2018-10-15 星期一

The high level of English is a standard for a top student.

1. Our deeds determine us, as much as we determine our deeds.

2. I hope that your dreams are as sweet as you are.

3. Describe a skill you learned in math class

You should say:
What it was
How you learned it
  
I am going to talk about one thing we learned in math class the first year of primary school. It’s multiplication. 
 
How we learned it was actually pretty interesting. It took several years and several steps. 
 
Step one, before we finished addition and subtraction within 100, we were asked just to recite the multiplication table without understanding it at all, which was actually beyond the capabilities of first-graders. Luckily, the teachers made it into rhymes so even beginners could get a jump start on learning multiplication by reciting the rhymes. They are really catchy and they go like this, instead of saying 7 multiplied by 7 equals 49. We say: 77 49; 99 81. It was extremely helpful and effective.

3. Time give us experience, reading to us knowledge.

4. The face can speak a thousand emotions, but it can easily mask what the heart feels. for the happiest face may be masking the most hurting heart. 

5. The beautiful thing about learning is nobody can take it away from you. ​​​​

6. I love you with all my heart and soul,and promise to be with you forever.

7. Have patience with all things, but first of all with yourself. 

8. Cowards die many times before their deaths; the valiant never taste of death but once. 

9. Life's tragedy is that we get old too soon and wise too late. 

10. Sleep is an important means for people to restore their physical strength, ensure their health and enhance their immunity. 

11. In recent days, the weather in most regions is no longer hot, and people can relax and enjoy the cool autumn. 

 

Paper

1. Mapping and localization of cooperative robots by ROS and SLAM in unknown working area

(https://ieeexplore.ieee.org/document/8105741) (http://sci-hub.tw/10.23919/sice.2017.8105741)

Abstract:

In this paper, we developed a control system hardware based on ROS and mapping and localization for two cooperative robots' self-driving and working in an unknown area. We applied the SLAM (Simultaneous Localization and Mapping) technology to recognize the robots' positions and environment conditions in the unknown area. And we also developed an UI system with C# windows programming to communicate between robots, and connected this UI to ROS. In order to improve the traditional architecture of ROS system and to use no outside sensors for self-driving and controlling of multi-robots, we designed a new hybrid architecture that only one PC served the task of master and the embedded system of Odroid-U3 and sensors were installed inside of two robots for communicating to master. By improving a control system with Linux, Windows, PC, and embedded board, the location of robot could be estimated successfully without expensive sensors and space markers. So, we expect that controlling of multiple robots in unknown working area would be possible easily without complex path planning of former studies.
 
2. An implementation of SLAM using ROS and Arduino https://ieeexplore.ieee.org/document/8286298
 
Abstract:
This paper aims to explore the Simultaneous Localization and Mapping (SLAM) problem in the context of implementation using the Robot Operating System (ROS) framework and the Arduino technology. The implementation of an inexpensive differential drive robot for SLAM is detailed and verified by mapping experiments conducted within domestic environments. Furthermore, a modest, yet convenient, theoretical explanation of the algorithm (Rao-Blackwellization particle filter) behind the platform is also presented. Overall, this report leads to a simple and cost effective way - including a code base and guidelines - to create robots for 2D mapping using modern technologies such as ROS.
 
Given a differential robot in an unstructured environment and without knowledge of its current position, the robot performs the SLAM as an integration of multiple steps for estimating the position of the different landmarks (features of the world) and its own location as it traverses the unknown map. One of the biggest feature in the SLAM problem is the high correlation between landmarks. In practical terms, this means that the success of SLAM is determined by the fact that, the more the robot explores the environment, the better the quality of the map and its estimated position.
  • A good map is needed for localization while…

  • An accurate pose estimate is required to build a map.

  1. Kalman filters approach.

  2. Particle filters approach.

The motivation behind the particle filter approach is to deal with arbitrary 

3. ROS based stereo vision system for autonomous vehiclehttps://ieeexplore.ieee.org/document/8392121
Abstract:
In this paper, we have designed a autonomous vehicle which is cost effective and powered by Robotic Operating System (ROS). The vehicle is capable of maintaining a constant speed and distance for monitoring or surveillance. ROS is implemented for trajectory tracking and telemetry. A low cost compact on-board embedded system powers the vehicle. Various image processing techniques are been implemented for navigation and obstacle detection. Artificial Neural Network which helps in finding the shortest path by using the acquired data from image processing. Different controllers were implemented for movement and obstacle avoidance including PI and PID. The performance were compared and the results are also discussed in this paper.   (very good!!!

Introduction

While the field of robotics has been developing significantly in recent years, robots still have a very long way to go before autonomous systems are viable in complex real-world situations. Autonomous robots are intelligent machines capable of performing tasks in the world by themselves, without explicit human control. In controlled conditions, autonomous robots have proved to be extremely successful. The problems with autonomous systems in the real-world are numerous. A robot must have some way to perceive its environment, but it becomes difficult to logically process only the data that is relevant, while ignoring the mountains of data that is not. A robot must have some way to localize its own position, but must be prepared to maintain this state even if any given sensor may not work. A robot must have some way of changing its position, but must be able to perceive exactly how its position is actually shifting. A fully autonomous robot must be able to make decisions to change its position based on the world it perceives; it should be noted, however, that this project does not include artificial intelligence within its scope.

Robotic Operating System

ROS is a unique open source platform for communication, data acquisition, image processing, modeling and other features required for robotic applications. It is highly reliable and has a impressive speed of response. In this application, the communication feature of ROS is utilized which makes it faster to communicate with the vehicle.

While transmitting control signals from base station to vehicle, the ROS in base station acts as a publisher and the ROS at on-board computer acts as a subscriber.

While transmitting sensor feedback from vehicle to base station, the ROS in base station acts as a subscriber and the ROS at on-board computer acts as a publisher.

Image Processing

Image processing is done using OpenCV which is a open source software. Various image processing techniques such as pattern matching, edge detection and other morphological operations. Vision system also generates a map that can be used for future reference so that the Artificial Neural Network can use it to take decisions on which path to take in the future. Stereo vision system is used for finding distance between object and vehicle using triangulation method which gives us the feedback for navigation. The vision system uses two cameras in the same plane.
 
posted @ 2018-10-15 20:52  三才  阅读(135)  评论(0编辑  收藏  举报