Distributed Intelligent Systems: Kalman Filters and Flocking

This lecture covers the application of Kalman filters in robot localization, fusion of sensory data, and collective movements. It explains the Kalman filter algorithm, motion model estimation, and the fusion of motion model prediction with new measurements. Additionally, it delves into non-deterministic uncertainties in wheel-based odometry and the challenges of dealing with obstacles in flocking behaviors. The lecture also explores the concept of flocking in animal societies, focusing on Reynolds' Boids algorithm for simulating collective movements. Various examples and algorithms are discussed, highlighting the importance of decentralized control and the Laplacian matrix in solving the rendezvous problem.