Autonomous Systems
Master Autonomous Robotics with a focus on navigation, perception, and AI-driven decision-making.
This program equips students with knowledge of sensors, control systems, machine vision, and path planning for autonomous vehicles and robots. Learn to design, simulate, and deploy autonomous systems using ROS, Python, and AI framework
₹4,000 + 18% GST
Week 1: Fundamentals of Autonomous Systems
- Introduction to Autonomous Vehicles & Robots
- Levels of Autonomy (from driver-assist to fully autonomous)
- Sensors & Perception Overview (LiDAR, Camera, GPS, IMU)
- Control Systems Basics for Autonomy
👉 Hands-on: Set up ROS environment & simulate a simple autonomous robot
- Levels of Autonomy (from driver-assist to fully autonomous)
- Sensors & Perception Overview (LiDAR, Camera, GPS, IMU)
- Control Systems Basics for Autonomy
👉 Hands-on: Set up ROS environment & simulate a simple autonomous robot
Week 2: Perception & Sensor Fusion
- Computer Vision basics (OpenCV, image preprocessing)
- LiDAR point cloud processing
- Sensor fusion techniques (Kalman Filter, Extended Kalman)
- SLAM (Simultaneous Localization and Mapping) introduction
👉 Hands-on: Build a simple SLAM map using ROS & LiDAR data
- LiDAR point cloud processing
- Sensor fusion techniques (Kalman Filter, Extended Kalman)
- SLAM (Simultaneous Localization and Mapping) introduction
👉 Hands-on: Build a simple SLAM map using ROS & LiDAR data
Week 3: Path Planning & Navigation
- Path planning algorithms: A*, Dijkstra, RRT
- Obstacle detection & avoidance
- Motion control for navigation
- GPS-based navigation & localization
👉 Hands-on: Implement path planning in a simulated environment (Gazebo/ROS)
- Obstacle detection & avoidance
- Motion control for navigation
- GPS-based navigation & localization
👉 Hands-on: Implement path planning in a simulated environment (Gazebo/ROS)
Week 4: AI for Autonomous Systems
- Machine Learning in autonomy (object detection, classification)
- Deep Learning models for perception (CNNs, YOLO)
- Reinforcement Learning for decision-making
- Ethical considerations in autonomous systems
👉 Hands-on: Train a model for lane detection or obstacle recognition
- Deep Learning models for perception (CNNs, YOLO)
- Reinforcement Learning for decision-making
- Ethical considerations in autonomous systems
👉 Hands-on: Train a model for lane detection or obstacle recognition
Week 5: Advanced Applications & Capstone Project
- Autonomous Cars: ADAS, lane keeping, adaptive cruise control
- Drones & UAV autonomy basics
- Industrial & warehouse autonomous robots (AGVs, AMRs)
- Final integration & project development
👉 Hands-on: Capstone Project – Build a simulation of an autonomous car or warehouse robot
- Drones & UAV autonomy basics
- Industrial & warehouse autonomous robots (AGVs, AMRs)
- Final integration & project development
👉 Hands-on: Capstone Project – Build a simulation of an autonomous car or warehouse robot
#Contact Us
Tools and Platforms
ROS | Gazebo | Python | OpenCV | TensorFlow | PyTorch | MATLAB | Arduino | LiDAR & Camera Modules | CARLA Simulator
Assessment
Assignments
0
%
Capstone Project
0
%
Presentation
0
%
Quizes
0
%
Particiapation
0
%