100架无人机在不确定条件下高效分配任务，通信受限时仍保持高完成率。
Dynamic Multi-Robot Task Allocation under Uncertainty and Communication Constraints: A Game-Theoretic Approach
- 基于局部福利最大化选择任务，实现去中心化动态分配
- 相比基线方法，任务完成率相当但计算更快
- 适合大规模无人机配送等分布式协作场景
我们研究在任务完成不确定性、时间窗约束及信息不完全条件下的动态多机器人任务分配问题。任务在有限时间段内在线到达，必须在规定截止时间内完成，而各智能体从分布式的枢纽点出发，感知和通信能力受限。通过基于枢纽的感知区域定义任务可见性，以及通信图控制枢纽间信息交换，建模不完全信息。在此框架下，提出迭代最优响应（IBR）算法：每个智能体选择对局部观测到的整体福利贡献最大的任务。在包含最多100架无人机的城市级快递配送场景中，对比最早截止日期优先（EDD）、匈牙利算法和随机冲突基础分配（SCoBA）三种基线方法，在全通信和稀疏通信条件下，IBR实现了具有竞争力的任务完成率，且计算时间更短。
原文摘要 · Abstract (English)
We study dynamic multi-robot task allocation under uncertain task completion, time-window constraints, and incomplete information. Tasks arrive online over a finite horizon and must be completed within specified deadlines, while agents operate from distributed hubs with limited sensing and communication. We model incomplete information through hub-based sensing regions that determine task visibility and a communication graph that governs inter-hub information exchange. Using this framework, we propose Iterative Best Response (IBR), a decentralized policy in which each agent selects the task that maximizes its marginal contribution to the locally observed welfare. We compare IBR against three baselines: Earliest Due Date first (EDD), Hungarian algorithm, and Stochastic Conflict-Based Allocation (SCoBA), on a city-scale package-delivery domain with up to 100 drones and varying task arrival scenarios. Under full and sparse communication, IBR achieves competitive task-completion performance with lower computation time.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。