イナゴの視覚神経回路に着想を得たUAV向け競合型衝突回避戦略
An LGMD Based Competitive Collision Avoidance Strategy for UAV
イナゴの衝突検知神経回路LGMDを模倣し、UAVの視野を4分割して各々の競合型LGMDで方向別の衝突回避を実現する手法を提案し、シミュレーションと実機クアッドコプターで検証した。
著者: Jiannan Zhao, Xingzao Ma, Qinbing Fu, Cheng Hu, Shigang Yue
分類: cs.RO, cs.NE
原文アブストラクト
Building a reliable and efficient collision avoidance system for unmanned aerial vehicles (UAVs) is still a challenging problem. This research takes inspiration from locusts, which can fly in dense swarms for hundreds of miles without collision. In the locust's brain, a visual pathway of LGMD-DCMD (lobula giant movement detector and descending contra-lateral motion detector) has been identified as collision perception system guiding fast collision avoidance for locusts, which is ideal for designing artificial vision systems. However, there is very few works investigating its potential in real-world UAV applications. In this paper, we present an LGMD based competitive collision avoidance method for UAV indoor navigation. Compared to previous works, we divided the UAV's field of view into four subfields each handled by an LGMD neuron. Therefore, four individual competitive LGMDs (C-LGMD) compete for guiding the directional collision avoidance of UAV. With more degrees of freedom compared to ground robots and vehicles, the UAV can escape from collision along four cardinal directions (e.g. the object approaching from the left-side triggers a rightward shifting of the UAV). Our proposed method has been validated by both simulations and real-time quadcopter arena experiments.
