ROS-robot-programming-book-by-turtlebo3-developers-EN
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The high level of English is a standard for a top student.
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3. gain new knowledge by reviewing old.
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5. You quietly into my heart, but in the end, kept distance with me.
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13. We should all start to live before we get too old.
Reading books (317~409 page)
Two simulation methods of the TurtleBot3 package were introduced. One is to use RViz, a 3D visualization tool of ROS, and the other is to use Gazebo, a 3D robot simulator. Simulation is a great tool for users because it allows performing programming tasks very close to the actual environment with a robot.
What do we need to implement navigation in robots? It may vary depending on the navigation algorithm, and the followings may be required as basic features.
? Map
? Pose of Robot
? Sensing
? Path Calculation and Driving
SLAM(Simultaneous Localization And Mapping) is developed to let the robot create a map with or without a help of a human being. This is a method of creating a map while the robot explores the unknown space and detects its surroundings and estimates its current location as well as creating a map.
Paper
1. The Closed-Loop Control System Design of Hexapod Robot Autonomous Navigation Based on Fuzzy Neural Network
(http://robot.sia.cn/CN/10.13973/j.cnki.robot.170252)
Abstract: For the autonomous navigation issue of hexapod robot in unknown environment, an autonomous navigation closed-loop control algorithm based on fuzzy neural network is proposed, and a navigation control system of hexapod robot is designed according to the algorithm. The algorithm combines the logical reasoning ability of fuzzy control with the learning and training ability of neural network, and introduces the closed-loop control method into optimization. The control system is composed of four modules:information input, fuzzy neural network, instruction execution and information feedback. The perception of environment and location information is completed by GPS (global positioning system) sensor, electronic compass sensor and ultrasonic sensor. The fuzzy neural network control algorithm is reconstructed by C language and is applied to the system. The simulation results show that the closed-loop control algorithm based on fuzzy neural network is better than the open-loop control algorithm theoretically. The closed-loop control algorithm can reduce the walking distance of the hexapod in the environment with obstacles, and the walking speed is increased by 6.14%, the walking time is shortened by 8.74%. On this basis, a physical test is carried out. The experimental results show that the control system can achieve the autonomous obstacle-avoidance control of the hexapod robot. Compared with the open-loop control system, the walking path can be shortened effectively, the walking speed is increased by 5.66% and the walking time is shortened by 7.25%, which verifies the feasibility and practicability of the closed-loop control system.
Key words: hexapod robot autonomous navigation fuzzy neural network closed-loop control environmental perception
2. Deep Learning for Control: The State of the Art and Prospects
Abstract: Deep learning has shown great potential and advantage in feature extraction and model fitting. It is significant to use deep learning for control problems involving high dimension data. Currently, there have been some investigations focusing on deep learning in control. This paper is a review of related work including control object recognition, state feature extraction, system parameter identification and control strategy calculation. Besides, this paper describes the approaches and ideas of deep control, adaptive dynamic programming and parallel control related to deep learning in control. Also, this paper summarizes the main functions and existing problems of deep learning in control presents some prospects of future work. Key words Deep learning, control, feature, adaptive dynamic programming (ADP)
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