MICRO-452: Basics of mobile robotics

Lectures in this course (9)

Covers the components and challenges of mobile robot control, including mechanics, locomotion, sensors, and architectures.

Covers the basics of computer vision, focusing on image processing techniques and feature detection.

Explores machine learning for feature extraction, 3D vision, and neural networks in mobile robotics.

Covers obstacle avoidance and navigation strategies in mobile robotics using sensors and potential fields.

Covers global navigation, path planning algorithms, cell decomposition, potential field methods, and stigmergy-based path optimization.

Covers the basics of mobile robotics focusing on localization, discussing sensors, uncertainty handling, GPS, and motion models.

Explores the basics of mobile robotics, emphasizing uncertainties and the Bayes filter algorithm.

Explores the fundamentals of mobile robotics, emphasizing uncertainties in localization, sensor fusion, and the Extended Kalman Filter.

Explores the basics of Simultaneous Localization and Mapping (SLAM), focusing on map creation techniques and prediction of measurements.

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