Unmanned Ground Vehicle Program Research Articles

Intelligent Systems: Architecture, Design, and Control

From the Publisher: A cutting-edge, multidisciplinary framework for intelligent systems While intelligence is a difficult concept to comprehend, research into its mechanisms and functions is rapidly exploding in both biological and artificial systems. This book introduces an original model of intelligent systems, integrating knowledge from diverse sources and developing a general theory that can be used for the analysis of all kinds of highly complex systems, from whole economies to integrated manufacturing. Intelligence as a computational phenomenon dwells upon grouping, focusing attention, and combinatorial search as its fundamental operators. In this groundbreaking work, Alex Meystel and James Albus, two of the foremost authorities in the field, draw on two decades of testing the theory in robots and other smart machines. (Dr. Meystel developed an autonomous Dune-Buggy in 1983—1987; Dr. Albus’s work forms the basis for the control system used in the Army Demo III Experimental Unmanned Ground Vehicle program, 1997—present.) Special coverage of the NIST-RCS (Real-time Control System) model is also featured in this liberally illustrated and clearly written book. Focusing on intelligence as a computational phenomenon with a multiresolutional architecture, Intelligent Systems: Architecture, Design, and Control promises to dramatically influence future research and development in intelligence worldwide. It is a one-of-a-kind resource for scientists, engineers, and anyone wishing to explore the contemporary theory and tools of computational intelligence.

Matching horizon features using a Messy Genetic Algorithm

Three line segment matching techniques are compared on horizon line matching problems. The application is to reduce camera angle uncertainty for outdoor robots. Each of the three algorithms seeks an optimal many-to-many match between a subset of 2D line segments extracted from a rendered terrain map and segments extracted from an image. Two algorithms are non-deterministic, with one performing Random Starts Local Search and the other using a Messy Genetic Algorithm. The third algorithm searches a list of ranked key feature matches using a limited deterministic local search. The Random Starts Local Search Algorithm is the slowest of the three algorithms and fails to find the best match on 9 out of 54 problems. In contrast, the Messy Genetic Algorithm correctly solves all 54 test problems. The Key Feature Algorithm is the fastest, but it fails to find the optimal match in three cases. The results suggest the Messy Genetic Algorithm is superior to the other two algorithms on problems of this type. The test imagery was collected on one of the Unmanned Ground Vehicle Program's vehicles operating at the Lockheed Martin Demo C test site. Using the optimal matches it is shown that uncertainty in camera pointing angle is reduced from several degrees to less than a degree.

Decision-theoretic cooperative sensor planning

This paper describes a decision-theoretic approach to cooperative sensor planning between multiple autonomous vehicles executing a military mission. For this autonomous vehicle application, intelligent cooperative reasoning must be used to select optimal vehicle viewing locations and select optimal camera pan and tilt angles throughout the mission. Decisions are made in such a way as to maximize the value of information gained by the sensors while maintaining vehicle stealth. Because the mission involves multiple vehicles, cooperation can be used to balance the work load and to increase information gain. This paper presents the theoretical foundations of our cooperative sensor planning research and describes the application of these techniques to ARPA's Unmanned Ground Vehicle program.