Robust MultiplePath Orienteering Problem: Securing Against Adversarial Attacks
Abstract
The multiplepath 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 multirobot 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 Multiplepath Orienteering Problem (RMOP) where we seek worstcase 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) constantfactor approximation when the cost function is modular; (ii) $\log$ factor approximation when the cost function is submodular; and (iii) constantfactor 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 twoplayer 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 informationgathering applications to demonstrate the efficacy of our approach.
 Publication:

arXiv eprints
 Pub Date:
 March 2020
 DOI:
 10.48550/arXiv.2003.13896
 arXiv:
 arXiv:2003.13896
 Bibcode:
 2020arXiv200313896S
 Keywords:

 Computer Science  Robotics
 EPrint:
 submitted to TRO