Distributed Intelligent Systems: Kalman Filters and Flocking

Related lectures (31)

Page 1 of 4

Explores robot localization through positioning systems, odometry, sensor fusion, and error modeling.

Covers the theory and application of Kalman Filters for estimation and localization in robotics.

Explores error sources in odometry, sensor fusion, feature-based localization, and the Kalman Filter algorithm.

Delves into the collective behavior of schooling fish and the impact of boundary effects on group dynamics.

Introduces the rigorous approach to sensor fusion for modern applications.

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

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

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

Explores compliant control for robots through impedance and variable stiffness, enabling safe and adaptive interactions with the environment.

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