多智能体在资源未知环境下，动态规划路径并逐步优化任务达成。
An Iterative Approach for Heterogeneous Multi-Agent Route Planning with Resource Transportation Uncertainty and Temporal Logic Goals
- 通过迭代算法平衡探索与任务执行，适应资源分布不确定性。
- 基于当前信息最大化满足任务目标，随新数据更新策略。
- 适合资源稀缺、任务复杂且智能体能力不同的场景。
本文提出一种针对资源分布未知环境的异构多智能体路径规划迭代方法。研究聚焦于具备不同能力的机器人团队，需执行由能力时序逻辑（CaTL）定义的任务，该形式化框架可处理空间、时间、能力及资源约束。核心挑战在于环境初始资源分布与数量的不确定性。为此，我们设计了一种迭代算法，动态平衡探索与任务完成。机器人在探索中识别资源位置与数量，逐步完善对资源环境的认知；同时依据现有信息尽可能满足任务目标，随新数据不断调整策略。该方法在动态、资源受限环境中表现出强鲁棒性，有效实现异构团队的协调。通过模拟案例验证了方法的有效性与性能。
原文摘要 · Abstract (English)
This paper presents an iterative approach for heterogeneous multi-agent route planning in environments with unknown resource distributions. We focus on a team of robots with diverse capabilities tasked with executing missions specified using Capability Temporal Logic (CaTL), a formal framework built on Signal Temporal Logic to handle spatial, temporal, capability, and resource constraints. The key challenge arises from the uncertainty in the initial distribution and quantity of resources in the environment. To address this, we introduce an iterative algorithm that dynamically balances exploration and task fulfillment. Robots are guided to explore the environment, identifying resource locations and quantities while progressively refining their understanding of the resource landscape. At the same time, they aim to maximally satisfy the mission objectives based on the current information, adapting their strategies as new data is uncovered. This approach provides a robust solution for planning in dynamic, resource-constrained environments, enabling efficient coordination of heterogeneous teams even under conditions of uncertainty. Our method's effectiveness and performance are demonstrated through simulated case studies.
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