Storing and predicting dynamic attributes in a world model knowledge store.

摘要

The world is an ever-changing, dynamic environment. If robots and other intelligent systems are to find ways to cope with and reason about the world adequately, they must be capable of understanding these dynamic features. This work examines the need for a centralized knowledge store capable of storing information that is both spatial and temporal in nature. The interface of a new and unique architecture to handle the exchange of dynamic information and questions about the future state of that information is presented. A novel algorithm, called the Statistics-Based Nth Order Polynomial Predictor (SNOPP), is also developed which allows state prediction of almost any time-variant data.;Each of these contributions is demonstrated through the use of a reference implementation. The author’s reference implementation is done using the Joint Architecture for Unmanned Systems (JAUS), a widely accepted, open robotics architecture developed for use in defense programs.;The architecture and predictor are tested using a real-world sensor algorithm deployed on an autonomous vehicle at the University of Florida’s Center for Intelligent Machines and Robotics (CIMAR). Findings and results of these tests are given which examine the behavior of the architecture and novel prediction algorithm in a variety of scenarios involving different time-variant data types.;The Dynamic World Model architecture and the SNOPP algorithm provide significant contributions to the future of robotics. Many robotic problems, including decision making, health monitoring and path planning, stand to benefit from better understanding of the dynamic nature of both the robot and its environment. This dissertation provides a framework in which many of these and other problems may be addressed and summarily solved by future robotic engineers.