MPC-CBF Strategy for Multi-Robot Collision-Free Path-Following

  • Arthur Da C. Vangasse UFMG
  • Guilherme V. Raffo UFMG
  • Luciano C. A. Pimenta UFMG


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.
Palavras-chave: Multi-Robot Systems, Collision Avoidance, Motion and Path Planning, Distributed Robot Systems
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VANGASSE, Arthur Da C.; RAFFO, Guilherme V.; PIMENTA, Luciano C. A.. MPC-CBF Strategy for Multi-Robot Collision-Free Path-Following. In: SIMPÓSIO BRASILEIRO DE ROBÓTICA E SIMPÓSIO LATINO AMERICANO DE ROBÓTICA (SBR/LARS), 15. , 2023, Salvador/BA. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 284-289.