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Internship on  Advance Robotics AI Controller Development with APSCHE and PHYTEC

Offers participants an in-depth understanding of developing AI-powered control systems for robotics applications.

Duration: 16 Weeks
Course Structure:

  • 9 Weeks: Hands-on Training

  • 6 Weeks: Project Development

  • 1 Weeks: Interview Preparation and Personality Development

Detailed Course Content

Week 1-8: Introduction to Drones and AI • Overview of Drone Technology • Drone components (sensors, motors, flight controllers, etc.) • Types of drones and their applications • Introduction to AI in Drones • Role of AI in autonomous systems • Key AI concepts: Machine Learning, Computer Vision, and Reinforcement Learning Week 2: • Tools and Frameworks • Python, ROS (Robot Operating System), and simulation tools (Gazebo, AirSim) • Introduction to TensorFlow/PyTorch for AI development • Assignment: Set up development environment and simulate a basic drone flight. Week 3: Drone Dynamics and Control Systems • Drone Physics and Dynamics • Kinematics and dynamics of quadcopters • Flight control principles (PID controllers) • Control Algorithms • Basics of PID tuning and stability analysis • Introduction to advanced control methods (MPC, LQR) • Lab: Implement and tune a PID controller in a simulation. Week 4: AI for Autonomous Navigation • Path Planning and Obstacle Avoidance • Algorithms: A*, RRT, Dijkstra • AI-based path optimization • Sensor Integration • LiDAR, IMU, GPS, and camera data fusion • Lab: Implement a basic path-planning algorithm in a simulated environment. Week 5: Computer Vision for Drones • Introduction to Computer Vision • Object detection, tracking, and recognition • OpenCV and YOLO for real-time image processing • Applications in Drones • Vision-based navigation and landing • Obstacle detection using cameras • Lab: Develop a computer vision module for object detection. Week 6: Machine Learning for Drone Control • Reinforcement Learning (RL) for Drone Control • Basics of RL and Q-learning • Training drones in simulated environments • Supervised Learning for Sensor Data • Predictive modeling for flight stability • Lab: Train a drone to perform a simple task using RL. Week 7: Advanced AI Techniques • Deep Learning for Autonomous Systems • Neural networks for decision-making • Transfer learning for drone applications • Swarm Intelligence • Multi-drone coordination and communication • Lab: Implement a neural network for autonomous decision-making. Week 8: Real-World Integration and Testing • Hardware-Software Integration • Deploying AI models on drone hardware • Real-time testing and debugging • Challenges in Real-World Deployment • Latency, power consumption, and environmental factors • Lab: Test AI controller on a physical drone.

Week 9-16: Capstone Project and Presentation • Capstone Project • Develop an end-to-end AI controller for a specific drone application (e.g., delivery, surveillance, agriculture) • Integrate all learned concepts: control systems, computer vision, and machine learning • Final Presentation • Demonstrate the project and present findings • Documentation and code submission

Key Deliverables • Weekly lab assignments and mini-projects • A fully functional AI-controlled drone system (simulated or real-world) • Final report and presentation

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