scholarly journals Hardware structure of a surveillance system for strategic objectives

2019 ◽  
Vol 290 ◽  
pp. 08004
Author(s):  
Victor Constantin ◽  
Mihai Avram ◽  
Constantin Bucsan ◽  
Alexandru Vlasceanu
Keyword(s):  
Control System ◽  
High Resolution ◽  
Mobile Robots ◽  
Surveillance System ◽  
Infrared Camera ◽  
Radar Sensor ◽  
Strategic Objectives ◽  
Hardware Structure

The paper presents the hardware structure of a system for the surveillance of strategic objectives. The proposed system consists of several mobile robots patrolling along a perimeter, identifying potential intruders. The information obtained is transmitted to both the mobile robots and a fixed control system, which may decide to get more detailed information about the event, sending a quadcopter in the area with a high-resolution infrared camera, and a radar sensor. It communicates to the control system all the information obtained, and these will lead to a decision.

Ultrasonic sensor system for the uses in intelligent motion control system of a mobile robots group

2010 ◽  
Vol 7 ◽  
pp. 109-117
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov ◽  
B.S. Yudintsev
Keyword(s):  
Control System ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Motion Control ◽  
High Speed ◽  
Ultrasonic Sensor ◽  
Sensor System ◽  
Motion Control System ◽  
Speed Sensor ◽  
Working Space

The article deals with the development of a high-speed sensor system for a mobile robot, used in conjunction with an intelligent method of planning trajectories in conditions of high dynamism of the working space.

scholarly journals Energy Modeling and Power Measurement for Mobile Robots

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 27 ◽  
Author(s):  
Linfei Hou ◽  
Liang Zhang ◽  
Jongwon Kim
Keyword(s):  
Control System ◽  
Energy Consumption ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Electrical Power ◽  
Energy Model ◽  
Energy Modeling ◽  
Power Measurement ◽  
Sensor System ◽  
System Control

To improve the energy efficiency of a mobile robot, a novel energy modeling method for mobile robots is proposed in this paper. The robot can calculate and predict energy consumption through the energy model, which provides a guide to facilitate energy-efficient strategies. The energy consumption of the mobile robot is first modeled by considering three major factors: the sensor system, control system, and motion system. The relationship between the three systems is elaborated by formulas. Then, the model is utilized and experimentally tested in a four-wheeled Mecanum mobile robot. Furthermore, the power measurement methods are discussed. The energy consumption of the sensor system and control system was at the milliwatt level, and a Monsoon power monitor was used to accurately measure the electrical power of the systems. The experimental results showed that the proposed energy model can be used to predict the energy consumption of the robot movement processes in addition to being able to efficiently support the analysis of the energy consumption characteristics of mobile robots.

The Development of an Infrared Camera Using Graphene: Achieving Efficient High-Resolution Infrared Images.

2012 ◽  
Vol 6 (1) ◽  
pp. 4-7 ◽  
Author(s):  
King Wai Chiu Lai ◽  
Ning Xi ◽  
Hongzhi Chen ◽  
Bo Song ◽  
LiangLiang Chen
Keyword(s):  
High Resolution ◽  
Infrared Camera ◽  
Infrared Images

scholarly journals Robot Control Using Alternative Trajectories Based on Inverse Errors in the Workspace

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Quoc Khanh Duong ◽  
Thanh Trung Trang ◽  
Thanh Long Pham
Keyword(s):  
Control System ◽  
High Resolution ◽  
Robot Control ◽  
Low Cost ◽  
Transmission System ◽  
Measuring System ◽  
Robot Calibration ◽  
Measuring Device ◽  
Calibration Solution ◽  
System Errors

