# AutoTrans: A Complete Planning and Control Framework for Autonomous UAV Payload Transportation

arXiv: https://arxiv.org/abs/2310.15050 · HTML: https://mlsanity.com/paper/2310.15050/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2023.

## Built on by (most cited first, 4 of 4)

- [Towards Dense and Accurate Radar Perception Via Efficient Cross-Modal Diffusion Model](https://mlsanity.com/paper/2403.08460/) (2024)
- [FLARE: Agile Flights for Quadrotor Cable-Suspended Payload System via Reinforcement Learning](https://mlsanity.com/paper/2508.09797/) (2025)
- [Self-Supervised Learning to Fly using Efficient Semantic Segmentation and Metric Depth Estimation for Low-Cost Autonomous UAVs](https://mlsanity.com/paper/2510.16624/) (2025)
- [ASTER: Attitude-aware Suspended-payload Quadrotor Traversal via Efficient Reinforcement Learning](https://mlsanity.com/paper/2603.10715/) (2026)

## Abstract (arXiv)

The robotics community is increasingly interested in autonomous aerial transportation. Unmanned aerial vehicles with suspended payloads have advantages over other systems, including mechanical simplicity and agility, but pose great challenges in planning and control. To realize fully autonomous aerial transportation, this paper presents a systematic solution to address these difficulties. First, we present a real-time planning method that generates smooth trajectories considering the time-varying shape and non-linear dynamics of the system, ensuring whole-body safety and dynamic feasibility. Additionally, an adaptive NMPC with a hierarchical disturbance compensation strategy is designed to overcome unknown external perturbations and inaccurate model parameters. Extensive experiments show that our method is capable of generating high-quality trajectories online, even in highly constrained environments, and tracking aggressive flight trajectories accurately, even under significant uncertainty. We plan to release our code to benefit the community.