Multi-Agent Systems in Robotics: Coordination and Communication using Machine Learning.

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Coenrad Adolph Groenewald
Gonesh Chandra Saha
Garima Mann
Bharat Bhushan
Eric Howard
Elma Sibonghanoy Groenewald


Multi-Agent Systems (MAS) in robotics have emerged as a promising paradigm for achieving complex tasks through distributed coordination and communication among autonomous agents. This paper explores the integration of machine learning techniques to enhance coordination and communication within MAS, focusing on its implications for robotic systems. The coordination aspect in MAS involves orchestrating the actions of multiple agents to achieve common goals efficiently. Traditional approaches often face challenges in scalability and adaptability, particularly in dynamic environments. Leveraging machine learning, particularly reinforcement learning, game theory, and swarm intelligence, offers novel solutions to address these challenges. Reinforcement learning algorithms enable agents to learn optimal policies for decision-making in dynamic and uncertain environments. [1] Game theory provides frameworks for strategic interaction and negotiation among agents, fostering cooperative behaviors. Swarm intelligence algorithms enable self-organization and emergent behaviors, enhancing adaptability and robustness in MAS. Communication plays a crucial role in facilitating collaboration and information exchange among agents in MAS. Machine learning techniques, such as natural language processing, graph neural networks, and attention mechanisms, offer innovative approaches to communication within robotic systems. Natural language processing enables human-robot interaction and facilitates intuitive communication in collaborative tasks. Graph neural networks enable agents to reason over structured data and perform message passing for decentralized communication. Attention mechanisms allow agents to focus on relevant information and selectively exchange messages, improving communication efficiency.

Integration of machine learning in MAS for coordination and communication presents several challenges and considerations. Issues such as scalability, robustness, and ethical concerns surrounding autonomous decision-making require further exploration and research. However, the potential applications of MAS in robotics are vast, spanning domains such as manufacturing, logistics, search and rescue, autonomous vehicles, surveillance, and monitoring. This paper highlights the significance of machine learning in advancing coordination and communication within MAS for robotics. By leveraging machine learning techniques, MAS can achieve enhanced autonomy, adaptability, and efficiency, paving the way for the development of more intelligent and collaborative robotic systems.

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