Enabling Large-scale Heterogeneous Collaboration with Opportunistic Communications
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Abstract (arXiv)
Multi-robot collaboration in large-scale environments with limited-sized teams and without external infrastructure is challenging, since the software framework required to support complex tasks must be robust to unreliable and intermittent communication links. In this work, we present MOCHA (Multi-robot Opportunistic Communication for Heterogeneous Collaboration), a framework for resilient multi-robot collaboration that enables large-scale exploration in the absence of continuous communications. MOCHA is based on a gossip communication protocol that allows robots to interact opportunistically whenever communication links are available, propagating information on a peer-to-peer basis. We demonstrate the performance of MOCHA through real-world experiments with commercial-off-the-shelf (COTS) communication hardware. We further explore the system's scalability in simulation, evaluating the performance of our approach as the number of robots increases and communication ranges vary. Finally, we demonstrate how MOCHA can be tightly integrated with the planning stack of autonomous robots. We show a communication-aware planning algorithm for a high-altitude aerial robot executing a collaborative task while maximizing the amount of information shared with ground robots. The source code for MOCHA and the high-altitude UAV planning system is available open source: http://github.com/KumarRobotics/MOCHA, http://github.com/KumarRobotics/air_router.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Sep 2023.
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Citing papers per year: 2025: 3; 2026: 1.
4 papers.
- 2025Air-Ground Collaboration for Language-Specified Missions in Unknown Environmentscited by 11
- 2025Deploying Foundation Model-Enabled Air and Ground Robots in the Field: Challenges and Opportunitiescited by 3
- 2025Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligencecited by 3
- 2026UDAV: Uncertainty-Driven Adaptive VLM Waypoint Plannercited by 0
- 2026UDAV: Uncertainty-Driven Adaptive VLM Waypoint Planner14 Sep 2026
- 2025Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence30 Oct 2025
- 2025Deploying Foundation Model-Enabled Air and Ground Robots in the Field: Challenges and Opportunities14 May 2025
- 2025Air-Ground Collaboration for Language-Specified Missions in Unknown Environments14 May 2025