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Journal of Integrative Neuroscience  2018, Vol. 17 Issue (4): 337-342    DOI: 10.31083/j.jin.2018.04.0413
Research article Previous articles | Next articles
Flight control of robo-pigeon using a neural stimulation algorithm
Hao Wang1, 2, Junjie Li1, Lei Cai2, *(), Ce Wang1, Aiju Shi3
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
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Abstract  

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.

Key words:  specialized robot      robo-pigeon      flight control      neuromodulation      cyborg      robo-animal      deep brain stimulation.     
Submitted:  12 November 2017      Accepted:  28 December 2017      Published:  15 November 2018     
*Corresponding Author(s):  Lei Cai     E-mail:  cailei@sdas.org.

Cite this article: 

Hao Wang, Junjie Li, Lei Cai, Ce Wang, Aiju Shi. Flight control of robo-pigeon using a neural stimulation algorithm. Journal of Integrative Neuroscience, 2018, 17(4): 337-342.

URL: 

https://jin.imrpress.com/EN/10.31083/j.jin.2018.04.0413     OR     https://jin.imrpress.com/EN/Y2018/V17/I4/337

Fig. 1.  Distribution of ICo and FRM at the coronal section of 3.50 mm. Left panel is cited from the stereotaxic atlas of the pigeon brain [25], while the right panel is a corresponding MRI image. The enclosed area in the MRI image indicates ICo (above) and FRM (below).

Table 1  Coordinates of the two targets chosen for deep brain stimulation in robo-pigeons
Target Sagital
position (mm)
Coronal
position mm)
Vertical
position (mm)
ICo 3.00~4.00 2.00~3.50 6.60~8.00
FRM 3.00~3.60 1.00~2.50 4.00~6.50
Fig. 2.  A. The system block diagram of the control module. CPU is the kernel of the system with the preprogrammed code. According to the signal from GPS, CPU judges whether to send stimulation commands to BMI. All these information processed by CPU is logged in SD-card; B. The side photo of the control module. It is in size of 34 mm× 24 mm× 20 mm(L×W×H) and in mass of 16.8 g including a rechargeable battery. During experiments, it is attached by the Velcro to the back of the tested bird.

Fig. 3.  A. The robo-pigeon is landing on the loft. The pigeons are trained to carry a similar sized and weighted gauge block in daily flight. During experiments, the gauge block is replaced by the control module. B. The close-up view of the control module mounted on the robo-pigeon. The module is connected by loose winding wires to the BMI on the robo-pigeon’s head, by which the stimulation commands are transmitted.

Table 2  Locations of the stimulation targets used in the two robo-pigeons
Robopigeon Half brain Sagital
position(mm)
Coronal
position (mm)
Vertical
position (mm)
# 1 Right 3.50 2.50 7.00
Left 3.50 3.00 7.20
# 2 Right 3.00 3.00 7.50
Left 3.50 2.50 7.20
Table 3  Success ratio of deep brain stimulation for two robo-pigeons
Robopigeon Half brain Singlestimuli(n = 50) Periodicstimuli(n = 25)
Multi periodicstimuli(n = 5)
# 1 Right 78% 88% 100%
Left 76% 84% 80%
# 2 Right 72% 92% 100%
Left 76% 88% 100%
Fig. 4.  A. Simple control logic for robo-pigeons flying around loft. After takeoff of a robo-pigeon, the control module waits 5 minutes to make sure the pigeon flying under stable conditions, and then generates the directional-stimuli alternatively by a specified number of times to test the stimulation efficiency. B. One example of flight trajectory under this control logic. GPS sampling frequency was 1 Hz, and the flight direction was indicated by black arrows on the trajectory. Right-pointing and left-pointing triangles indicate the timing when neural stimulation applied, respectively.

Fig. 5.  A. One example of robo-pigeon flight long distance outdoor. The color coded curve is the flight trajectory from south to north, and the color represents the robo-pigeon’s flight speed. The background is the local map referring to Google Map. Two white squares indicate two ROIs, where the multi periodic-stimuli were applied and the orbiting flight was elicited. B. & C. The detail of the flight trajectory in the squares indicated in A.

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