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Distributed Model Predictive Control for Safety-Critical Obstacle Avoidance in Multi-Robot Systems with Social Preferences

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Autor(es):
Magalhaes, Andre Chaves ; de Araujo Pimenta, Luciano Cunha
Número total de Autores: 2
Tipo de documento: Artigo Científico
Fonte: IFAC PAPERSONLINE; v. 59, n. 21, p. 6-pg., 2025-11-28.
Resumo

This paper proposes a novel Decentralized Model Predictive Control (DMPC) approach to address the obstacle avoidance problem in non-holonomic multi-robot systems operating in dynamic environments. Unlike fully cooperative strategies, the proposed method accounts for asymmetric interactions among agents, which exhibit semi-cooperative behaviors with varying levels of cooperation. The approach is based on the concept of Responsibility-aware Social Value Orientation (R-SVO), introduced by Lyu et al. (2022), which expresses the intended relative social implications between pairs of agents. The proposed control system incorporates these social implications using local pairwise responsibility weights, defined with constraints based on a Responsibility-Associated Control Barrier Function for each agent, to prevent collisions between multiple agents in dynamic environments. Simulations are presented to demonstrate the effectiveness and efficiency of the proposed approach in navigation tasks. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) (AU)

Processo FAPESP: 14/50851-0 - INCT 2014: Instituto Nacional de Ciência e Tecnologia para Sistemas Autônomos Cooperativos Aplicados em Segurança e Meio Ambiente
Beneficiário:Marco Henrique Terra
Modalidade de apoio: Auxílio à Pesquisa - Temático