Capability-aware Task Allocation and Team Formation Analysis for Cooperative Exploration of Complex Environments

TL;DR

This paper develops a Markov decision process-based model for deploying heterogeneous robot teams in complex exploration missions, optimizing team productivity and mission rewards while considering real-world constraints.

Contribution

It introduces a novel operation model for multi-robot exploration that accounts for heterogeneity, constraints, and real-world scenarios like the DARPA SubT Challenge.

Findings

The proposed model effectively balances robot capabilities and mission constraints.

Deployment policies outperform human strategies in simulated scenarios.

The approach provides insights into designing heterogeneous robot teams for complex environments.

Abstract

To achieve autonomy in complex real-world exploration missions, we consider deployment strategies for a team of robots with heterogeneous autonomy capabilities. In this work, we formulate a multi-robot exploration mission and compute an operation policy to maintain robot team productivity and maximize mission rewards. The environment description, robot capability, and mission outcome are modeled as a Markov decision process (MDP). We also include constraints in real-world operation, such as sensor failures, limited communication coverage, and mobility-stressing elements. Then, we study the proposed operation model on a real-world scenario in the context of the DARPA Subterranean (SubT) Challenge. The computed deployment policy is also compared against the human-based operation strategy in the final competition of the SubT Challenge. Finally, using the proposed model, we discuss the design trade-off on building a multi-robot team with heterogeneous capabilities.