Intelligent Navigation System of Mobile Robot.
Valentyn N Sichkar. Intelligent Navigation System of Mobile Robot // GitHub platform. DOI: 10.5281/zenodo.1317906
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Sichkar V. N. Effect of various dimension convolutional layer filters on traffic sign classification accuracy. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2019, vol. 19, no. 3, pp. DOI: 10.17586/2226-1494-2019-19-3-546-552 (Full-text available also here https://www.researchgate.net/profile/Valentyn_Sichkar)
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Sichkar V.N. Comparison analysis of knowledge based systems for navigation of mobile robot and collision avoidance with obstacles in unknown environment. St. Petersburg State Polytechnical University Journal. Computer Science. Telecommunications and Control Systems, 2018, Vol. 11, No. 2, Pp. 64–73. DOI: 10.18721/JCSTCS.11206 (Full-text available also here https://www.researchgate.net/profile/Valentyn_Sichkar)
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The investigation of Reinforcement Learning for the tasks of shortest path planning is put in separate repository and is available here: https://github.com/sichkar-valentyn/Reinforcement_Learning_in_Python
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The research results for Neural Network Knowledge Based system for the tasks of collision avoidance is put in separate repository and is available here: https://github.com/sichkar-valentyn/Matlab_implementation_of_Neural_Networks
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The study of Semantic Web languages OWL and RDF for Knowledge representation of Alarm-Warning System is put in separate repository and is available here: https://github.com/sichkar-valentyn/Knowledge_Base_Represented_by_Semantic_Web_Language
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The study of Semantic Representation of knowledge and querying of it through owl files with SPARQL is put in separate repository and is available here: https://github.com/sichkar-valentyn/System_programming_for_SPARQL_querying_with_interface_development_by_html_files
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The study of Neural Networks for Computer Vision in autonomous vehicles and robotics is put in separate repository and is available here: https://github.com/sichkar-valentyn/Neural_Networks_for_Computer_Vision
Hardware - Arduino Mega, Motor Shield L298P, DC Motors, Ultrasonic Sensors, Gyroscope, Laser Sensors, Cameras, Lidar Sensor, Bluetooth Module, Batteries, Six Wheel High Pass Base with Active Suspension.
Software - C# via Visual Studio, Python, Arduino IDE, Android SDK, Matlab.
Development - Algorithms for Overcoming Obstacles, Algorithms for Localization, Algorithms for Mapping, SLAM Algorithms.
Codes (it'll send you to appropriate folder):
Experimental results (figures and tables on this page):
- Introduction
- Connecting DC Motors
- More information about equipment
- Adding FIVE Ultrasonic sensors US-015
- Checking the abilities to stop before the possible collisions with obstacles
- Adding TEN Ultrasonic sensors HC-SR04
- Connecting two Arduino Mega together
- Checking the abilities to overcome obstacles
Explaining the main goals of the Project
Connecting and checking the High Pass Six Wheel Base - HPSWB - for simple commands to move
General view of the Motor Shield L298P is shown below on the figure
The view from the top of Motor Shield L298P and showing the main connectors that are needed for the Project.
General view of the DC Motor
Connection DC Motors to the Shield
General view of the Bluetooth Module HC-06
Connection Bluetooth Module HC-06 to the Shield or Arduino
More about equipment
General view of the Ultrasonic Sensor US-015
Connection Ultrasonic Sensor US-015 (or HC-SR04) to the Arduino
Equations for Ultrasonic Sensors, explaining how they work
Checking the environment around with Ultrasonic Sensors US-015
HPSWB with Ultrasonic Sensors - view from the front
HPSWB with Ultrasonic Sensors - view from the back
HPSWB with Ultrasonic Sensors - view from one side
Figure below shows the results of working system in Real Time by SPARQL Querying of the Knowledge Base
This figure shows the results of Neural Network Knowledge Base
With the help of Ultrasonic Sensors and seeing the obstacles to avoid the collisions
Checking the environment around with Ten Ultrasonic Sensors HC-SR04
HPSWB - view from the front
The way how to connect Master and Slave Arduino Mega together through Serial Port
Implementing and testing Algorithms for HPSWB
Valentyn N Sichkar. Intelligent Navigation System of Mobile Robot // GitHub platform. DOI: 10.5281/zenodo.1317906