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

arXiv: https://arxiv.org/abs/2407.08084 · HTML: https://mlsanity.com/paper/2407.08084/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2024.

## Built on by (most cited first, 6 of 6)

- [A Comprehensive Study on Quantization Techniques for Large Language Models](https://mlsanity.com/paper/2411.02530/) (2024)
- [Optimized Coordination Strategy for Multi-Aerospace Systems in Pick-and-Place Tasks By Deep Neural Network](https://mlsanity.com/paper/2412.09877/) (2024)
- [Visual SLAM with 3D Gaussian Primitives and Depth Priors Enabling Novel View Synthesis](https://mlsanity.com/paper/2408.05635/) (2024)
- [Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models](https://mlsanity.com/paper/2410.19241/) (2024)
- [SafeAug: Safety-Critical Driving Data Augmentation from Naturalistic Datasets](https://mlsanity.com/paper/2501.02143/) (2025)
- [CROPS: A Deployable Crop Management System Over All Possible State Availabilities](https://mlsanity.com/paper/2411.06034/) (2024)

## Abstract (arXiv)

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.