# Robust Collaborative Object Transportation Using Multiple MAVs

arXiv: https://arxiv.org/abs/1711.08753 · HTML: https://mlsanity.com/paper/1711.08753/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2017.

## Built on by (most cited first, 2 of 2)

- [Efficient Deep Learning of Robust, Adaptive Policies using Tube MPC-Guided Data Augmentation](https://mlsanity.com/paper/2303.15688/) (2023)
- [Aerobatic maneuvers in insect-scale flapping-wing aerial robots via deep-learned robust tube model predictive control](https://mlsanity.com/paper/2508.03043/) (2025)

## Abstract (arXiv)

Collaborative object transportation using multiple Micro Aerial Vehicles (MAVs) with limited communication is a challenging problem. In this paper we address the problem of multiple MAVs mechanically coupled to a bulky object for transportation purposes without explicit communication between agents. The apparent physical properties of each agent are reshaped to achieve robustly stable transportation. Parametric uncertainties and unmodeled dynamics of each agent are quantified and techniques from robust control theory are employed to choose the physical parameters of each agent to guarantee stability. Extensive simulation analysis and experimental results show that the proposed method guarantees stability in worst case scenarios.