Multi-robot systems can do more than single robots through coordination and collaboration. Planning for teams introduces challenges in scalability, as the joint configuration space grows exponentially with the number of robots, and in handling interactions between robots that must share workspace, coordinate actions, or physically collaborate on tasks.
Effective multi-robot planning requires reasoning about dependencies between robots’ motions and managing computational complexity. Applications include warehouse automation with fleets of mobile robots and collaborative manipulation where multiple arms transport objects together, requiring task-level coordination.
Multi-robot motion planning for high degree-of-freedom manipulators in shared, constrained, and narrow spaces is a complex problem and essential for many scenarios such as construction, surgery, and more. Traditional coupled and decoupled methods either scale poorly or lack completeness, and hybrid methods that compose paths from individual robots together require the enumeration of many paths before they can find valid composite solutions. This paper introduces Scheduling to Avoid Collisions (StAC), a hybrid approach that more effectively composes paths from individual robots by scheduling (adding random stops and coordination motion along each path) and generates paths that are more likely to be feasible by using bidirectional feedback between the scheduler and motion planner for informed sampling. StAC uses 10 to 100 times fewer paths from the low-level planner than state-of-the-art baselines on challenging problems in manipulator cases.
@article{guo2026stac,title={Efficient Multi-Robot Motion Planning for Manifold-Constrained Manipulators by Randomized Scheduling and Informed Path Generation},author={Guo, Weihang and Kingston, Zachary and Hang, Kaiyu and Kavraki, Lydia E.},journal={IEEE Robotics and Automation Letters},year={2026},note={To Appear},}
2025
OCEANS
Underwater Multi-Robot Simulation and Motion Planning in Angler
Deploying multi-robot systems in underwater environments is expensive and lengthy; testing algorithms and software in simulation improves development by decoupling software and hardware. However, this requires a simulation framework that closely resembles the real-world. Angler is an open-source framework that simulates low-level communication protocols for an onboard autopilot, such as ArduSub, providing a framework that is close to reality, but unfortunately lacking support for simulating multiple robots. We present an extension to Angler that supports multi-robot simulation and motion planning. Our extension has a modular architecture that creates non-conflicting communication channels between Gazebo, ArduSub Software-in-the-Loop (SITL), and MAVROS to operate multiple robots simultaneously in the same environment. Our multi-robot motion planning module interfaces with cascaded controllers via a JointTrajectory controller in ROS 2. We also provide an integration with the Open Motion Planning Library (OMPL), a collision avoidance module, and tools for procedural environment generation. Our work enables the development and benchmarking of underwater multi-robot motion planning in dynamic environments.
@inproceedings{agrawal2025mrangler,title={Underwater Multi-Robot Simulation and Motion Planning in Angler},author={Agrawal, Akshaya and Palmer, Evan and Kingston, Zachary and Hollinger, Geoffrey A.},booktitle={IEEE/MTS OCEANS Conference},pages={1--6},year={2025},doi={10.1109/OCEANS58557.2025.11104649},address={Brest, France},}
Cooperative manipulation tasks impose various structure-, task-, and robot-specific constraints on mobile manipulators. However, current methods struggle to model and solve these myriad constraints simultaneously. We propose a twofold solution: first, we model constraints as a family of manifolds amenable to simultaneous solving. Second, we introduce the constrained nonlinear Kaczmarz (cNKZ) projection technique to produce constraint-satisfying solutions. Experiments show that cNKZ dramatically outperforms baseline approaches, which cannot find solutions at all. We integrate cNKZ with a sampling-based motion planning algorithm to generate complex, coordinated motions for 3 to 6 mobile manipulators (18–36 DoF), with cNKZ solving up to 80 nonlinear constraints simultaneously and achieving up to a 92% success rate in cluttered environments. We also demonstrate our approach on hardware using three Turtlebot3 Waffle Pi robots with OpenMANIPULATOR-X arms.
