Multi-mobile robot systems require collaborative navigation and path planning approaches to accomplish tasks effectively in dynamic and complex environments. Distributed control of heterogeneous robots, ensuring coordination, and efficient task allocation pose both theoretical and practical challenges. Collaborative planning allows robots to move without collisions, manage uncertainties, and complete missions autonomously. Unlike single-robot systems where optimizing one trajectory is sufficient, multi-robot scenarios demand coordination, collision avoidance, and communication management. At the same time, they provide flexibility, scalability, and fault tolerance, since malfunctioning robots’ tasks can be reassigned to others to maintain mission continuity. A distinctive strength of multi-robot systems is their ability to integrate distributed information. Through collective sensor fusion, robots combine complementary perceptions, and distributed reasoning enables context-aware, scenario-specific decisions. These mechanisms significantly enhance navigation and decision-making. However, achieving such benefits introduces challenges due to computation and communication constraints inherent in distributed architectures, making collaborative path planning both complex and transformative.

This Research Topic aims to advance collaborative navigation and path planning approaches for multi-robot systems. Effective task sharing, distributed control, and coordination are essential for safe and efficient operation in complex environments. Robots’ ability to perceive, decide in real time, and plan paths autonomously directly affects system performance. The objective is to achieve accurate and adaptive path planning and decision-making in dynamic and uncertain environments by leveraging advanced AI techniques such as deep learning, reinforcement learning, and generative AI. While reinforcement learning enables adaptive control through interaction with the environment, generative AI enhances reasoning and long-term planning capabilities, allowing robots to operate more flexibly and robustly. Beyond these methods, a key advantage of multi-robot systems is their capacity to integrate distributed information. Through collective sensor fusion, robots can combine complementary environmental perceptions, and distributed reasoning supports context-aware, scenario-specific decisions. These mechanisms significantly improve navigation efficiency, scalability, and fault tolerance, as malfunctioning robots’ tasks can be seamlessly reassigned to others to ensure mission continuity. However, realizing these benefits also introduces challenges, particularly due to computation and communication constraints inherent in distributed multi-robot architectures. Addressing these opportunities and difficulties provides the foundation for developing scalable, resilient, and truly cooperative multi-robot systems.

This Research Topic explores distributed control, coordination, and task allocation for collaborative navigation and path planning in multi-mobile robot systems. Multi-robot collaboration enables collective sensor fusion and distributed reasoning, improving perception, decision-making, and mission continuity by reallocating tasks when failures occur. Yet, these benefits come with challenges from computation and communication constraints. We invite contributions applying AI methods—deep learning, reinforcement learning, and generative AI—for real-time data processing, adaptive decision-making, fault tolerance, and autonomous strategies in localization, mapping, coverage, swarm robotics, and optimization.
• Multi-Robot Systems and Distributed Architectures
• Swarm Robotics and Collective Behaviors
• Path Planning and Navigation in Multi-Robot Environments
• Generative AI for Reasoning and Decision-Making
• Machine Learning and Deep Learning for Multi-Agent Systems
• Optimization Methodologies for Distributed Control
• Multi-Robot Localization, Coverage, and Mapping
• Collective Sensor Fusion and Distributed Reasoning
• Adaptive Decision-Making under Communication and Computation Constraints

This Research Topic welcomes original studies including theoretical modeling, AI-based solution proposals, and application-oriented case studies on coordination, control, and path planning of multi-mobile robots.

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Collaborative Navigation and Path Planning for Multi-Mobile Robots

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