首页|Study Data from University of Minnesota Update Knowledge of Robotics (Sequential Control Barrier Functions for Mobile Robots With Dynamic Temporal Logic Specifi cations)
Study Data from University of Minnesota Update Knowledge of Robotics (Sequential Control Barrier Functions for Mobile Robots With Dynamic Temporal Logic Specifi cations)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Robotics are disc ussed in a new report. According to news reporting originating in Minneapolis, M innesota, by NewsRx journalists, research stated, “We address a motion planning and control problem for mobile robots to satisfy rich, time -varying tasks expre ssed as Signal Temporal Logic (STL) specifications. The specifications may inclu de tasks with nested temporal operators or time -conflicting requirements (e.g., achieving periodic tasks or tasks defined within the same time interval).” The news reporters obtained a quote from the research from the University of Min nesota, “Moreover, the tasks can be defined in locations changing with time (i.e ., dynamic targets), and their future motions are not known a priori. This unpre dictability requires an online control approach which motivates us to investigat e the use of control barrier functions (CBFs). The proposed CBFs take into accou nt the actuation limits of the robots and a feasible sequence of STL tasks. They define time -varying feasible sets of states the system must always stay inside . We show the feasible sequence generation process that even includes the decomp osition of periodic tasks and alternative scenarios due to disjunction operators . The sequence is used to define CBFs, ensuring STL satisfaction. We also show s ome theoretical results on the correctness of the proposed method.”
MinneapolisMinnesotaUnited StatesN orth and Central AmericaEmerging TechnologiesMachine LearningNano-robotR oboticsUniversity of Minnesota
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