It is easy to realize that most robots do not move to the desired endpoint (Tool Center Point (TCP)) using high-resolution noncontact instrumentation because of manufacturing and assembly errors, transmission system errors, and mechanical wear. This paper presents a robot calibration solution by changing the endpoint trajectories while maintaining the robot’s control system and device usages. Two independent systems to measure the endpoint positions, the robot encoder and a noncontact measuring system with a high-resolution camera, are used to determine the endpoint errors. A new trajectory based on the measured errors will be built to replace the original trajectory. The results show that the proposed method can significantly reduce errors; moreover, this is a low-cost solution and easy to apply in practice and calibration can be done cyclically. The only requirement for this method is a noncontact measuring device with high-resolution and located independently with the robot in calibration.

scholarly journals Assessment of Doppler Radar Radial Wind Observation Quality from Different Echo Sources for Assimilation during the Sydney 2014 Forecast Demonstration Project

2018 ◽  
Vol 35 (8) ◽  
pp. 1605-1620 ◽  
Author(s):  
Susan Rennie ◽  
Peter Steinle ◽  
Alan Seed ◽  
Mark Curtis ◽  
Yi Xiao
Keyword(s):  
Quality Control ◽  
Control System ◽  
Statistical Analysis ◽  
High Resolution ◽  
Radial Velocity ◽  
Numerical Weather Prediction ◽  
Doppler Radar ◽  
Weather Prediction ◽  
Demonstration Project ◽  
Prediction System

AbstractA new quality control system, primarily using a naïve Bayesian classifier, has been developed to enable the assimilation of radial velocity observations from Doppler radar. The ultimate assessment of this system is the assimilation of observations in a pseudo-operational numerical weather prediction system during the Sydney 2014 Forecast Demonstration Project. A statistical analysis of the observations assimilated during this period provides an assessment of the data quality. This will influence how observations will be assimilated in the future, and what quality control and errors are applicable. This study compares observation-minus-background statistics for radial velocities from precipitation and insect echoes. The results show that with the applied level of quality control, these echo types have comparable biases. With the latest quality control, the clear air observations of wind are apparently of similar quality to those from precipitation and are therefore suitable for use in high-resolution NWP assimilation systems.

Expert Guided Autonomous Mobile Robot Learning

2011 ◽  
pp. 216-231
Author(s):  
Gintautas Narvydas ◽  
Vidas Raudonis ◽  
Rimvydas Simutis
Keyword(s):  
Control System ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Control Systems ◽  
Intelligent Control ◽  
Main Idea ◽  
Autonomous Mobile Robots ◽  
Autonomous Mobile Robot ◽  
Intelligent Control System ◽  
Objective Function Values

In the control of autonomous mobile robots there exist two types of control: global control and local control. The requirement to solve global and local tasks arises respectively. This chapter concentrates on local tasks and shows that robots can learn to cope with some local tasks within minutes. The main idea of the chapter is to show that, while creating intelligent control systems for autonomous mobile robots, the beginning is most important as we have to transfer as much as possible human knowledge and human expert-operator skills into the intelligent control system. Successful transfer ensures fast and good results. One of the most advanced techniques in robotics is an autonomous mobile robot on-line learning from the experts’ demonstrations. Further, the latter technique is briefly described in this chapter. As an example of local task the wall following is taken. The main goal of our experiment is to teach the autonomous mobile robot within 10 minutes to follow the wall of the maze as fast and as precisely as it is possible. This task also can be transformed to the obstacle circuit on the left or on the right. The main part of the suggested control system is a small Feed-Forward Artificial Neural Network. In some particular cases – critical situations – “If-Then” rules undertake the control, but our goal is to minimize possibility that these rules would start controlling the robot. The aim of the experiment is to implement the proposed technique on the real robot. This technique enables to reach desirable capabilities in control much faster than they would be reached using Evolutionary or Genetic Algorithms, or trying to create the control systems by hand using “If-Then” rules or Fuzzy Logic. In order to evaluate the quality of the intelligent control system to control an autonomous mobile robot we calculate objective function values and the percentage of the robot work loops when “If-Then” rules control the robot.

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