A Bee in the Mirror: A Bio-Inspired Model for Vision Based Mid-Air Collision Avoidance

The objective of much robotics research in recent years has been to develop lightweight autonomous flying robots. Applications for these aircrafts are numerous, including natural disaster monitoring, mapping, and search-and-rescue missions. One of the major challenges is the development of a control system that is capable of avoiding collisions with moving obstacles whilst being lightweight, energy efficient and requiring low processing power. Flying insects, like engineers, face similar challenges and have developed solutions that rely primarily upon information extracted from their visual system. Detecting and avoiding head-on collisions with other flying agents is a challenging task because the approaching agent remains in the centre of focus of the visual field and thus do not generate visual motion cues. Here, we attempt to address this problem by developing an obstacle avoidance model linking a flying animal morphology and the minimum visual acuity required to detect and avoid a frontal collision. Our model results are tested on bumblebees. According to our model, these flying insects have to detect objects subtending only 2° to 5° in their visual field, which is close to the spatial resolution of their visual system.

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