CoCoPlan: Adaptive Coordination and Communication for Multi-Robot Systems in Dynamic and Unknown Environments

2026-01-15

IEEE Robotics and Automation Letters

AI Summary

This work addresses the challenge of efficient coordination in multi-robot systems operating in dynamic, unknown environments under communication constraints and non-stationary spatiotemporal task distributions. The authors propose CoCoPlan, a novel framework that jointly optimizes task planning and intermittent communication in an adaptive manner—a first in the field. By integrating a branch-and-bound architecture to simultaneously encode task allocation and communication events, CoCoPlan employs an adaptive objective function and a dedicated communication-event optimization module to intelligently determine when, where, and how robots should communicate, thereby overcoming the limitations of fixed communication protocols. Experimental results in both 2D office and 3D disaster-response scenarios demonstrate that CoCoPlan scales to up to 100 robots, achieving a 22.4% improvement in task completion rate while reducing communication overhead by 58.6%.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Multiagent Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Multi-robot systems can greatly enhance efficiency through coordination and collaboration, yet in practice, full-time communication is rarely available and interactions are constrained to close-range exchanges. Existing methods either maintain all-time connectivity, rely on fixed schedules, or adopt pairwise protocols, but none adapt effectively to dynamic spatio-temporal task distributions under limited communication, resulting in suboptimal coordination. To address this gap, we propose CoCoPlan, a unified framework that co-optimizes collaborative task planning and team-wise intermittent communication. Our approach integrates a branch-and-bound architecture that jointly encodes task assignments and communication events, an adaptive objective function that balances task efficiency against communication latency, and a communication event optimization module that strategically determines when, where and how the global connectivity should be re-established. Extensive experiments demonstrate that it outperforms state-of-the-art methods by achieving a 22.4% higher task completion rate, reducing communication overhead by 58.6%, and improving the scalability by supporting up to 100 robots in dynamic environments. Hardware experiments include the complex 2D office environment and large-scale 3D disaster-response scenario.

Problem

• multi-robot systems
• intermittent communication
• dynamic environments
• task coordination
• adaptive coordination

Innovation

• adaptive coordination
• intermittent communication
• multi-robot task planning
• branch-and-bound optimization
• communication-aware planning