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• W3089748765 abstract "We aim to guard swarm-robotics applications against denial-of-service (DoS) attacks that result in withdrawals of robots. We focus on applications requiring the selection of actions for each robot, among a set of available ones, e.g., which trajectory to follow. Such applications are central in large-scale robotic applications, e.g., multi-robot motion planning for target tracking. But the current attack-robust algorithms are centralized, and scale quadratically with the problem size (e.g., number of robots). In this paper, we propose a general-purpose distributed algorithm towards 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 K cliques of robots. The cliques optimize in parallel, independently of each other. That way, DRM also offers computational speed-ups up to 1/K <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> the running time of its centralized counterparts. K depends on the robots' communication range, which is given as input to DRM. DRM also achieves a close-to-optimal performance. We demonstrate DRM's performance in Gazebo and MATLAB simulations, in scenarios of active target tracking with multiple robots. We observe DRM achieves significant computational speed-ups (it is 3 to 4 orders faster) and, yet, nearly matches the tracking performance of its centralized counterparts." @default.
• W3089748765 created "2020-10-08" @default.
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• W3089748765 date "2020-05-01" @default.
• W3089748765 modified "2023-09-23" @default.
• W3089748765 title "Distributed Attack-Robust Submodular Maximization for Multi-Robot Planning" @default.
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• W3089748765 doi "https://doi.org/10.1109/icra40945.2020.9197243" @default.
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