IMR Press / JIN / Volume 17 / Issue 4 / DOI: 10.31083/j.jin.2018.04.0413
Open Access Research article
Flight control of robo-pigeon using a neural stimulation algorithm
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1 College of Astronautics, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China
2 Shandong Provincial Key Laboratory of Biosensors, Biology Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250103, China
3 College of Science, Nanjing University of Posts and Telecommunications, Nanjing 210016, China
J. Integr. Neurosci. 2018, 17(4), 337–342;
Submitted: 12 November 2017 | Accepted: 28 December 2017 | Published: 15 November 2018
Copyright: © 2018 The authors. Published by IMR press.
This is an open access article under the CC BY-NC 4.0 license.

Compared to conventional robots, animals have inherent advantages in terms of flexibility, stability, and the energy supply used for movement. Robo-pigeon has been investigated for several years because of their ideal mobility and carying capacity, but until- now, outdoor studies have not been reported. To develop a robo-pigeon flying outdoors, a miniaturized onboard preprogrammed control module has been developed, and a hierarchical stimulation algorithm proposed to ensure the effectiveness of brain stimulation. The control module consisted of a miniaturized Global Positioning System, a micro-controller, a brain stimulator, and a Secure Digital Memory Card saving a data log. It was capable of the flight control or flight trajectory manipulation of robo-pigeons in long-distance free-flight outdoors. The dimensions of the microsystem are 34 mm × 24 mm × 20 mm (L × W × H) and it weighs less than 17g. According to spatial coordinates or temporal settings, the controller can automatically emit a stimulus signal. This is one of the first outdoor demonstrations of flight control of robo-animals by neural-stimulation. The microsystem and control method described here offers distinct advantages for the control of movement and the investigation of bird flight.

specialized robot
flight control
deep brain stimulation.
Fig. 1.
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