Autonomous Mobile Robot Algorithms and Coordination
Summary
Autonomous mobile robots integrate advanced sensing, control and computation to navigate and perform tasks in unstructured environments without continuous human guidance. Core capabilities include path planning for collision-free movement, obstacle avoidance and Simultaneous Localisation and Mapping (SLAM), which allows a robot to build and update a map of its surroundings while estimating its own pose. When multiple robots operate as a team, coordination challenges emerge in the form of communication constraints, dynamic role assignment and distributed decision-making. Approaches range from centralised planners that allocate trajectories and tasks to each agent, to decentralised consensus algorithms that enable adaptation to local observations. Multi-Agent Path Finding (MAPF) ensures conflict-free routing, formation control maintains prescribed geometric patterns, and rendezvous strategies support collaborative search and target retrieval. Recent progress combines optimisation and learning methods to enhance scalability and robustness. Applications span warehouse logistics, environmental monitoring and search-and-rescue operations, where coordinated swarms offer increased coverage and fault tolerance. As the field evolves, integrating real-time adaptive coordination with high-level planning remains an active research frontier, driven by demands for efficiency in complex, rapidly changing settings.
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Autonomous Mobile Robot Algorithms and Coordination publication trend
The graph below shows the total number of articles in autonomous mobile robot algorithms and coordination across all publications each year (not limited to Nature Index journals).
Technical terms
Simultaneous Localisation and Mapping (SLAM): A process by which a robot incrementally builds a map of an unknown environment while simultaneously estimating its own position within that map.
Multi-Agent Path Finding (MAPF): The problem of computing paths for multiple robots such that each reaches its goal without colliding with others, often under time or distance minimisation objectives.
Decentralised coordination: A coordination paradigm in which robots make local decisions based on neighbour communication or observations, without a central planner.
Formation control: Algorithms designed to maintain or achieve desired geometric patterns or relative positions among a team of robots during motion.
Rendezvous problem: Strategies for guiding multiple agents to meet at a common location or exchange information/resources in an environment with minimal prior knowledge.
References
- Non-Crossing Anonymous MAPF for Tethered Robots. Journal of Artificial Intelligence Research (2023).
- Search-and-rescue rendezvous. European Journal of Operational Research (2022).
- k-Circle Formation and k-epf by Asynchronous Robots. Algorithms (2021).
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