Robust Multiple-Path Orienteering Problem: Securing Against Adversarial Attacks

TL;DR

This paper introduces the Robust Multiple-path Orienteering Problem (RMOP) to enhance multi-robot routing robustness against adversarial attacks, providing approximation algorithms and online solutions with practical simulation results.

Contribution

It proposes a novel RMOP framework with approximation schemes for offline and online scenarios, including a Monte Carlo Tree Search-based online method, addressing robustness in adversarial environments.

Findings

Approximation guarantees depend on the cost function type.

Algorithm achieves constant-factor approximation for modular costs.

Simulation results demonstrate effectiveness in ocean monitoring and tunnel exploration.

Abstract

The multiple-path orienteering problem asks for paths for a team of robots that maximize the total reward collected while satisfying budget constraints on the path length. This problem models many multi-robot routing tasks such as exploring unknown environments and information gathering for environmental monitoring. In this paper, we focus on how to make the robot team robust to failures when operating in adversarial environments. We introduce the Robust Multiple-path Orienteering Problem (RMOP) where we seek worst-case guarantees against an adversary that is capable of attacking at most $\alpha$ robots. We consider two versions of this problem: RMOP offline and RMOP online. In the offline version, there is no communication or replanning when robots execute their plans and our main contribution is a general approximation scheme with a bounded approximation guarantee that depends on $\alpha$ and the approximation factor for single robot orienteering. In particular, we show that the algorithm yields a (i) constant-factor approximation when the cost function is modular; (ii) $\log$ factor approximation when the cost function is submodular; and (iii) constant-factor approximation when the cost function is submodular but the robots are allowed to exceed their path budgets by a bounded amount. In the online version, RMOP is modeled as a two-player sequential game and solved adaptively in a receding horizon fashion based on Monte Carlo Tree Search (MCTS). In addition to theoretical analysis, we perform simulation studies for ocean monitoring and tunnel information-gathering applications to demonstrate the efficacy of our approach.