MAS514 Introduction to Autonomous Robotics (Autumn 2025)

On successful completion of this course, students will be able to:

• Understand and apply principles of autonomous systems using the "Sense, Think, and Act" framework.
• Operate and analyze sensors and measurement techniques in robotics, including LiDAR, encoders, and IMUs.
• Model and address sensor uncertainty and signal noise through statistical techniques.
• Develop sensor-based mathematical models for localization methods such as odometry, map-based localization, and SLAM.
• Integrate multi-sensor data using advanced techniques like Kalman filters for accurate localization and navigation.
• Create and test kinematic models and motion control strategies for mobile robots and robotic manipulators.
• Design and implement motion planning algorithms for autonomous navigation.
• Utilize ROS 2 (Python or C++) for robotic system integration and development.
• Conduct experimental work using the UiAbot platform to validate system performance and refine autonomous behavior.

This course provides a comprehensive introduction to autonomous robotics, combining theoretical foundations with practical applications and hands-on development. Key topics include:

• Instrumentation and Sensor Integration (Perception): Use of sensors such as LiDAR, IMUs, and encoders for environmental perception and robot state estimation.
• Robot Kinematics (Motion Control): Modeling and controlling the motion of mobile robots and manipulators, including differential drive systems.
• Localization and Mapping: Techniques such as odometry, map-based localization, and SLAM for robot positioning.
• Navigation and Motion Planning: Algorithms for global and local path planning and obstacle avoidance.

Practical components complement the theoretical content, with hands-on work using the UiAbot platform and tools like ROS 2 (programming in Python or C++) for system design, integration, and implementation.