@inproceedings{agrawal2025cnkz,title={Constrained Nonlinear {Kaczmarz} Projection on Intersections of Manifolds for Coordinated Multi-Robot Mobile Manipulation},author={Agrawal, Akshaya and Mayer, Parker and Kingston, Zachary and Hollinger, Geoffrey A.},booktitle={IEEE International Conference on Robotics and Automation},pages={7726--7732},year={2025},doi={10.1109/ICRA55743.2025.11127991},}
2018
Distributed Object Characterization with Local Sensing by a Multi-Robot System
This paper presents two distributed algorithms for enabling a swarm of robots with local sensing and local coordinates to estimate the dimensions and orientation of an unknown complex polygonal object, ie, its minimum and maximum width and its main axis. Our first approach is based on a robust heuristic of distributed Principal Component Analysis (DPCA), while the second is based on turning the idea of Rotating Calipers into a distributed algorithm (DRC). We simulate DRC and DPCA methods and test DPCA on real robots. The result show our algorithms successfully estimate the dimension and orientation of convex or concave objects with a reasonable error in the presence of noisy data.
@incollection{habibi2018dars,title={Distributed Object Characterization with Local Sensing by a Multi-Robot System},author={Habibi, Golnaz and Fekete, S{\'a}ndor P. and Kingston, Zachary and McLurkin, James},booktitle={Distributed Autonomous Robotic Systems},editor={Gro{\ss}, Roderich and Kolling, Andreas and Berman, Spring and Frazzoli, Emilio and Martinoli, Alcherio and Matsuno, Fumitoshi and Gauci, Melvin},volume={6},pages={205--218},year={2018},doi={10.1007/978-3-319-73008-0_15},publisher={Springer Proceedings in Advanced Robotics},}
2015
AAMAS
Pipelined Consensus for Global State Estimation in Multi-Agent Systems
This paper presents pipelined consensus, an extension of pair-wise gossip-based consensus, for multi-agent systems using mesh networks. Each agent starts a new consensus in each round of gossiping, and stores the intermediate results for the previous k consensus in a pipeline message. After k rounds of gossiping, the results of the first consensus are ready. The pipeline keeps each consensus independent, so any errors only persist for k rounds. This makes pipelined consensus robust to many real-world problems that other algorithms cannot handle, including message loss, changes in network topology, sensor variance, and changes in agent population. The algorithm is fully distributed and self-stabilizing, and uses a communication message of fixed size. We demonstrate the efficiency of pipelined consensus in two scenarios: computing mean sensor values in a distributed sensor network, and computing a centroid estimate in a multi-robot system. We provide extensive simulation results, and real-world experiments with up to 24 agents. The algorithm produces accurate results, and handles all of the disturbances mentioned above.
@incollection{habibi2015aamas,title={Pipelined Consensus for Global State Estimation in Multi-Agent Systems},author={Habibi, Golnaz and Kingston, Zachary and Wang, Zijian and Schwager, Mac and McLurkin, James},booktitle={Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems},pages={1315--1323},year={2015},doi={10.5555/2772879.2773320},isbn={9781450334136},publisher={International Foundation for Autonomous Agents and Multiagent Systems},}
This paper presents four distributed motion controllers to enable a group of robots to collectively transport an object towards a guide robot. These controllers include: rotation around a pivot robot, rotation in-place around an estimated centroid of the object, translation, and a combined motion of rotation and translation in which each manipulating robot follows a trochoid path. Three of these controllers require an estimate of the centroid of the object, to use as the axis of rotation. Assuming the object is surrounded by manipulator robots, we approximate the centroid of the object by measuring the centroid of the manipulating robots. Our algorithms and controllers are fully distributed and robust to changes in network topology, robot population, and sensor error. We tested all of the algorithms in real-world environments with 9 robots, and show that the error of the centroid estimation is low, and that all four controllers produce reliable motion of the object.
@inproceedings{habibi2015icra,title={Distributed Centroid Estimation and Motion Controllers for Collective Transport by Multi-Robot Systems},author={Habibi, Golnaz and Kingston, Zachary and Xie, William and Jellins, Mathew and McLurkin, James},booktitle={IEEE International Conference on Robotics and Automation},pages={1282--1288},year={2015},doi={10.1109/ICRA.2015.7139356},}
