Distributed Attack-Robust Submodular Maximization for Multirobot Planning

In this article, we design algorithms to protect swarm-robotics applications against sensor denial-of-service attacks on robots. We focus on applications requiring the robots to jointly select actions, e.g., which trajectory to follow, among a set of available actions. Such applications are central in large-scale robotic applications, such as multirobot motion planning for target tracking. But the current attack-robust algorithms are centralized. In this article, we propose a general-purpose distributed algorithm toward robust optimization at scale, with local communications only. We name it
distributed robust maximization
(
DRM
).
DRM
proposes a divide-and-conquer approach that distributively partitions the problem among cliques of robots. Then, the cliques optimize in parallel, independently of each other. We prove
DRM
achieves a close-to-optimal performance. We demonstrate
DRM
’s performance in Gazebo and MATLAB simulations, in scenarios of
active target tracking with swarms of robots
. In the simulations,
DRM
achieves computational speed-ups, being 1 to 2 orders faster than the centralized algorithms.
Yet
, it nearly matches the tracking performance of the centralized counterparts. Since,
DRM
overestimates the number of attacks in each clique, in this article, we also introduce an
improved distributed robust maximization
(
IDRM
) algorithm.
IDRM
infers the number of attacks in each clique less conservatively than
DRM
by leveraging three-hop neighboring communications. We verify
IDRM
improves
DRM
’s performance in simulations.