Machine Learning

A Bio-inspired Collision Detecotr for Small Quadcopter

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  • arXiv
    5 pts

    A Bio-inspired Collision Detecotr for Small Quadcopter

    Sense and avoid capability enables insects to fly versatilely and robustly in dynamic 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 LGMD neurons in the locusts, and modeled into an STM32F407 MCU. Compared to other collision detecting methods applied on quadcopters, we focused on enhancing the collision selectivity in a bio-inspired way that can considerably increase the computing efficiency during an obstacle detecting task even in complex dynamic environment. We designed the quadcopter’s responding operation 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.

    A Bio-inspired Collision Detecotr for Small Quadcopter
    by Jiannan Zhao, Cheng Hu, Chun Zhang, Zhihua Wang, Shigang Yue
    https://arxiv.org/pdf/1801.04530v1.pdf

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