MPC-CBF strategy for multi-robot collision-free path-following
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Universidade Federal de Minas Gerais
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Multi-agent time-varying path following problems still offer a wide variety of open challenges, in which efficient collision avoidance is of great importance in this context. This work proposes a solution based on artificial vector fields that generate velocity references for single agents in path-following tasks. A distributed Model Predictive Control (MPC) scheme accountable for double integrator dynamic models and collision avoidance features enables the group of robots to follow the dynamic field in a safe manner. Control Barrier Functions (CBF) are utilized to include collision avoidance in the MPC problem. Simulation scenarios corroborate the method’s efficiency and highlight the improvements in contrast with previous works.
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Robôs - Sistemas de controle
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Conferences , Education , Predictive models , Robustness , Delays , Collision avoidance , Task analysis, Multi-Robot Systems , Collision Avoidance , Motion and Path Planning , Distributed Robot Systems, Dynamic Model , Vector Field , Model Predictive Control , Multiple Integration , Robotic Group , Model Predictive Control Problem , Active Control , Optimal Control , 3D Space , Number Of Agents , Multi-agent Systems , Safe Distance , Prediction Horizon , Deadlock , Safe Set , Pair Of Agents , Safe Path , Sublevel Set
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https://ieeexplore.ieee.org/document/10332984