VENI: Accurate Control of Aerial Robotic Manipulators under Uncertainties

People

Dr. Sihao Sun - Principal Investigator

Prof. Javier Alonso-Mora

Funding

This project is funded by the Netherlands Organisation for Scientific Research (NWO) Applied Sciences with project Veni 20256

About the Project

Aerial manipulators, often described as “flying hands,” can manipulate objects in midair. This is a key capability for applications such as aerial delivery, nondestructive inspection, and infrastructure maintenance. However, they are subject to significant dynamical uncertainties, which can lead to inaccurate control and unstable operation. This project aims to develop planning and control algorithms that enable aerial manipulators to perform accurate, agile, and safe manipulation tasks despite strong dynamical uncertainties, including wind gusts, unknown contact wrenches, load variations, and even system failures.

In particular, the project will leverage incremental nonlinear flight control and incremental nonlinear modeling techniques to establish a theoretical foundation for integrating incremental nonlinear flight control with various outer-loop control strategies. These include impedance control, model predictive control, and learning-based control, especially for contact-based operations where aerial manipulators experience strong dynamic coupling with manipulated objects and the environment.

Furthermore, the project will establish a comprehensive experimental validation framework to test and verify the proposed algorithms in real-world scenarios. This will be achieved through the development of novel aerial manipulator platforms that balance system complexity and performance in contact-rich aerial manipulation tasks.

Funding & Partners

This project is funded by the Dutch Research Council (NWO).

Project Demonstrations

Decentralized Aerial Manipulation of a Cable-Suspended Load Using Multi-Agent Reinforcement Learning
Jack Zeng, Andreu Matoses Gimenez, Eugene Vinisky, Javier Alonso-Mora, Sihao Sun. In 2025 Conference on Robot Learning (CoRL), 2025.

abstract arxiv video web

This paper presents the first decentralized method to enable real-world 6-DoF manipulation of a cable-suspended load using a team of Micro-Aerial Vehicles (MAVs). Our method leverages multi-agent reinforcement learning (MARL) to train an outer-loop control policy for each MAV. Unlike state-of-the-art controllers that utilize a centralized scheme, our policy does not require global states, inter-MAV communications, nor neighboring MAV information. Instead, agents communicate implicitly through load pose observations alone, which enables high scalability and flexibility. It also significantly reduces computing costs during inference time, enabling onboard deployment of the policy. In addition, we introduce a new action space design for the MAVs using linear acceleration and body rates. This choice, combined with a robust low-level controller, enables reliable sim-to-real transfer despite significant uncertainties caused by cable tension during dynamic 3D motion. We validate our method in various real-world experiments, including full-pose control under load model uncertainties, showing setpoint tracking performance comparable to the state-of-the-art centralized method. We also demonstrate cooperation amongst agents with heterogeneous control policies, and robustness to the complete in-flight loss of one MAV.

Agile and Cooperative Aerial Manipulation of a Cable-Suspended Load
Sihao Sun, Xuerui Wang, Dario Sanalitro, Antonio Franchi, Marco Tognon, Javier Alonso-Mora. In ArXiv, 2025.

abstract arxiv video

Quadrotors can carry slung loads to hard-to-reach locations at high speed. Since a single quadrotor has limited payload capacities, using a team of quadrotors to collaboratively manipulate a heavy object is a scalable and promising solution. However, existing control algorithms for multi-lifting systems only enable low-speed and low-acceleration operations due to the complex dynamic coupling between quadrotors and the load, limiting their use in time-critical missions such as search and rescue. In this work, we present a solution to significantly enhance the agility of cable-suspended multi-lifting systems. Unlike traditional cascaded solutions, we introduce a trajectory-based framework that solves the whole-body kinodynamic motion planning problem online, accounting for the dynamic coupling effects and constraints between the quadrotors and the load. The planned trajectory is provided to the quadrotors as a reference in a receding-horizon fashion and is tracked by an onboard controller that observes and compensates for the cable tension. Real-world experiments demonstrate that our framework can achieve at least eight times greater acceleration than state-of-the-art methods to follow agile trajectories. Our method can even perform complex maneuvers such as flying through narrow passages at high speed. Additionally, it exhibits high robustness against load uncertainties and does not require adding any sensors to the load, demonstrating strong practicality.