Multi-robot navigation is increasingly crucial in various domains, including disaster response, autonomous vehicles, and warehouse and manufacturing automation. Robot teams often must operate in highly dynamic environments and under strict bandwidth constraints imposed by communication infrastructure, rendering effective observation sharing within the system a challenging problem. This paper presents a novel optimal communication scheme, Intelligent Knapsack (iKnap), for multi-robot navigation in dynamic environments under bandwidth constraints. We model multi-robot communication as belief propagation in a graph of inferential agents. We then formulate the combinatorial optimization for observation sharing as a 0/1 knapsack problem, where each potential pairwise communication between robots is assigned a decision-making utility to be weighed against its bandwidth cost, and the system has some cumulative bandwidth limit. Compared to state-of-the-art broadcast-based optimal communication schemes, iKnap yields significant improvements in navigation performance with respect to scenario complexity while maintaining a similar runtime. Furthermore, iKnap utilizes allocated bandwidth and observational resources more efficiently than existing approaches, especially in very low-resource and high-uncertainty settings. Based on these results, we claim that the proposed method enables more robust collaboration for multi-robot teams in real-world navigation problems.

翻译：多机器人导航在灾害响应、自动驾驶车辆以及仓储与制造自动化等诸多领域日益重要。机器人团队常需在高度动态的环境中运行，并受限于通信基础设施施加的严格带宽约束，这使得系统内有效的观测共享成为一个具有挑战性的问题。本文提出了一种新颖的最优通信方案——智能背包（iKnap），用于解决带宽约束下动态环境中的多机器人导航问题。我们将多机器人通信建模为推理智能体图上的置信传播过程，进而将观测共享的组合优化问题形式化为0/1背包问题：其中机器人间每个潜在的成对通信都被赋予需与其带宽成本权衡的决策效用值，而系统存在累积带宽限制。与当前基于广播的最优通信方案相比，iKnap在保持相近运行时间的同时，显著提升了导航性能对场景复杂度的适应性。此外，iKnap比现有方法更高效地利用了分配的带宽与观测资源，在极低资源和高不确定性的场景中尤为突出。基于这些结果，我们认为所提方法能够为现实世界导航问题中的多机器人团队实现更鲁棒的协作。