Enhancing LGMD’s Looming Selectivity for UAV With Spatial–Temporal Distributed Presynaptic Connections

Abstract

Collision detection is one of the most challenging tasks for Unmanned Aerial Vehicles (UAVs). This is especially true for small or micro UAVs, due to their limited computational power. In nature, flying insects with compact and simple visual systems demonstrate their remarkable ability to navigate and avoid collision in complex environments. A good example of this is provided by locusts. They can avoid collisions in a dense swarm through the activity of a motion-based visual neuron called the Lobula Giant Movement Detector (LGMD). The defining feature of the LGMD neuron is its preference for looming. As a flying insect's visual neuron, LGMD is considered to be an ideal basis for building UAV's collision detecting system. However, existing LGMD models cannot distinguish looming clearly from other visual cues such as complex background movements caused by UAV agile flights. To address this issue, we proposed a new model implementing distributed spatial-temporal synaptic interactions, which is inspired by recent findings in locusts' synaptic morphology. We first introduced the locally distributed excitation to enhance the excitation caused by visual motion with preferred velocities. Then radially extending temporal latency for inhibition is incorporated to compete with the distributed excitation and selectively suppress the non-preferred visual motions. This spatial-temporal competition between excitation and inhibition in our model is therefore tuned to preferred image angular velocity representing looming rather than background movements with these distributed synaptic interactions. Systematic experiments have been conducted to verify the performance of the proposed model for UAV agile flights. The results have demonstrated that this new model enhances the looming selectivity in complex flying scenes considerably, and has the potential to be implemented on embedded collision detection systems for small or micro UAVs.

Bio-Inspired Small Target Motion Detection With Spatio-Temporal Feedback in Natural Scenes

“…Inspiration from neuroscience is a promising approach for designing artificial visual systems with requirement of a high level of efficiency and robustness but limited in computational and memory budget [25]- [30]. It has attracted a great deal of interests and become an emerging research area with a number of practical applications, such as visually guided flights or landing [31], [32], autonomous navigation [33], [34], and collision detection [35], [36]. Our work is mainly related to two types of widely investigated motion-sensitive neurons, called lobula plate tangential cells (LPTCs) [37], [38] and small target motion detectors (STMDs), whose biological properties and computational models are briefly discussed.…”

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

“…This method suffers from high failure rates. To improve the UAV's collision detection system, a model with distributed spatial-temporal synaptic interactions is developed [51]. The model is motivated by locusts' ability to avoid collisions using a motion-based visual neuron called lobula giant movement detector.…”

An Angular Acceleration Based Looming Detector for Moving UAVs

“…Rind utilized the LGMD model [ 14 ] to extract the fast-moving edges of the looming objects and set the angular size threshold for collision identification. Zhao, on the other hand, extracted the image angular velocity using the D-LGMD model [ 15 ] and established the angular velocity threshold for collision identification. Both approaches were employed for collision detection.…”