首页|New Robotics Findings from Carleton University Described (A Linear Mpc With Cont rol Barrier Functions for Differential Drive Robots)
New Robotics Findings from Carleton University Described (A Linear Mpc With Cont rol Barrier Functions for Differential Drive Robots)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Robotics is now availab le. According to news reporting originating from Ottawa, Canada, by NewsRx corre spondents, research stated, “The need for fully autonomous mobile robots has sur ged over the past decade, with the imperative of ensuring safe navigation in a d ynamic setting emerging as a primary challenge impeding advancements in this dom ain. In this article, a Safety Critical Model Predictive Control based on Dynami c Feedback Linearization tailored to the application of differential drive robot s with two wheels is proposed to generate control signals that result in obstacl e-free paths.” Financial support for this research came from CGIAR. Our news editors obtained a quote from the research from Carleton University, “A barrier function introduces a safety constraint to the optimization problem of the Model Predictive Control (MPC) to prevent collisions. Due to the intrinsic n onlinearities of the differential drive robots, computational complexity while i mplementing a Nonlinear Model Predictive Control (NMPC) arises. To facilitate th e real-time implementation of the optimization problem and to accommodate the un deractuated nature of the robot, a combination of Linear Model Predictive Contro l (LMPC) and Dynamic Feedback Linearization (DFL) is proposed. The MPC problem i s formulated on a linear equivalent model of the differential drive robot render ed by the DFL controller. The analysis of the closed-loop stability and recursiv e feasibility of the proposed control design is discussed.”
OttawaCanadaNorth and Central Americ aEmerging TechnologiesMachine LearningNano-robotRobotRoboticsCarleto n University
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