This paper proposes a novel planning framework to handle a multi-agent pathfinding problem under team-connected communication constraint, where all agents must have a connected communication channel to the rest of the team during their entire movements. Standard multi-agent path finding approaches (e.g., priority-based search) have potential in this domain but fail when neighboring configurations at start and goal differ. Their single-expansion approach -- computing each agent's path from the start to the goal in just a single expansion -- cannot reliably handle planning under communication constraints for agents as their neighbors change during navigating. Similarly, leader-follower approaches (e.g., platooning) are effective at maintaining team communication, but fixing the leader at the outset of planning can cause planning to become stuck in dense-clutter environments, limiting their practical utility. To overcome this limitation, we propose a novel two-level multi-agent pathfinding framework that integrates two techniques: adaptive path expansion to expand agent paths to their goals in multiple stages; and dynamic leading technique that enables the reselection of the leading agent during each agent path expansion whenever progress cannot be made. Simulation experiments show the efficiency of our planners, which can handle up to 25 agents across five environment types under a limited communication range constraint and up to 11-12 agents on three environment types under line-of-sight communication constraint, exceeding 90% success-rate where baselines routinely fail.

翻译：本文提出一种新颖的规划框架，用于处理团队连通通信约束下的多智能体路径规划问题，该约束要求所有智能体在整个移动过程中必须与团队其他成员保持连通的通信信道。标准多智能体路径规划方法（如基于优先级的搜索）在该领域具有潜力，但当起始与目标位置的相邻构型存在差异时会失效。其单次扩展方法——仅通过单次扩展计算每个智能体从起点到目标的路径——无法可靠处理通信约束下的规划问题，因为智能体在导航过程中其相邻关系会动态变化。类似地，领导者-跟随者方法（如队列行进）能有效维持团队通信，但在规划初始阶段固定领导者可能导致规划在密集杂乱环境中陷入停滞，限制了其实用性。为克服这一局限，我们提出一种新颖的双层多智能体路径规划框架，该框架整合了两种技术：自适应路径扩展技术，将智能体路径分多阶段扩展至目标；以及动态引导技术，在每次智能体路径扩展无法取得进展时，允许重新选择引导智能体。仿真实验表明，我们的规划器在有限通信距离约束下可处理五种环境类型中最多25个智能体，在视距通信约束下可处理三种环境类型中最多11-12个智能体，在基线方法普遍失效的场景中成功率超过90%。