Defining problem and model so| ution: Minimizing localization error Comb imize gain in explored map bined Information Utilities Integrated Adaptive Information-based Exploration Algorithm

Vision-based SLAM Mobile Robot Localization And Mapping With Uncertainty using Scale-Invariant Visual Landmarks -e,lowe, Little Vikash Mansinghka Spren Riisgaard Outline

Outline Model-based programming The need for model-based reactive planning The Burton model-based reactive planner Artificial Intelligence Space Systems

Massachusetts Institute of Technology 16.412/6.834 Cognitive Robotics Distributed: Monday, 3/31/04 Objective The purpose of the following handout is to walk you step by step through the execution of the FF planning algorithm, on a simple example. The FF algorithm is presented in the paper:

Cognitive Robotics Outline Temporal Action Graph Walksat: Stochastic Local Search Better Neighbor Relaxed Plan A. Gerevini, A. Saetti, I. Serina \Planning through Stochastic Local Search and Temporal Action Graphs\, to appear in Journal of Artificial Intelligence

Readings in Planning as Forward Heuristic Search \The FF Planning System: Fast Plan Generation Through Heuristic Search,\ by Jorg Hoffmann and Bernhard Nebel, Journal of Artificial Intelligence Research, 2001. “Planning as Heuristic Search,\ by Blai Bonet and Hector Geffner, Artificial

Hybrid Mode Estimation and Gaussian Filtering with Hybrid HMMs Stanislav funiak 16. 412/6.834 Lecture, 15 March 2004 References Hofbaur, M. W, and Williams, B C(2002). Mode estimation of probabilistic hybrid systems. In: Hybrid Systems: Computation and Control Hscc 2002 Funiak, S, and Williams, B. C (2003 ). Multi-modal particle filtering for hybrid systems with autonomous mode transitions. In: DX-2003 SafeProcess 2003 Lerner, U.,R. Parr, D. Koller and G. Biswas(2000). Bayesian fault detection and diagnosis in dynamic

Brian C Williams, copyright 2000 Mode estimation Mode reconfiguration Select a most likely set of Select a least cost set of component modes that are commandable component consistent with the model and modes that entail the current observations

Conflicts and Kernel Diagnoses Generating Kernels from Conflicts Finding Consistent Modes Estimating Likely Modes Conflict-directed

Foundations of state Estimation Topics: Bayes Filters Kalman filters Hidden markov models Additional readins G. Welch and G. Bishop. An Introduction to the Kalman Filter \. University of North Carolina at Chapel Hill