Stochastic Assignment for Deploying Multiple Marsupial Robots

TL;DR

This paper introduces a heuristic algorithm combining Monte Carlo Tree Search and dynamic programming to optimize the deployment of multiple marsupial robots in complex, uncertain environments, improving exploration efficiency.

Contribution

It presents a novel centralized heuristic search method for multi-carrier robot deployment, addressing the complex dependencies and conflicts inherent in such systems.

Findings

The approach outperforms alternative algorithms in exploration tasks.

It is effective on both procedurally-generated and real-world DARPA data.

The method demonstrates substantial improvements in exploration performance.

Abstract

Marsupial robot teams consist of carrier robots that transport and deploy multiple passenger robots, such as a team of ground robots that carry and deploy multiple aerial robots, to rapidly explore complex environments. We specifically address the problem of planning the deployment times and locations of the carrier robots to best meet the objectives of a mission while reasoning over uncertain future observations and rewards. While prior work proposed optimal, polynomial-time solutions to single-carrier robot systems, the multiple-carrier robot deployment problem is fundamentally harder as it requires addressing conflicts and dependencies between deployments of multiple passenger robots. We propose a centralized heuristic search algorithm for the multiple-carrier robot deployment problem that combines Monte Carlo Tree Search with a dynamic programming-based solution to the Sequential Stochastic Assignment Problem as a rollout action-selection policy. Our results with both procedurally-generated data and data drawn from the DARPA Subterranean Challenge Urban Circuit show the viability of our approach and substantial exploration performance improvements over alternative algorithms.