Multi-Agent Systems
We showcase a range of projects in multi-agent systems, spanning from fundamental motion-planning methods for coordinating teams of robots to more involved challenges such as task allocation and integrated task-and-motion planning. Our work explores how agents can collaborate to complete shared objectives, navigate dynamic environments, and even influence the behavior or movement of other groups of agents. Several of these projects draw on a unified modeling approach that combines multi-agent simulation with roadmap-based planning, enabling us to address a broad spectrum of multi-agent coordination problems.
ARC: Adaptive Robot Coordination
A hybrid framework for multi-robot motion planning that uses local subproblems to resolve inter-robot conflicts, dynamically adjusting between coupled and decoupled planning to deliver efficient, probabilistically complete solutions.
CBS Extensions to MR-TAMP
Hybrid Conflict-Based Search extensions, including CBS-MP and TMP-CBS, that scale multi-robot motion planning and task allocation to continuous, high-DOF, heterogeneous robot teams with complex inter-task dependencies.
DaSH: Decomposable State Space Hypergraph
A hypergraph-based multi-robot task and motion planning algorithm that uses a decomposed state space representation, scaling linearly rather than exponentially with the number of robots and objects.
Multi-Agent Workspace Guidance
Extends topological skeleton guidance to multi-robot motion planning via CDR-RRT and Workspace-DaSH, leveraging workspace structure to efficiently coordinate teams of robots through narrow passages.
Multi-Agent Path Finding
Hierarchical Composition Conflict-Based Search (HC-CBS) and its parallel extensions PHC-CBS and DPHC-CBS, which exploit the embarrassingly parallel structure of CBS to scale multi-agent pathfinding via multithreading.
Metamorphic Robots
Decentralized algorithms for controlling collections of identical, independently controlled hexagonal modules that connect, disconnect, and reconfigure to navigate around irregular obstacles in dangerous environments.
RRVO: Reciprocally-Rotating Velocity Obstacles
An extension to ORCA that allows polygonal agents to actively rotate to avoid collisions, overcoming the deadlocking problem that arises when large agents with opposing goals meet in dense multi-agent simulations.
PRISM: Distributed Multi-Task Pathfinding
Pathfinding with Rapid Information Sharing using Motion Constraints — a decentralized MT-MAPF algorithm that uses information packets to share motion constraints, provably resolving deadlocks and scaling to large agent teams.
Robot Interaction
The Interaction Template Method, a scalable motion-planning-first approach for multi-robot task and motion planning that embeds task semantics directly into a combined roadmap to coordinate heterogeneous robot teams.
CIPHER: Scalable Multi-Robot Motion Planning
Coordinated Incremental Planning with Hierarchical Expansion and Refinement — introduces resolution guidance that reasons over workspace representations at different resolutions to scale multi-robot motion planning.
Publications
- Ngui, I. , McBeth, C. , Motes, J.D. , Morales, M. , & Amato, N.M. (2026). Scalable Multi-robot Motion Planning via Hierarchical Subproblem Expansion and Workspace Decomposition Refinement. arXiv
- Qin, M. , Solis, I. , Motes, J. , Morales, M. , & Amato, N.M. (2025). K-ARC: Adaptive Robot Coordination for Multi-Robot Kinodynamic Planning. ArXiv. https://doi.org/https://doi.org/10.48550/arXiv.2501.01559
- Solis, I. (2024). Hybrid Multi-Robot Motion Planning. Doctoral dissertation. https://doi.org/NA
- Solis, I. , Motes, J. , Qin, M. , Morales, M. , & Amato, N.M. (2024). Experience-based Subproblem Planning for Multi-Robot Motion Planning. ArXiv. https://doi.org/https://doi.org/10.48550/arXiv.2411.08851
- Lee, S. , Motes, J. , Ngui, I. , Morales, M. , & Amato, N.M. (2024). Lazy-DaSH: Lazy Approach for Hypergraph-based Multi-robot Task and Motion Planning. ICRA@40. https://doi.org/Unpublished
- Solis, I. , Motes, J. , Qin, M. , Morales, M. , & Amato, N.M. (2024). Adaptive Robot Coordination: A Subproblem-based Approach for Hybrid Multi-Robot Motion Planning. IEEE Robotics and Automation Letters , 9(8) , 7238-7245. https://doi.org/10.1109/LRA.2024.3420548
- McBeth, C. , Motes, J. , Ngui, I. , Morales, M. , & Amato, N.M. (2024). Scalable Multi-Robot Motion Planning Using Guidance-Informed Hypergraphs. ArXiv Preprint. arXiv
- Ngui, I. , Lee, S. , Motes, J. , Morales, M. , & Amato, N.M. (2024). A Hierarchical Approach to Workstation-based Task Allocation and Motion Planning. IROS 2023. https://doi.org/Unpublished
- Motes, J. , Chen, T. , Bretl, T. , Morales, M. , & Amato, N.M. (2023). Hypergraph-Based Multi-robot Task and Motion Planning. IEEE Transactions on Robotics (TRO) , 6(4) , 7001--7008. https://doi.org/10.1109/TRO.2023.3297011
- McBeth, C. , Motes, J. , Uwacu, D. , Morales, M. , & Amato, N.M. (2023). Scalable Multi-robot Motion Planning for Congested Environments With Topological Guidance. In IEEE Robotics and Automation Letters , 1-8. https://doi.org/10.1109/LRA.2023.3312980
- Lee, H. , Motes, J. , Morales, M. , & Amato, N.M. (2021). Parallel Hierarchical Composition Conflict-Based Search. IEEE/RSJ International Conference on Intelligent Robots and Systems , 6(4) , 7001--7008. https://doi.org/10.1109/LRA.2021.3096476.
