IEEE Trans Neural Netw Learn Syst. 2023 May;34(5):2539-2553. doi: 10.1109/TNNLS.2021.3106946. Epub 2023 May 2.
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 nonpreferred 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.
碰撞检测是无人机 (UAV) 面临的最具挑战性的任务之一。对于小型或微型无人机来说，这尤其如此，因为它们的计算能力有限。在自然界中，具有紧凑而简单视觉系统的飞行昆虫展现出了在复杂环境中导航和避免碰撞的非凡能力。蝗虫就是一个很好的例子。它们可以通过一种名为“Lobula 巨大运动探测器 (LGMD)”的基于运动的视觉神经元的活动，在密集的群体中避免碰撞。LGMD 神经元的定义特征是其对逼近的偏好。作为一种飞行昆虫的视觉神经元，LGMD 被认为是构建无人机碰撞检测系统的理想基础。然而，现有的 LGMD 模型无法清晰地区分逼近与其他视觉线索，例如由于无人机敏捷飞行而导致的复杂背景运动。为了解决这个问题，我们提出了一种新的模型，该模型实现了分布式时空突触相互作用，这是受蝗虫突触形态学的最新发现启发的。我们首先引入了局部分布式激发，以增强具有首选速度的视觉运动引起的激发。然后，引入了径向扩展的时间延迟抑制，以与分布式激发竞争，并选择性地抑制非首选视觉运动。因此，我们的模型中的兴奋和抑制之间的这种时空竞争是针对代表逼近而不是具有这些分布式突触相互作用的背景运动的首选图像角速度进行调整的。已经进行了系统的实验来验证所提出的模型在无人机敏捷飞行中的性能。结果表明，这种新模型大大提高了复杂飞行场景中的逼近选择性，并且有可能在小型或微型无人机的嵌入式碰撞检测系统上实现。
