Optic flow-based collision-free strategies: From insects to robots

Abstract

Flying insects are able to fly smartly in an unpredictable environment. It has been found that flying insects have smart neurons inside their tiny brains that are sensitive to visual motion also called optic flow. Consequently, flying insects rely mainly on visual motion during their flight maneuvers such as: takeoff or landing, terrain following, tunnel crossing, lateral and frontal obstacle avoidance, and adjusting flight speed in a cluttered environment. Optic flow can be defined as the vector field of the apparent motion of objects, surfaces, and edges in a visual scene generated by the relative motion between an observer (an eye or a camera) and the scene. Translational optic flow is particularly interesting for short-range navigation because it depends on the ratio between (i) the relative linear speed of the visual scene with respect to the observer and (ii) the distance of the observer from obstacles in the surrounding environment without any direct measurement of either speed or distance. In flying insects, roll stabilization reflex and yaw saccades attenuate any rotation at the eye level in roll and yaw respectively (i.e. to cancel any rotational optic flow) in order to ensure pure translational optic flow between two successive saccades. Our survey focuses on feedback-loops which use the translational optic flow that insects employ for collision-free navigation. Optic flow is likely, over the next decade to be one of the most important visual cues that can explain flying insects' behaviors for short-range navigation maneuvers in complex tunnels. Conversely, the biorobotic approach can therefore help to develop innovative flight control systems for flying robots with the aim of mimicking flying insects' abilities and better understanding their flight.

Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review

“…Franceschini devoted to survey biological research and computational models on the basis of a scheme of fly elementary motion detectors (EMDs) and relevant bio-robotics applications, systematically [59]. Recently, Serres and Ruffier reviewed the applications of fly optic flow-based strategies to UAVs and MAVs for multiple visually guided behaviours, like collision avoidance, terrain following, landing and tunnel crossing [193]. More generically, Desouza and Kak surveyed vision techniques, that varied from traditional computer vision methods to insects optic flow strategies, for mobile robots navigation [44].…”

“…Although there have been a few reviews on biological and computational models, as well as applications of fly visual systems, e.g. [193,59,26]; no survey has been provided to involve the looming sensitive neuron models inspired by locust visual systems and the small target motion sensitive neuron models inspired by dragonflies and hover flies and corresponding applications, systematically.…”

“…As illustrated in the Fig. 24a, the OF can be defined as a flow vector-field perceived by the visual modality of either animals or machines [121,193]; this field is generated by the relative and apparent motion between observer and scene. The OF includes two sub-fields of the translational flow and the rotational flow: both are rigorously dependent of the structure of environment including textures and brightness and etc.…”

“…Insects, in particular, are 'experts' in motion perception, even though they have tiny brains and much smaller number of visual neurons compared to vertebrates. Much evidence has demonstrated their amazing ability to deal with visual motion and interacting with dynamic and cluttered scenes corresponding to quick and flexible reactions like collision avoidance and target tracking and following, and even on some aspects, performing better than vertebrates and humans [30,28,26,31,66,59,193].…”

“…In terms of biology, the underlying circuits and mechanisms of insects' visual processing systems remain largely unknown to date [193,59,66]. While the biological substrates are elusive, the computational modelling and applications to machine vision are of particular usefulness to help understand the neural characteristics and demonstrate the functionality of visual circuits or pathways [193,31,59]. In addition, these models can be ideal modules for designing dynamic vision systems or sensors for future intelligent machines like robots and vehicles for motion perception in a low-energy, fast and reliable manner.…”