# An LGMD Based Competitive Collision Avoidance Strategy for UAV

## TL;DR

This paper introduces a novel LGMD-based collision avoidance system for UAVs, inspired by locust vision, dividing the UAV's view into four sectors with competitive LGMD neurons to enable effective indoor navigation and collision avoidance.

## Contribution

It proposes a new LGMD-based approach with four competing neurons for directional collision avoidance in UAVs, validated through simulations and real-world experiments.

## Key findings

- Effective collision avoidance in indoor UAV navigation.
- Successful validation via simulations and real-time experiments.
- Enhanced directional control with four LGMD neurons.

## Abstract

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.