Building 2D Model of Compound Eye Vision for Machine Learning

This paper presents a two-dimensional mathematical model of compound eye vision. Such a model is useful for solving navigation issues for autonomous mobile robots on the ground plane. The model is inspired by the insect compound eye that consists of ommatidia, which are tiny independent photoreception units, each of which combines a cornea, lens, and rhabdom. The model describes the planar binocular compound eye vision, focusing on measuring distance and azimuth to a circular feature with an arbitrary size. The model provides a necessary and sufficient condition for the visibility of a circular feature by each ommatidium. On this basis, an algorithm is built for generating a training data set to create two deep neural networks (DNN): the first detects the distance, and the second detects the azimuth to a circular feature. The hyperparameter tuning and the configurations of both networks are described. Experimental results showed that the proposed method could effectively and accurately detect the distance and azimuth to objects.

Tiling mechanisms of the compound eye through geometrical tessellation

Tilling patterns are observed in many biological structures. Hexagonal tilling, commonly observed in the compound eyes of wild-type Drosophila, is dominant in nature; this dominance can probably be attributed to physical restrictions such as structural robustness, minimal boundary length, and space filling efficiency. Surprisingly, tetragonal tiling patterns are also observed in some Drosophila small eye mutants and aquatic crustaceans. Herein, geometrical tessellation is shown to determine the ommatidial tiling patterns. In small eye mutants, the hexagonal pattern is transformed into a tetragonal pattern as the relative positions of neighboring ommatidia are stretched along the dorsal-ventral axis. Hence, the regular distribution of ommatidia and their uniform growth collectively play an essential role in the establishment of tetragonal and hexagonal tiling patterns in compound eyes.

Soft Array Surface-Changing Compound Eye

The field-of-view (FOV) of compound eyes is an important index for performance evaluation. Most artificial compound eyes are optical, fabricated by imitating insect compound eyes with a fixed FOV that is difficult to adjust over a wide range. The compound eye is of great significance in the field of tracking high-speed moving objects. However, the tracking ability of a compound eye is often limited by its own FOV size and the reaction speed of the rudder unit matched with the compound eye, so that the compound eye cannot better adapt to tracking high-speed moving objects. Inspired by the eyes of many organisms, we propose a soft-array, surface-changing compound eye (SASCE). Taking soft aerodynamic models (SAM) as the carrier and an infrared sensor as the load, the basic model of the variable structure infrared compound eye (VSICE) is established using an array of infrared sensors on the carrier. The VSICE model is driven by air pressure to change the array surface of the infrared sensor. Then, the spatial position of each sensor and its viewing area are changed and, finally, the FOV of the compound eye is changed. Simultaneously, to validate the theory, we measured the air pressure, spatial sensor position, and the FOV of the compound eye. When compared with the current compound eye, the proposed one has a wider adjustable FOV.

Building 2D Model of Compound Eye Vision for Machine Learning

This paper presents a two-dimensional mathematical model of compound eye vision. Such a model is useful for solving navigation issues for autonomous mobile robots on the ground plane. The model is inspired by the insect compound eye that consists of ommatidia, which are tiny independent photoreception units, each of which combines a cornea, lens, and rhabdom. The model describes the planar binocular compound eye vision, focusing on measuring distance and azimuth to a circular feature with an arbitrary size. The model provides a necessary and sufficient condition for the visibility of a circular feature by each ommatidium. On this basis, an algorithm is built for generating a training data set to create two deep neural networks (DNN): the first detects the distance, and the second detects the azimuth to a circular feature. The hyperparameter tuning and the configurations of both networks are described. Experimental results showed that the proposed method could effectively and accurately detect the distance and azimuth to objects.

New species of bristletails of the family Machilidae (Archaeognatha) from Kazakhstan

The fauna of bristletails of the family Machilidae in Kazakhstan currently includes one species of the genus Silvestrichiloides Mendes, 1990 and 13 species of the genus Allopsontus Silvestri, 1911. The present study describes one new species of the genus Silvestrichiloides (S. berkarensis Kaplin, sp. nov. from South Kazakhstan) and two new species of the genus Allopsontus (A. (Kaplinilis) nigrostriatus Kaplin, sp. nov. and A. (Machilanus) perfectus Kaplin, sp. nov. from Southeastern Kazakhstan). Silvestrichiloides berkarensis sp. nov. differs from the other species of this genus in the structure of antennal flagellum, apical palpomere of labial palp and ovipositor. Among species of the subgenus Kaplinilis Mendes, 1990, A. nigrostriatus sp. nov. belongs to a group of species characterized by numerous short chaetae on the ventral surface of the 5–7th palpomeres of the male maxillary palp and by the absence on the labial palp. This group includes two species: A. volgensis Kaplin, 1999 from Samara Region and A. smelyanskii Kaplin, 1999 from Orenbourg Region (both Russia). The new species differs from A. volgensis and A. smelyanskii in the length of the body and antenna, color of scales on the upper surface of the body, shape of the compound eye and paired ocellus, structure of the flagellum and apical palpomere of the male labial palp. The subgenus Machilanus Silvestri, 1934 is represented only by A. bitschi Wygodzinsky, 1962 from Afghanistan and A. perfectus sp. nov., which are characterized by numerous short chaetae on the ventral surface of the 2nd–7th palpomeres of the male maxillary palp. Allopsontus perfectus sp. nov. differs from A. bitschi in the shape of compound eyes, paired ocellus, structure of male labial palp and genitalia.

Biomimetic apposition compound eye fabricated using microfluidic-assisted 3D printing

After half a billion years of evolution, arthropods have developed sophisticated compound eyes with extraordinary visual capabilities that have inspired the development of artificial compound eyes. However, the limited 2D nature of most traditional fabrication techniques makes it challenging to directly replicate these natural systems. Here, we present a biomimetic apposition compound eye fabricated using a microfluidic-assisted 3D-printing technique. Each microlens is connected to the bottom planar surface of the eye via intracorporal, zero-crosstalk refractive-index-matched waveguides to mimic the rhabdoms of a natural eye. Full-colour wide-angle panoramic views and position tracking of a point source are realized by placing the fabricated eye directly on top of a commercial imaging sensor. As a biomimetic analogue to naturally occurring compound eyes, the eye’s full-colour 3D to 2D mapping capability has the potential to enable a wide variety of applications from improving endoscopic imaging to enhancing machine vision for facilitating human–robot interactions.