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.

翻译：具有风险边的图上的团队协调（\textsc{tcgre}）是一个近期提出的问题，在该问题中，机器人为自身寻找通往目标点的路径，同时考虑可能的协调以降低团队整体成本。然而，\textsc{tcgre}假设整个环境对一个具有无处不在通信能力的同构机器人团队是完全可用的。在本文中，我们研究了一个扩展版本的\textsc{tcgre}，称为\textsc{hpr-tcgre}，它包含三项放宽条件：异构机器人、部分可观测性和现实通信约束。为此，我们在\textsc{tcgre}的基础上构建了一个新的组合优化问题。经过分析，我们根据机器人的通信可用性，将其划分为两个子问题：一个用于机器人个体移动，另一个用于机器人组队移动。随后，我们开发了一种算法，该算法利用实时局部地图来解决局部最短路径问题，并采用一种类似A*的子目标分配机制，以探索符合全局利益的潜在协调机会。大量实验表明，即使在引入我们的三项放宽条件下，我们的算法仍能产生团队协调行为，从而降低整体成本。