A Bio-inspired Collision Detecotr for Small Quadcopter
This work addresses collision avoidance for small quadcopters in dynamic environments, but it is incremental as it builds on existing bio-inspired methods without broad SOTA claims.
The paper tackled the problem of enabling small quadcopters to detect collisions efficiently by developing a bio-inspired detector based on locust LGMD neurons, implemented on an STM32F407 MCU, and demonstrated its feasibility in indoor tests for collision avoidance.
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.