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

TL;DR

CoCoPlan addresses the challenge of coordinating multiple robots under limited communication and unknown dynamic task distributions by integrating an online branch-and-bound planner with joint task- and communication-scheduling, an adaptive objective that balances task throughput and latency, and an iterative communication-optimization module to guarantee global connectivity. The approach is validated through large-scale simulations and hardware experiments, showing significant gains in task completion rate and reductions in communication overhead, while scaling to fleets of up to 100 robots. Key contributions include a general online co-design framework, an adaptive coordination mechanism with theoretical guarantees, and comprehensive validation across 2D and 3D environments, including real-world office and disaster-response scenarios. This work advances practical, robust multi-robot coordination by enabling dynamic replanning and strategic connectivity under realistic communication constraints, with potential impact on autonomous exploration, search-and-rescue, and large-scale service robotics.

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.

Figures (9)

• Figure 1: Hardware demonstration of the proposed adaptive coordination and communication scheme ($r_i$ denotes an individual hardware agent $i$, $\forall i \in \mathcal{N}$): $4$ UGVs to fulfil office errands such as delivery and cleaning as requested online (top); $2$ UAVs and $4$ UGVs to detect fire hazards, search and rescue victims during a disaster-response mission (bottom).
• Figure 2: Overview of the CoCoPlan framework, which integrates real-time task detection, a branch-and-bound planner for joint task and communication scheduling, and an adaptation scheme to unknown spatio-temporal task distributions.
• Figure 3: (Left): Ad-hoc local communication devices used in the hardware experiments; (Right): Map of the predicted communication quality used in the coordination of communication events.
• Figure 4: Illustration of the iterative process for computing lower bounds, after inserting new tasks in the collective plan.
• Figure 5: Optimization of communication events, showing the selection of the earliest-arriving agent and the sampling of candidate communication points to achieve synchronized connectivity.

Theorems & Definitions (4)

• Lemma 1
• proof
• Theorem 2
• proof