Cognition, Exploration, and Collaboration of Multi-Agent Systems in Dynamic Open Environments

The cognitive, exploratory, and collaborative capabilities of multi-agent systems are advancing rapidly, driven by the development of large models and the progress of embodied intelligence. These advances enable robots to be rapidly deployed in real-world applications such as healthcare, transportation, logistics, and rescue missions. Along this rapid development, multi-agent systems are expected to achieve multi-modal fusion representation at the cognitive level, enable proactive exploration at the learning level, and enhance task allocation, resource sharing, and collaborative coordination at the team level.

However, several critical challenges remain, including communication and energy constraints in robot coordination, hallucination issues in large models, as well as safety and ethical concerns in artificial intelligence. The cognition, exploration, and collaboration of multi-robot systems in dynamic and open environments have become major research frontiers.

The goal of this Research Topic is to advance multi-agent systems toward autonomous, safe, and efficient environmental cognition, exploration, and collaborative operation in large-scale, uncertain, and communication-constrained scenarios. Specifically, this Research Topic aims to break the limitations of traditional methods that rely heavily on geometric information and closed-world semantics. We seek to explore how high-level semantic cognition can be deeply integrated into cooperative localization, active exploration, and consensus optimization for multi-agent systems, thereby enabling a paradigm shift from spatial coverage to value-aware exploration.

This Research Topic focuses on theories and methods for cooperative cognition and exploration in multi-agent systems, with a strong emphasis on fundamental principles and practical deployment under real-world constrained conditions. We welcome contributions that advance both foundational theories and practical deployment, explicitly addressing the challenges of decentralization, uncertainty, and sim-to-real transfer. Topics of interest include, but are not limited to:

· Collaborative robotics
· Cooperative SLAM
· Multi-agent reinforcement learning
· Internet of Things and multi-agent deployment
· Active robotic exploration
· Other applications of multi-agent systems

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Systematic Review
• Technology and Code

Keywords: Collaborative robotics; SLAM; multi-agent systems; embodied intelligence; Multi-agent reinforcement learning

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.