Abstract
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.
Highlights
Robotics is a rapidly developing industrial area
The ommatidium field of view is defined as a solid angle bounded by its legs
Using the 2D model of binocular compound eye vision presented in Section 3, we investigated the capability of deep neural networks (DNN) to determine the distance and azimuth to a visible object
Summary
Robotics is a rapidly developing industrial area. The modern classification of robots is based on the environment in which the robot operates and its functionality. One of the most important functions is the ability of the robot to move in the operating environment. Robots capable of movement are classified as mobile. Robots that do not have functionality for movement are classified as fixed. An example of a fixed robot is a robot manipulator, widely used in assembly production. Such a robot operates in an environment adapted for its functioning. Mobile robots have to operate in boundless spaces in a changing environment under conditions that are not known in advance [1]. The non-autonomous mobile robot rely on operator control. The autonomous robot has no connection with the operator and must make decisions about further actions on its own
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