- Solis, I. , Motes, J. , Sandström, R. , & Amato, N.M. (2021). Representation-Optimal Multi-Robot Motion Planning using Conflict-Based Search. IEEE Robotics and Automation Letters. https://doi.org/https://doi.org/10.1109/LRA.2021.3068910
- Motes, J. , Sandstrom, R. , Lee, H. , Thomas, S. , & Amato, N.M. (2020). Multi-Robot Task and Motion Planning with Subtask Dependencies. IEEE Robotics and Automation Letters (RA-L) , 5(2) , 3338--3345. https://doi.org/10.1109/LRA.2020.2976329
- Motes, J. , Sandstrom, R. , Adams, W. , Ogunyale, T. , Thomas, S. , & Amato, N.M. (2019). Interaction Templates for Multi-Robot Systems. IEEE Robotics and Automation Letters , 4(3) , 2926--2933. https://doi.org/10.1109/LRA.2019.2923386
- Giese, A. , Latypov, D. , & Amato, N.M. (2014). Reciprocally-Rotating Velocity Obstacles. 2014 IEEE International Conference on Robotics and Automation (ICRA) , 3234-3241. https://doi.org/10.1109/ICRA.2014.6907324
- Rodriguez, S. , Denny, J. , Burgos, J. , Mahadevan, A. , Manavi, K. , Murray, L. , Kodochygov, A. , Zourntos, T. , & Amato, A.N.M. (2011). Toward Realistic Pursuit-Evasion Using a Roadmap-Based Approach. IEEE International Conference on Robotics and Automation , 1738--1745. https://doi.org/10.1109/ICRA.2011.5980467
- Rodriguez, S. & Amato, N.M. (2010). Behavior-Based Evacuation Planning. IEEE International Conference on Robotics and Automation , 350--355. https://doi.org/10.1109/ROBOT.2010.5509502
- Bayazıt, O.B. , Lien, J. , & Amato, N.M. (2005). Swarming Behavior Using Probabilistic Roadmap Techniques. Lecture Notes in Computer Science , 3342 , 112--125. https://doi.org/10.1007/978-3-540-30552-1_10
- Walter, J.E. , Brooks, M.E. , Little, D.F. , & Amato, N.M. (2004). Enveloping multi-pocket obstacles with hexagonal metamorphic robots. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA) , 3 , 2204-2209 Vol.3. https://doi.org/10.1109/ROBOT.2004.1307389
- Lien, J. , Bayazit, O.B. , Sowell, R.T. , Rodriguez, S. , & Amato, N.M. (2004). Shepherding Behaviors. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA) , 4 , 4159-4164 Vol.4. https://doi.org/10.1109/ROBOT.2004.1308924
- Walter, J.E. , Brooks, M.E. , Little, D.F. , & Amato, N.M. (2003). Enveloping Obstacles with Hexagonal Metamorphic Robots. Proc. IEEE Int. Conf. Robot. Autom. (ICRA) , 1 , 741-748 vol.1. https://doi.org/10.1109/ROBOT.2003.1241682
- Bayazit, O.B. , Lien, J. , & Amato, N.M. (2002). Better Group Behaviors Using Rule-Based Roadmaps. In Proc. Int. Wkshp. on Alg. Found. of Rob. (WAFR) , 7 , 95-111. https://doi.org/10.1007/978-3-540-45058-0_7