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Flying robots such as the quadrotor could provide an efficient approach formedical treatment or sensor placing of wild animals. In these applications,continuously targeting the moving animal is a crucial requirement. Due to theunderactuated characteristics of the quadrotor and the coupled kinematics withthe animal, nonlinear optimal tracking approaches, other than smooth feedbackcontrol, are required. However, with severe nonlinearities, it would betime-consuming to evaluate control inputs, and real-time tracking may not beachieved with generic optimizers onboard. To tackle this problem, a novelefficient egocentric regulation approach with high computational efficiency isproposed in this paper. Specifically, it directly formulates the optimaltracking problem in an egocentric manner regarding the quadrotor's bodycoordinates. Meanwhile, the nonlinearities of the system are peeled off througha mapping of the feedback states as well as control inputs, between theinertial and body coordinates. In this way, the proposed efficient egocentricregulator only requires solving a quadratic performance objective with linearconstraints and then generate control inputs analytically. Comparativesimulations and mimic biological experiment are carried out to verify theeffectiveness and computational efficiency. Results demonstrate that theproposed control approach presents the highest and stablest computationalefficiency than generic optimizers on different platforms. Particularly, on acommonly utilized onboard computer, our method can compute the control actionin approximately 0.3 ms, which is on the order of 350 times faster than that ofgeneric nonlinear optimizers, establishing a control frequency around 3000 Hz.

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