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Course Outline
Introduction to AI and Robotics
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Overview of the convergence between modern robotics and AI.
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Applications in autonomous systems, drones, and service robots.
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Key AI components: perception, planning, and control.
Setting Up the Development Environment
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Installing Python, ROS 2, OpenCV, and TensorFlow.
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Using Gazebo or Webots for robot simulation.
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Working with Jupyter Notebooks for AI experiments.
Perception and Computer Vision
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Using cameras and sensors for perception.
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Image classification, object detection, and segmentation using TensorFlow.
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Edge detection and contour tracking with OpenCV.
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Real-time image streaming and processing.
Localization and Sensor Fusion
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Understanding probabilistic robotics.
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Kalman Filters and Extended Kalman Filters (EKF).
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Particle Filters for non-linear environments.
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Integrating LiDAR, GPS, and IMU data for localization.
Motion Planning and Pathfinding
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Path planning algorithms: Dijkstra, A*, and RRT*.
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Obstacle avoidance and environment mapping.
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Real-time motion control using PID.
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Dynamic path optimization using AI.
Reinforcement Learning for Robotics
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Fundamentals of reinforcement learning.
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Designing reward-based robotic behaviours.
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Q-learning and Deep Q-Networks (DQN).
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Integrating RL agents in ROS for adaptive motion.
Simultaneous Localisation and Mapping (SLAM)
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Understanding SLAM concepts and workflows.
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Implementing SLAM with ROS packages (gmapping, hector_slam).
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Visual SLAM using OpenVSLAM or ORB-SLAM2.
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Testing SLAM algorithms in simulated environments.
Advanced Topics and Integration
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Speech and gesture recognition for human-robot interaction.
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Integration with IoT and cloud robotics platforms.
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AI-driven predictive maintenance for robots.
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Ethics and safety in AI-enabled robotics.
Capstone Project
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Design and simulate an intelligent mobile robot.
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Implement navigation, perception, and motion control.
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Demonstrate real-time decision-making using AI models.
Summary and Next Steps
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Review of key AI robotics techniques.
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Future trends in autonomous robotics.
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Resources for continued learning.
Requirements
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Programming experience in Python or C++.
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Fundamental understanding of computer science and engineering principles.
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Familiarity with probability concepts, calculus, and linear algebra.
Audience
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Engineers.
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Robotics enthusiasts.
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Researchers in automation and AI.
21 Hours
Testimonials (1)
its knowledge and utilization of AI for Robotics in the Future.
