Learning-Based Geometric Leader-Follower Control for Cooperative Rigid-Payload Transport with Aerial Manipulators

TL;DR

This paper introduces a learning-based control framework for cooperative aerial manipulation of a rigid payload, integrating geometric modeling, disturbance compensation, and probabilistic error bounds to enhance tracking accuracy.

Contribution

It develops a unified geometric model and a leader-follower control scheme that incorporates Gaussian Process learning for disturbance rejection in aerial payload transport.

Findings

Payload tracking errors are uniformly ultimately bounded with high probability.

The control framework effectively compensates for unknown disturbances and uncertainties.

The approach achieves improved payload transport accuracy in cooperative aerial manipulation.

Abstract

This paper presents a learning-based tracking control framework for cooperative transport of a rigid payload by multiple aerial manipulators under rigid grasp constraints. A unified geometric model is developed, yielding a coupled agent--payload differential--algebraic system that explicitly captures contact wrenches, payload dynamics, and internal force redundancy. A leader--follower architecture is adopted in which a designated leader generates a desired payload wrench based on geometric tracking errors, while the remaining agents realize this wrench through constraint-consistent force allocation. Unknown disturbances and modeling uncertainties are compensated using Gaussian Process (GP) regression. High-probability bounds on the learning error are explicitly incorporated into the control design, combining GP feedforward compensation with geometric feedback. Lyapunov analysis establishes uniform ultimate boundedness of the payload tracking errors with high probability, with an ultimate bound that scales with the GP predictive uncertainty.