Our Autonomous Navigation program teaches the principles and technologies behind self-navigating systems, with a focus on drones, robots, and autonomous vehicles. Students learn how to design systems that perceive, plan, and act in dynamic environments.
Key Topics Covered
- Perception for Navigation: SLAM, visual odometry, and environment mapping.
- Path Planning & Obstacle Avoidance: A*, RRT, and reinforcement learning-based planners.
- Control Systems: PID controllers, model predictive control, and trajectory optimization.
- Real-World Deployment: Testing in GPS-denied environments and adverse conditions.
Learning Outcomes
- Implement autonomous navigation systems for drones and ground robots.
- Develop real-time obstacle avoidance algorithms for dynamic environments.
- Build end-to-end autonomous pipelines from perception to control.
Who Should Join?
- Robotics engineers and autonomous systems developers.
- Researchers in drone navigation and mobile robotics.
- Students in AI, control systems, and embedded systems.
Format
- Hands-on Labs: Work with drones, ROS, and Gazebo simulators.
- Field Testing: Deploy systems in real-world environments (indoor/outdoor).
- Projects: Build a drone-based delivery system or autonomous warehouse robot.