  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.
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 potential to be implemented on embedded collision
detection systems for small or micro UAVs.
