from the research from the Huazhong Universi ty of Science and Technology, “Specifically, we initially propose a discrete-tim e control model for multi-robot flocking that describes the inherent instability in the transmission of robot state information as a Bernoulli variable. Compare d to existing controllers, this model requires less information exchange and is more practical for implementation in multi-robot systems. Subsequently, stabilit y analysis is conducted for the controller, revealing that the MRS cannot achiev e asymptotic flocking but can attain flocking in expectation when relevant stabi lity conditions are satisfied. The stability conditions are deduced using the Ly apunov method, imposing constraints on the controller gains, the interaction per iod, and the successful transmission probability of communication links. Notably , the upper bound for the interaction period is significantly improved, thereby alleviating the communication network’s burden.”

WuhanPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningRobotRoboticsHuazhong University o f Science and Technology

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Oct.14)