A Study on Vision-Based Backstepping Control for a Target Tracking System

Abstract

This paper proposes a new method to control the pose of a camera mounted on a two-axis gimbal system for visual servoing applications. In these applications, the camera should be stable while its line-of-sight points at a target located within the camera’s field of view. One of the most challenging aspects of these systems is the coupling in the gimbal kinematics as well as the imaging geometry. Such factors must be considered in the control system design process to achieve better control performances. The novelty of this study is that the couplings in both mechanism’s kinematics and imaging geometry are decoupled simultaneously by a new technique, so popular control methods can be easily implemented, and good tracking performances are obtained. The proposed control configuration includes a calculation of the gimbal’s desired motion taking into account the coupling influence, and a control law derived by the backstepping procedure. Simulation and experimental studies were conducted, and their results validate the efficiency of the proposed control system. Moreover, comparison studies are conducted between the proposed control scheme, the image-based pointing control, and the decoupled control. This proves the superiority of the proposed approach that requires fewer measurements and results in smoother transient responses.

A Study on Robust Finite-Time Visual Servoing with a Gyro-Stabilized Surveillance System

“…The structure of the gyro-stabilized system to be controlled is shown in Figure 1. A completed representation of the system, with the dynamics of a two-axis gimbal and kinematics of a projection on the image plane, is as follows [27]: Moreover, let the system model (1) be rewritten as follows:…”

Design and Experimentation of a Residual-Input Tube-End Cutting System for Plasma Bags Based on Machine Vision

“…Moreover, two different paths, namely machine vision and deep learning, have been separately verified to detect defects, and it has been pointed out that each approach has its advantages and disadvantages for different types of defects [13]. Finally, vision and servo system integration can be employed to maintain a stable camera level status by simultaneously decoupling the machine kinematics and imaging geometry for control [14]. Domestic and international scholars have conducted many studies covering various aspects regarding machine-vision-based systems, such as precision, color, shape, and position.…”

Actuators and Sensors for Application in Agricultural Robots: A Review

“…Similarly to machine vision, and based on CMOS image sensor (CIS) cameras, Lyu et al [138] proposed a freespace recognition technique in an orchard environment, for a developed small agricultural unmanned ground vehicle (UGV), and using a low-cost, lightweight processor. As an external sensor of intelligent agricultural robots, machine vision is the eye of the operating equipment for agricultural robots and the most prominent information source, which has the advantages of rich perceptual information and complete information collection [131]. The first step for agricultural robot operation is to achieve autonomous obstacle avoidance, and the prerequisite for obstacle avoidance is to enable robots to perceive their surrounding environment [132].…”