An Efficient Egocentric Regulator for Continuous Targeting Problems of the Underactuated Quadrotor

TL;DR

This paper introduces a novel egocentric regulation method for underactuated quadrotors that achieves real-time, high-frequency target tracking with significantly improved computational efficiency over traditional nonlinear optimization techniques.

Contribution

The paper proposes an efficient egocentric control approach that simplifies the nonlinear tracking problem into a quadratic form, enabling analytical control input generation and real-time performance.

Findings

Achieves control computation in approximately 0.3 ms on standard onboard hardware.

Outperforms generic nonlinear optimizers by a factor of about 350 in speed.

Maintains stable and high-frequency tracking in simulations and biological experiments.

Abstract

Flying robots such as the quadrotor could provide an efficient approach for medical treatment or sensor placing of wild animals. In these applications, continuously targeting the moving animal is a crucial requirement. Due to the underactuated characteristics of the quadrotor and the coupled kinematics with the animal, nonlinear optimal tracking approaches, other than smooth feedback control, are required. However, with severe nonlinearities, it would be time-consuming to evaluate control inputs, and real-time tracking may not be achieved with generic optimizers onboard. To tackle this problem, a novel efficient egocentric regulation approach with high computational efficiency is proposed in this paper. Specifically, it directly formulates the optimal tracking problem in an egocentric manner regarding the quadrotor's body coordinates. Meanwhile, the nonlinearities of the system are peeled off through a mapping of the feedback states as well as control inputs, between the inertial and body coordinates. In this way, the proposed efficient egocentric regulator only requires solving a quadratic performance objective with linear constraints and then generate control inputs analytically. Comparative simulations and mimic biological experiment are carried out to verify the effectiveness and computational efficiency. Results demonstrate that the proposed control approach presents the highest and stablest computational efficiency than generic optimizers on different platforms. Particularly, on a commonly utilized onboard computer, our method can compute the control action in approximately 0.3 ms, which is on the order of 350 times faster than that of generic nonlinear optimizers, establishing a control frequency around 3000 Hz.