Agile and Cooperative Aerial Manipulation of a Cable-Suspended Load

2025-01-30

AI Summary

To address poor maneuverability and slow response in multi-quadrotor cooperative payload transportation, this paper proposes a trajectory-based online whole-system motion planning and control framework. We unify the coupled rotor-cable-payload dynamics and physical constraints—departing from conventional cascaded control architectures—and integrate online kinodynamic planning, model predictive control (MPC), tension-observation-based compensation, and recursive time-domain tracking. Crucially, the framework operates without payload-mounted sensors, significantly enhancing robustness against payload uncertainties. Experimental results demonstrate that the system achieves peak accelerations over eight times higher than state-of-the-art methods and successfully executes high-agility tasks such as high-speed narrow-gap traversal. These outcomes validate the framework’s practicality and technical advancement in time-critical applications, including emergency rescue operations.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Multi-Quadrotor
• Collaborative Transport
• Emergency Response

Innovation

• Path-based Control System
• Quadrotor Collaboration
• Adaptive Load Handling