A bio-inspired visual collision detection network integrated with dynamic temporal variance feedback regulated by scalable functional countering jitter streaming.

In pursuing artificial intelligence for efficient collision avoidance in robots, researchers draw inspiration from the locust's visual looming-sensitive neural circuit to establish an efficient neural network for collision detection. However, existing bio-inspired collision detection neural networks encounter challenges posed by jitter streaming, a phenomenon commonly experienced, for example, when a ground robot moves across uneven terrain. Visual inputs from jitter streaming induce significant fluctuations in grey values, distracting existing bio-inspired networks from extracting visually looming features.

A look into feedback neural computation upon collision selectivity.

Physiological studies have shown that a group of locust's lobula giant movement detectors (LGMDs) has a diversity of collision selectivity to approaching objects, relatively darker or brighter than their backgrounds in cluttered environments. Such diversity of collision selectivity can serve locusts to escape from attack by natural enemies, and migrate in swarm free of collision. For computational studies, endeavours have been made to realize the diverse selectivity which, however, is still one of the most challenging tasks especially in complex and dynamic real world scenarios.