让异构机器人在部分可见环境中，通过真实通信协作，降低团队总成本。
Heterogeneous Team Coordination on Partially Observable Graphs with Realistic Communication
- 分两类处理：独立移动与组队协作，依据通信状态动态决策。
- 实验显示算法在三重现实约束下仍能有效降低团队总开销。
- 适合研究多机器人协同、复杂环境导航的科研人员参考。
团队路径协调问题（TCGRE）要求机器人在存在风险边的图上寻找通往目标的路径，同时考虑协调以降低整体团队成本。然而，传统TCGRE假设环境完全已知、机器人同质且通信无处不在。本文提出扩展版本HPR-TCGRE，引入三个更真实的约束：异构机器人、部分可观测性与真实通信。为此，我们在TCGRE基础上构建新的组合优化问题，经分析将其分解为两类子问题：基于通信可用性的个体移动与群体协作。我们设计了一种利用实时局部地图求解局部最短路径的算法，结合类似A*的子目标分配机制，探索全局协调机会。大量实验表明，该算法在三项现实约束下仍能生成有效的团队协调行为，显著降低整体成本。
原文摘要 · Abstract (English)
Team Coordination on Graphs with Risky Edges (\textsc{tcgre}) is a recently proposed problem, in which robots find paths to their goals while considering possible coordination to reduce overall team cost. However, \textsc{tcgre} assumes that the \emph{entire} environment is available to a \emph{homogeneous} robot team with \emph{ubiquitous} communication. In this paper, we study an extended version of \textsc{tcgre}, called \textsc{hpr-tcgre}, with three relaxations: Heterogeneous robots, Partial observability, and Realistic communication. To this end, we form a new combinatorial optimization problem on top of \textsc{tcgre}. After analysis, we divide it into two sub-problems, one for robots moving individually, another for robots in groups, depending on their communication availability. Then, we develop an algorithm that exploits real-time partial maps to solve local shortest path(s) problems, with a A*-like sub-goal(s) assignment mechanism that explores potential coordination opportunities for global interests. Extensive experiments indicate that our algorithm is able to produce team coordination behaviors in order to reduce overall cost even with our three relaxations.
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