  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.
