Intelligent Unmanned Ground Vehicles describes the technology developed and the results obtained by the Carnegie Mellon Robotics Institute in the course of the DARPA Unmanned Ground Vehicle (UGV) project. The goal of this work was to equip off-road vehicles with computer-controlled, unmanned driving capabilities. The book describes contributions in the area of mobility for UGVs including: tools for assembling complex autonomous mobility systems; on-road and off-road navigation; sensing techniques; and route planning algorithms. In addition to basic mobility technology, the book covers a number of integrated systems demonstrated in the field in realistic scenarios. The approaches presented in this book can be applied to a wide range of mobile robotics applications, from automated passenger cars to planetary exploration, and construction and agricultural machines. Intelligent Unmanned Ground Vehicles shows the progress that was achieved during this program, from brittle specially-built robots operating under highly constrained conditions, to groups of modified commercial vehicles operating in tough environments. One measure of progress is how much of this technology is being used in other applications. For example, much of the work in road-following, architectures and obstacle detection has been the basis for the Automated Highway Systems (AHS) prototypes currently under development. AHS will lead to commercial prototypes within a few years. The cross-country technology is also being used in the development of planetary rovers with a projected launch date within a few years. The architectural tools built under this program have been used in numerous applications, from an automated harvester to an autonomous excavator. The results reported in this work provide tools for further research development leading to practical, reliable and economical mobile robots.

Inhaltsangabe1. Introduction; M. Hebert, et al. 2. A Behavior-based Architecture for Mobile Navigation; J.K. Rosenblatt, C.E. Thorpe. 3. SAUSAGES: Between Planning and Action; J. Gowdy. 4. Neural Network Vision for Robot Driving; D. Pomerleau. 5. Vision-Based Neural Network Road and Intersection Detection; T. Jochem, D. Pomerleau. 6. SMARTY: Point-Based Range Processing for Autonomous Driving; M. Hebert. 7. RANGER: Feedforward Control Approach to Autonomous Navigation; A. Kelly. 8. Sensor Fusion for Autonomous Navigation Using Neural Networks; I.L. Davis, A. Stentz. 9. Sonar-Based Outdoor Vehicle Navigation; D. Langer, C.E. Thorpe. 10. STRIPE: Low-Bandwidth and High-Latency Teleoperation; J. Kay, C.E. Thorpe. 11. Optimal and Efficient Path Planning for Partially Known Environments; A. Stentz. 12. Dynamic Mission Planning for Multiple Mobile Robots; B. Brumitt, A. Stentz. 13. Integrating Position Estimation and Perception for Navigation; C.E. Thorpe, et al. 14. An Integrated System for Autonomous Off-Road Navigation; D. Langer, et al. 15. A Navigation System for Goal Acquisition in Unknown Environments; A. Stentz, M. Hebert. Subject Index.