A Bio-inspired Collision Detector for Small Quadcopter

Zhao, Jiannan, Hu, Cheng, Zhang, Chun , Wang, Zhihua and Yue, Shigang (2018) A Bio-inspired Collision Detector for Small Quadcopter. In: 2018 International Joint Conference on Neural Networks (IJCNN), 8-13 July 2018, Rio de Janeiro, Brazil.

Full content URL: http://doi.org/10.1109/IJCNN.2018.8489298

A Bio-inspired Collision Detector for Small Quadcopter
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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


The sense and avoid capability enables insects to fly versatilely and robustly in dynamic and complex environment. Their biological principles are so practical and efficient that inspired we human imitating them in our flying machines. In this paper, we studied a novel bio-inspired collision detector and its application on a quadcopter. The detector is inspired from Lobula giant movement detector (LGMD) neurons in the locusts, and modeled into an STM32F407 Microcontroller Unit (MCU).
Compared to other collision detecting methods applied on quadcopters, we focused on enhancing the collision accuracy in a bio-inspired way that can considerably increase the computing efficiency during an obstacle detecting task even in complex and dynamic environment. We designed the quadcopter's responding operation to imminent collisions and tested this bio-inspired system in an indoor arena. The observed results from the experiments demonstrated that the LGMD collision detector is feasible to work as a vision module for the quadcopter's collision avoidance task.

Keywords:Detectors, Collision avoidance, robot vision, Lobula giant movement detector neurons, quadcopters collision avoidance task
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:34847
Deposited On:18 Mar 2019 14:34

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