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.
Contents:
- 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
Readership: Robotics engineers.
