Multi-Agent Reinforcement Learning for Side-by-Side Navigation of Autonomous Wheelchairs

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2024

Autores

Fonseca, T (Autor) Outra; Leao, G (Autor) Outra; Ferreira, LL (Autor) Outra; Armando Jorge Sousa (Autor) FEUP; Severino, R (Autor) Outra; reis, lp (Autor) FEUP

Ata de Conferência Internacional

Título: 2024 IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS, ICARSC

Páginas: 138-143

International Conference on Autonomous Robot Systems and Competitions (ICARSC)

Paredes de Coura, PORTUGAL, MAY 02-03, 2024

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-010-HFE

DOI: 10.1109/icarsc61747.2024.10535919

Abstract (EN): This paper explores the use of Robotics and decentralized Multi-Agent Reinforcement Learning (MARL) for side-by-side navigation in Intelligent Wheelchairs (IW). Evolving from a previous work approach using traditional single-agent methodologies, it adopts a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to provide control input and enable a pair of IW to be deployed as decentralized computing agents in real-world environments, discarding the need to rely on communication between each other. In this study, the Flatland 2D simulator, in conjunction with the Robot Operating System (ROS), is used as a realistic environment to train and test the navigation algorithm. An overhaul of the reward function is introduced, which now provides individual rewards for each agent and revised reward incentives. Additionally, the logic for identifying side-by-side navigation was improved, to encourage dynamic alignment control. The preliminary results outline a promising research direction, with the IWs learning to navigate in various realistic hallways testing scenarios. The outcome also suggests that while the MADDPG approach holds potential over single-agent techniques for the decentralized IW robotics application, further investigation are needed for real-world deployment.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6