Resilient Active Information Acquisition With Teams of Robots

Emerging applications of collaborative autonomy, such as multitarget 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 article, 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 multirobot 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 that 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: multitarget 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.