Day 1: Introduction to Advanced AI Techniques in Robotics

Session 1: Overview of AI in Robotics

• Evolution of AI in robotics: from rule-based systems to autonomous agents
• Key AI components: perception, planning, and control
• Real-world applications: autonomous vehicles, drones, and industrial robots

Session 2: Fundamentals of Path Planning

• Introduction to path planning algorithms: graph-based vs. sampling-based methods
• Shortest path algorithms: Dijkstra’s algorithm, A*, and their applications in robotics
• Case study: Path planning for autonomous delivery robots

Session 3: Hands-on Lab: Implementing A Algorithm for Path Planning*

• Setting up Python and basic simulation environment
• Coding A* for 2D grid-based environments
• Practical exercise: Finding optimal paths for a robot navigating a maze

Session 4: Probabilistic Roadmaps (PRM) and Rapidly-exploring Random Trees (RRT)

• Understanding sampling-based planning algorithms
• Applications in high-dimensional spaces and complex environments
• Case study: RRT for path planning in drone navigation

Session 5: Hands-on Lab: Path Planning with PRM and RRT

• Implementing PRM and RRT algorithms in Python
• Simulating path planning for robots in dynamic environments
• Practical exercise: Navigating complex terrains with obstacles

Day 2: Motion Planning and Trajectory Generation

Session 1: Introduction to Motion Planning

• Difference between path planning and motion planning
• Trajectory generation techniques: polynomial trajectories, Bézier curves
• Case study: Motion planning for robotic arms in manufacturing

Session 2: Hands-on Lab: Trajectory Generation for Mobile Robots

• Implementing trajectory generation algorithms in Python
• Simulating smooth trajectories for differential drive robots
• Practical exercise: Creating collision-free trajectories in Gazebo

Session 3: Optimization-Based Motion Planning

• Introduction to optimization techniques for robotics
• Model Predictive Control (MPC) for trajectory optimization
• Case study: MPC for autonomous vehicle lane-keeping

Session 4: Hands-on Lab: Implementing Model Predictive Control (MPC)

• Setting up MPC for real-time control in robotic simulations
• Optimizing robot movements for efficiency and stability
• Practical exercise: Using MPC for dynamic obstacle avoidance

Session 5: Kinematics and Dynamics in Robotics

• Forward and inverse kinematics for robotic manipulators
• Dynamics modeling: Newton-Euler and Lagrangian approaches
• Case study: Dynamic motion control in bipedal robots

Day 3: Reinforcement Learning for Robotic Control

Session 1: Introduction to Reinforcement Learning (RL) in Robotics

• Understanding RL concepts: agents, environments, rewards, policies
• Applications of RL in path planning, navigation, and manipulation
• Case study: RL for robotic soccer-playing agents

Session 2: Hands-on Lab: Implementing Q-Learning for Robotic Navigation

• Setting up OpenAI Gym for RL experiments
• Coding Q-learning algorithms for simple robotic tasks
• Practical exercise: Training a robot to navigate through dynamic obstacles

Session 3: Deep Reinforcement Learning (DRL) for Complex Environments

• Introduction to Deep Q-Networks (DQN) and policy gradient methods
• Applications of DRL in autonomous vehicles and robotic arms
• Case study: DRL for robotic grasping and manipulation

Session 4: Hands-on Lab: Building a Deep Q-Network (DQN) for Robot Control

• Implementing DQN with TensorFlow or PyTorch
• Training DRL models for complex robotic environments
• Practical exercise: Training a robotic arm to pick and place objects

Session 5: Challenges in RL for Robotics

• Sample efficiency, exploration-exploitation trade-off, and reward design
• Strategies for improving learning efficiency and generalization
• Case study: Transfer learning in reinforcement learning for robotics

Day 4: Real-Time Decision-Making and Control Systems

Session 1: Real-Time AI Systems for Robotics

• Requirements for real-time robotic systems
• AI-based decision-making under uncertainty
• Case study: Real-time decision-making in autonomous drones

Session 2: Hands-on Lab: Real-Time Robot Control with ROS

• Setting up Robot Operating System (ROS) for real-time control
• Integrating AI algorithms with ROS for real-time applications
• Practical exercise: Building a real-time obstacle avoidance system

Session 3: Sensor Fusion for Reliable Decision-Making

• Combining data from LiDAR, cameras, IMUs, and GPS
• Kalman filters and particle filters for sensor fusion
• Case study: Sensor fusion in autonomous vehicle navigation

Session 4: Hands-on Lab: Implementing Sensor Fusion with ROS and Python

• Fusing multiple sensor data streams for robust robot perception
• Practical exercise: Enhancing navigation performance using sensor fusion

Session 5: Robustness and Safety in AI-Driven Robotics

• Ensuring safety and reliability in AI-controlled robots
• Fault detection, error handling, and fail-safe mechanisms
• Ethical considerations in autonomous decision-making

Day 5: Capstone Project: Design and Implementation of an AI-Powered Robotic System

Session 1: Capstone Project Briefing and Team Formation

• Introduction to the capstone project: Designing an AI-driven robotic system for real-world applications
• Forming project teams and defining project goals

Session 2: Capstone Project Work (Hands-on)

• Developing AI algorithms for path planning, motion control, and decision-making
• Integrating machine learning models with ROS and simulation environments
• Implementing real-time control systems with sensor fusion

Session 3: Project Presentations

• Team presentations showcasing AI-powered robotic solutions
• Demonstrating functionality, performance, and real-time decision-making capabilities
• Peer feedback and expert evaluation

Session 4: Lessons Learned and Best Practices for AI in Robotics

• Key takeaways from the course: AI algorithms, path planning, control systems, and integration
• Best practices for deploying AI in real-world robotics applications
• Group discussion: The future of AI-driven robotics

Session 5: Course Wrap-Up and Final Q&A

• Recap of key concepts: advanced AI techniques, path planning, and motion control
• Final Q&A session to address participants’ specific questions
• Resources for continuous learning in AI, robotics, and automation