发表机构
University of Southern California（南加州大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对异构多机器人团队在负载感知信息路径规划中的协调难题，提出MA-LIPP框架，通过异步“秘密投放”实现协作，结合精确MIQP与可扩展LNS启发式算法，在95.5%案例中达到最优并显著降低不确定性。
AI 中文摘要
野外机器人任务通常需要将物理样本送回实验室进行分析，这使得路径规划本质上具有负载感知性和顺序依赖性，因为累积的样本会增加有效载荷和行进能量成本。在单机器人负载感知信息路径规划（LIPP）中，感知与运输被刚性耦合：单个机器人必须运输每个收集的样本，迫使频繁返回基地，从而严重限制其空间覆盖范围。异构多机器人团队可以通过分工来克服这一瓶颈——使高精度采样器负责收集，而高容量运输器负责运输。然而，这引入了在LIPP问题之上关于交接何时、何地、交接什么以及交接给谁的复杂协调挑战。为解决这一紧密耦合的问题，我们提出了多智能体LIPP（MA-LIPP），它使团队能够通过异步“秘密投放”进行合作，允许一个机器人存放样本供另一个机器人稍后取回，而无需同步会合。我们将MA-LIPP表述为一个精确的混合整数二次规划（MIQP），并配以可扩展的成对大邻域搜索（LNS）启发式算法，用于复杂的现实应用。该启发式算法在95.5%的认证案例中匹配精确最优解，并在多达12个机器人的较大实例上，相对于顺序基线将加权后验方差降低了16.1%–19.8%，为协作物理采样任务提供了稳健的框架。
英文摘要
Field robotics missions often require physical samples to be returned to laboratories for analysis, making path planning inherently load-aware and order-dependent as accumulated samples increase payload and traversal energy costs. In single-robot Load-Aware Informative Path Planning (LIPP), this rigidly couples sensing with hauling: a solitary robot must transport every collected sample, forcing frequent depot returns that severely restrict its spatial coverage. Heterogeneous multi-robot teams can overcome this bottleneck by dividing labor---enabling high-precision samplers to collect while high-capacity carriers handle transport. However, this introduces a complex coordination challenge regarding when, where, what, and to whom handoffs should occur on top of the LIPP problem. To address this tightly coupled problem, we introduce Multi-Agent LIPP (MA-LIPP), which enables teams to cooperate through asynchronous "dead drops," allowing one robot to deposit samples for another to retrieve later without requiring synchronous rendezvous. We formulate MA-LIPP as an exact Mixed-Integer Quadratic Program (MIQP) alongside a scalable Pairwise Large-Neighborhood Search (LNS) heuristic for complex real-world applications. The heuristic matches exact optima in $95.5\%$ of certified cases and reduces weighted posterior variance by $16.1$--$19.8\%$ relative to a sequential baseline on larger instances of up to 12 robots, providing a robust framework for cooperative physical-sampling missions.
