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This article describes the robot Stanley, which won the 2005 DARPA Grand Challenge. Stanley was developed for high-speed desert driving without human intervention. The robot’s software system relied predominately on state-of-the-art AI technologies, such as machine learning and probabilistic reasoning. This article describes the major components of this architecture, and discusses the results of the...
This article presents a robust approach to navigating at high-speed across desert terrain. A central theme of this approach is the combination of simple ideas and components to build a capable and robust system. A pair of robots were developed which completed a 212 kilometer Grand Challenge desert race in approximately seven hours. A path-centric navigation system uses a combination of LIDAR and RADAR...
Kat-5 was the fourth vehicle to make history in DARPA’s 2005 Grand Challenge, where for the first time ever, autonomous vehicles were able to travel through 100 miles of rough terrain at average speeds greater than 15 mph. In this paper, we describe the mechanisms and methods that were used to develop the vehicle. We describe the main hardware systems with which the vehicle was outfitted for navigation,...
The TerraMax vehicle is based on Oshkosh Truck’s Medium Tactical Vehicle Replacement (MTVR) truck platform and was one of the 5 vehicles able to successfully reach the finish line of the 132 miles DARPA Grand Challenge desert race. Due to its size (30,000 pounds, 27′-0″ long, 8′-4″ wide, and 8′-7″ high) and the narrow passages, TerraMax had to travel slowly, but its capabilities demonstrated the maturity...
There are two commonly accepted paradigms for organizing intelligence in robotic vehicles, namely, reactive and deliberative. Although these paradigms are well known to researchers, there are few published examples directly comparing their development and application on similar vehicles operating in similar environments. Virginia Tech’s participation, with two nearly identical vehicles in the DARPA...
This paper describes one aspect of our approach in developing an intelligent off-road autonomous vehicle, the Intelligent Off-road Navigator (ION), as team Desert Buckeyes from the Ohio State University for the DARPA Grand Challenge 2005. The real-time navigation is one of the critical components in an autonomous ground vehicle system. In this paper, we focus on the navigation module, whose main responsibility...
This paper presents the Golem Group/UCLA entry to the 2005 DARPA Grand Challenge competition. We describe the main design principles behind the development of Golem 2, the race vehicle. The subsystems devoted to obstacle detection, avoidance, and state estimation are discussed in more detail. An overview of the vehicle performance in the field is provided, including successes together with an analysis...
CajunBot, an autonomous ground vehicle and a finalist in the 2005 DARPA Grand Challenge, is built on the chassis of MAX IV, a six-wheeled ATV. Transformation of the ATV to an AGV (Autonomous Ground Vehicle) required adding drive-by-wire control, LIDAR sensors, an INS, and a computing system. Significant innovations in the core computational algorithms include an obstacle detection algorithm that takes...
This paper presents a summary of SciAutonics-Auburn Engineering’s efforts in the 2005 DARPA Grand Challenge. The areas discussed in detail include the team makeup and strategy, vehicle choice, software architecture, vehicle control, navigation, path planning, and obstacle detection. In particular, the advantages and complications involved in fielding a low budget all-terrain vehicle are presented...
This paper describes the development of an autonomous vehicle system that participated in the 2005 DARPA Grand Challenge event. After a brief description of the event, the architecture, based on version 3.2 of the Department of Defense Joint Architecture for Unmanned Systems (JAUS), and the design of the system are presented in detail. In particular, the “smart sensor” concept is introduced which...
This paper describes Princeton University’s approach to the 2005 DARPA Grand Challenge, an off-road race for fully autonomous ground vehicles. The system, Prospect Eleven, takes a simple approach to address the problems posed by the Grand Challenge including obstacle detection, path planning, and extended operation in harsh environments. Obstacles are detected using stereo vision, and tracked in the...
The 2005 DARPA Grand Challenge required teams to design and build autonomous off-road vehicles capable of handling harsh terrain at high speeds while following a loosely-defined path. This paper discusses the critical subsystems of Cornell University’s entry, an autonomous Spider Light Strike Vehicle. An attitude and position estimator is presented with modifications for specific problems associated...
A real-time terrain mapping and estimation algorithm using Gaussian sum elevation densities to model terrain variations in a planar gridded elevation model is presented. A formal probabilistic analysis of each individual sensor measurement allows the modeling of multiple sources of error in a rigorous manner. Measurements are associated to multiple locations in the elevation model using a Gaussian...
This paper describes the implementation and testing of Alice, the California Institute of Technology’s entry in the 2005 DARPA Grand Challenge. Alice utilizes a highly networked control system architecture to provide high performance, autonomous driving in unknown environments. Innovations include a vehicle architecture designed for efficient testing in harsh environments, a highly sensory-driven...
The MITRE Meteor team fielded an autonomous vehicle that competed in DARPA’s 2005 Grand Challenge race. This paper describes the team’s approach to building its robotic vehicle, the vehicle and components that let the vehicle see and act, and the computer software that made the vehicle autonomous. It presents how the team prepared for the race and how their vehicle performed.
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