Time-of-Travel Methods for Measuring Optical Flow on Board a Micro Flying Robot

Bioinspired Perception and Navigation of Service Robots in Indoor Environments: A Review

Biological principles draw attention to service robotics because of similar concepts when robots operate various tasks. Bioinspired perception is significant for robotic perception, which is inspired by animals' awareness of the environment. This paper reviews the bioinspired perception and navigation of service robots in indoor environments, which are popular applications of civilian robotics. The navigation approaches are classified by perception type, including vision-based, remote sensing, tactile sensor, olfactory, sound-based, inertial, and multimodal navigation. The trend of state-of-art techniques is moving towards multimodal navigation to combine several approaches. The challenges in indoor navigation focus on precise localization and dynamic and complex environments with moving objects and people.

Modelling Drosophila motion vision pathways for decoding the direction of translating objects against cluttered moving backgrounds

Decoding the direction of translating objects in front of cluttered moving backgrounds, accurately and efficiently, is still a challenging problem. In nature, lightweight and low-powered flying insects apply motion vision to detect a moving target in highly variable environments during flight, which are excellent paradigms to learn motion perception strategies. This paper investigates the fruit fly Drosophila motion vision pathways and presents computational modelling based on cutting-edge physiological researches. The proposed visual system model features bio-plausible ON and OFF pathways, wide-field horizontal-sensitive (HS) and vertical-sensitive (VS) systems. The main contributions of this research are on two aspects: (1) the proposed model articulates the forming of both direction-selective and direction-opponent responses, revealed as principal features of motion perception neural circuits, in a feed-forward manner; (2) it also shows robust direction selectivity to translating objects in front of cluttered moving backgrounds, via the modelling of spatiotemporal dynamics including combination of motion pre-filtering mechanisms and ensembles of local correlators inside both the ON and OFF pathways, which works effectively to suppress irrelevant background motion or distractors, and to improve the dynamic response. Accordingly, the direction of translating objects is decoded as global responses of both the HS and VS systems with positive or negative output indicating preferred-direction or null-direction translation. The experiments have verified the effectiveness of the proposed neural system model, and demonstrated its responsive preference to faster-moving, higher-contrast and larger-size targets embedded in cluttered moving backgrounds.

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

Motion perception is a critical capability determining a variety of aspects of insects' life, including avoiding predators, foraging, and so forth. A good number of motion detectors have been identified in the insects' visual pathways. Computational modeling of these motion detectors has not only been providing effective solutions to artificial intelligence, but also benefiting the understanding of complicated biological visual systems. These biological mechanisms through millions of years of evolutionary development will have formed solid modules for constructing dynamic vision systems for future intelligent machines. This article reviews the computational motion perception models originating from biological research on insects' visual systems in the literature. These motion perception models or neural networks consist of the looming-sensitive neuronal models of lobula giant movement detectors (LGMDs) in locusts, the translation-sensitive neural systems of direction-selective neurons (DSNs) in fruit flies, bees, and locusts, and the small-target motion detectors (STMDs) in dragonflies and hoverflies. We also review the applications of these models to robots and vehicles. Through these modeling studies, we summarize the methodologies that generate different direction and size selectivity in motion perception. Finally, we discuss multiple systems integration and hardware realization of these bio-inspired motion perception models.

Insect-inspired vision for autonomous vehicles

Flying insects are being studied these days as if they were agile micro air vehicles fitted with smart sensors, requiring very few brain resources. The findings obtained on these natural fliers have proved to be extremely valuable when it comes to designing compact low-weight artificial optical sensors capable of performing visual processing tasks robustly under various environmental conditions (light, clouds, contrast). Here, we review some outstanding bio-inspired visual sensors, which can be used for either detecting motion in the visible spectrum or controlling celestial navigation in the ultraviolet spectrum and for attitude stabilisation purposes. Biologically inspired visual sensors do not have to comprise a very large number of pixels: they are able to perform both short and long range navigation tasks surprisingly well with just a few pixels and a weak resolution.