Resilient Active Information Acquisition With Teams of Robots
Brent Schlotfeldt, Vasileios Tzoumas, George J. Pappas · IEEE Transactions on Robotics · 2021
Emerging applications of collaborative autonomy, such asmultitarget tracking,unknown map exploration, andpersistent 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 callResilient Active Information acquisitioN(RAIN).RAINcalls, in an online fashion, arobust trajectory planning(RTP) subroutine that plans attack-robust control inputs over a look-ahead planning horizon. We quantifyRTP’s performance by bounding its suboptimality. We base our theoretical analysis on notions of curvature introduced in combinatorial optimization. We evaluateRAINin three information acquisition scenarios:multitarget tracking,occupancy grid mapping, andpersistent surveillance. The scenarios are simulated in C++ and a unity-based simulator. In all simulations,RAINruns 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 demonstrateRAIN’s robustness and effectiveness against varying models of attacks (worst case and random), as well as varying replanning rates.