by John E Mclnroy (University of Wyoming, USA) , Joseph C Musto (W H Brady Co., USA) , & George N Saridis (Rensselaer Polytechnic Institute, USA)

This book derives techniques which allow reliable plans to be automatically selected by Intelligent Machines. It concentrates on the uncertainty analysis of candidate plans so that a highly reliable candidate may be identified and used. For robotic components, such as a particular vision algorithm for pose estimation or a joint controller, methods are explained for directly calculating the reliability. However, these methods become excessively complex when several components are used together to complete a plan. Consequently, entropy minimization techniques are used to estimate which complex tasks will perform reliably. The book first develops tools for directly calculating the reliability of sub-systems, and methods of using entropy minimization to greatly facilitate the analysis are explained. Since these sub-systems are used together to accomplish complex tasks, the book then explains how complex tasks can be efficiently evaluated.

• Selecting Reliable Plans: An Introduction
• Calculating Reliability in Multi-Dimensional Systems
• A Review of Entropy Methods
• Application to Pose Algorithms
• Reliability Optimization of Single-Input Control Systems
• Reliability Estimation Techniques
• Reliability Estimation for Complex Tasks and Systems
• Case Study: Robotic Assembly System
• Reflections on the State of the Art