Resilient Active Information Acquisition with Teams of Robots

TL;DR

This paper introduces RAIN, a novel receding horizon algorithm for multi-robot information acquisition that is robust against sensor attacks, demonstrating superior performance in various simulated scenarios.

Contribution

The paper presents the first attack-robust multi-robot planning algorithm, RAIN, with theoretical performance bounds and real-time implementation for adversarial environments.

Findings

RAIN outperforms state-of-the-art algorithms in simulated scenarios.

RAIN maintains robustness against different attack models and replanning rates.

Theoretical bounds on RTP's suboptimality are established.

Abstract

Emerging applications of collaborative autonomy, such as Multi-Target Tracking, Unknown Map Exploration, and Persistent Surveillance, require robots plan paths to navigate an environment while maximizing the information collected via on-board sensors. In this paper, we consider such information acquisition tasks but in adversarial environments, where attacks may temporarily disable the robots' sensors. We propose the first receding horizon algorithm, aiming for robust and adaptive multi-robot planning against any number of attacks, which we call Resilient Active Information acquisitioN (RAIN). RAIN calls, in an online fashion, a Robust Trajectory Planning (RTP) subroutine which plans attack-robust control inputs over a look-ahead planning horizon. We quantify RTP's performance by bounding its suboptimality. We base our theoretical analysis on notions of curvature introduced in combinatorial optimization. We evaluate RAIN in three information acquisition scenarios: Multi-Target Tracking, Occupancy Grid Mapping, and Persistent Surveillance. The scenarios are simulated in C++ and a Unity-based simulator. In all simulations, RAIN runs in real-time, and exhibits superior performance against a state-of-the-art baseline information acquisition algorithm, even in the presence of a high number of attacks. We also demonstrate RAIN's robustness and effectiveness against varying models of attacks (worst-case and random), as well as, varying replanning rates.