Heterogeneous Team Coordination on Partially Observable Graphs with Realistic Communication

TL;DR

This paper extends the Team Coordination on Graphs with Risky Edges problem to heterogeneous robots with partial observability and realistic communication, proposing a new algorithm that effectively coordinates teams under these constraints.

Contribution

It introduces the HPR-TCGRE problem, analyzes it into sub-problems, and develops an A*-like algorithm that exploits partial maps for effective team coordination.

Findings

The algorithm reduces overall team cost under relaxed conditions.

Effective coordination achieved despite partial observability and communication limits.

Experimental results demonstrate the algorithm's robustness and efficiency.

Abstract

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.