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Decentralized Adaptive Aerospace Transportation of Unknown Loads Using A Team of Robots

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arxiv 2407.08084 v2 pith:H5CK5P36 submitted 2024-07-10 cs.RO

classification cs.RO
keywords robotsaerospacetransportationadaptivecontrollerdecentralizeddifferentgrasping
verification ladder T0 review T1 audit T2 compute T3 formal
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Transportation missions in aerospace are limited to the capability of each aerospace robot and the properties of the target transported object, such as mass, inertia, and grasping locations. We present a novel decentralized adaptive controller design for multiple robots that can be implemented in different kinds of aerospace robots. Our controller adapts to unknown objects in different gravity environments. We validate our method in an aerial scenario using multiple fully actuated hexarotors with grasping capabilities, and a space scenario using a group of space tugs. In both scenarios, the robots transport a payload cooperatively through desired three-dimensional trajectories. We show that our method can adapt to unexpected changes that include the loss of robots during the transportation mission.

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SafeAug: Safety-Critical Driving Data Augmentation from Naturalistic Datasets

    cs.CV 2025-01 reject novelty 4.0 of 10

    A depth-based geometric augmentation pipeline moves the detected front vehicle closer in real KITTI images and rescales acceleration labels, reported to improve downstream emergency-braking prediction.

  2. Maximum Solar Energy Tracking Leverage High-DoF Robotics System with Deep Reinforcement Learning

    cs.RO 2024-11 reject novelty 3.0 of 10

    A 6-DOF robot arm uses a deep Q-network with a solar-objectness loss to track the sun, reporting 81% training and 58% real-world success, but the method and evidence are under-specified.

  3. Optimized Coordination Strategy for Multi-Aerospace Systems in Pick-and-Place Tasks By Deep Neural Network

    cs.RO 2024-12 reject novelty 2.0 of 10

    A deep RL policy for multi-agent space debris pick-and-place claims 16% efficiency gains in simulation, but lacks the details needed to evaluate or reproduce the result.

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