Course overview
Build your career in Autonomous software engineering
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The autonomous vehicle algorithm engineering course is available to learn online. The course duration is 9 months and is limited to 5 participants intake.
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Applications are now accepted for freshers or experienced up to 2 years, should be from the background of studies (BE/BTech/MTech) in Mechanical/Automotive/Electronics & Electrical are accepted. (Program is also applicable for students who are planning to take up master's abroad in Automotive/Autonomous engineering)
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Participants can expect to get placed at OEM / Tier 1 / Tier 2 and at Engineering service companies. *** Average salary of placed students varied from 3 lakh to 6 lakh per annum.
Key highlights of the course
Build projects to gain similar industry experience
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The course is taught practically with the help of tools & software’s used in industry
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The course consists of 12+ minor projects & 3 majors projects
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The course will cover the complete life cycle of from software development, hardware configuration to calibration, testing & validation
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The course has the extensive depth of curriculum to meet any job profile for 0 to 2 years of experience bandwidth
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Course graduates will be supported to start a career in core sector (100% placement)
Course curriculum
- Programming tool, workbenches & OS foundation
- Python
- Robotic Operating System (ROS)
- Ubuntu
- Expertise ROS
- ROS framework
- ROS topics and Nodes
- Robot links and joints
- Forward and Inverse Kinematics
- Publisher and Subscriber programming
- Foundation to Rviz & Gazebo
- Forward and Inverse Kinematics programming of UR5
- Forward and Inverse Kinematics programming of Delta robot
- Localisation
- Types of sensors
- Odometry sensor and its parameters
- Joint Distribution & Conditional Probability
- Baye's filter 1D localisation
- 2D Baye's filter localisation
- Particle filter localisation
- Mapping
- Laser sensor and its parameters
- Occupancy grid mapping
- Bresenham's line algorithm
- Control system engineering foundation
- Practical design of PID controllers
- Foundation to MPC
- PID controller for tank water level control
- PID controller for furnace temperature
- RLC circuit tuning with PID controls
- Pitch angle control system for drone
- Motion Planning
- Path Planning
- Grid based representation of environment
- Dijkstra’s algorithm
- A-star algorithm
- RRT and RRT* algorithm
- PID based motion control
- Programming Dijkstra’s algorithm for a maze
- Programming A-star algorithm for a maze
- Programming RRT and RRT* algorithm in a given environment
- Programming and tuning PID controller for mobile robot
- Foundation to Machine learning
- Linear Regression and Curve Fitting
- Logistic Regression
- Decision trees
- Random Forest
- K-Nearest algorithm
- K Means
- Reinforcement Learning
- Q Learning
- Predictive Maintenance
- Data Wrangling
- Confusion Matrix, F1-Score, ROC & ROS-AUC curve
- Programming Linear Regression for salary prediction
- Programming Logistic regression for job prediction
- Movie decision making based on decision tree
- Number recognition based on random forest
- Flower species classification based on K-Nearest Neighbour
- Flower cluster classification based on K means
- Programming reinforcement learning for material transportation
- Machine Learning based Predictive Maintenance of Aircraft Engine
- Computer vision
- Foundation to Open CV
- Lane Detection
- Canny Edge Detection
- Hough Transformation on an image
- Image Distortion
- Programming Lane detection algorithm
- Programming Canny edge detection algorithm
- Neural networks
- Foundation to TensorFlow & Keras
- Back and forward propagation
- Convolutional neural networks
- Convolution of an image
- Pooling
- rectified linear activation function
- Programming a neural network algorithm
- Programming CNN algorithm
- Building the algorithm for Self-Driving car
- Convolution of an image
- Pooling
- Rectified linear activation function
- Behavior planning
- Programming a mobile robot with three cameras
- Programming image and vehicle data collection algorithm
- Programming CNN for self-driving car steering decision
- Programming a motion control algorithm for self-driving car
- Programming behavior planning for self-driving car
- Embedded programming & hardware for autonomous vehicle
- Embedded C
- Fundamentals of circuit components
- Fundamentals of circuit design
- Micro controllers hands-on (STM/TI/NXP)
- Micro processor (Raspberry pi/Nvidia)
- Sensor data acquisition
- Camera
- LIDAR
- Sensor data processing
- Build an autonomous vehicle
- Build a AV with RGBD camera, Lidar with Nvidia JETSON computation board
- Configure power management, motors & controls
- Configure data communication between AV & computer
- Perform sensor data acquisition & filtering
- Implement localisation, mapping & motion control
- Implement computer vision
- Implement Neural networks to teach the AV
- Teach & test the AV for different track profiles
- Decision making with sensor data
- Throttle, brake & steering control of AV with ML & AI
- Teach AV at complex environments
- Industry practices
- ASPICE
- Functional safety (ISO 26262)
- Communication protocols
Master course projects
Define your experience through
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Learn ROS, Forward and Inverse Kinematics with UR5 and delta robot programming
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Baye's filter 1D localization, 2D Baye's filter localization & Particle filter localization
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Occupancy grid mapping
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Design & tune PID for projects, 1) Controller for tank water level control, 2) controller for furnace temperature, 3) RLC circuit tuning and 4)Pitch angle control system for drone
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Maze solving with Dijkstra’s & A-star algorithms, Programming RRT and RRT* algorithms, Programming and tuning PID controller for mobile robot
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Lane Detection & Canny Edge Detection
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Programming a neural network algorithm & CNN algorithm
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Programming CNN for self-driving car steering decision & Programming behavior planning for self-driving car
Master course Includes
Course fee & payment option
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Autonomous Vehicle Algorithm Engineer
10 x ₹17,700.00
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