Boss is an autonomous vehicle that uses on-board sensors (global positioning system, lasers, radars, and cameras) to track other vehicles, detect static obstacles, and localize itself relative to a road model. A three-layer planning system combines mission, behavioral, and motion planning to drive in urban environments. The mission planning layer considers which street to take to achieve a mission goal. The behavioral layer determines when to change lanes and precedence at intersections and performs error recovery maneuvers. The motion planning layer selects actions to avoid obstacles while making progress toward local goals. The system was developed from the ground up to address the requirements of the DARPA Urban Challenge using a spiral system development process with a heavy emphasis on regular, regressive system testing. During the National Qualification Event and the 85-km Urban Challenge Final Event, Boss demonstrated some of its capabilities, qualifying first and winning the challenge. C 2008 Wiley Periodicals, Inc.

Boss is an autonomous vehicle that uses on-board sensors (global positioning system, lasers, radars, and cameras) to track other vehicles, detect static obstacles, and localize itself relative to a road model. A three-layer planning system combines mission, behavioral, and motion planning to drive in urban environments. The mission planning layer considers which street to take to achieve a mission goal. The behavioral layer determines when to change lanes and precedence at intersections and performs error recovery maneuvers. The motion planning layer selects actions to avoid obstacles while making progress toward local goals. The system was developed from the ground up to address the requirements of the DARPA Urban Challenge using a spiral system development process with a heavy emphasis on regular, regressive system testing. During the National Qualification Event and the 85-km Urban Challenge Final Event, Boss demonstrated some of its capabilities, qualifying first and winning the challenge. C 2008 Wiley Periodicals, Inc.

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 km Grand Challenge desert race in approximately 7 h. A pathcentric navigation system uses a combination of LIDAR and RADAR based perception sensors to traverse trails and avoid obstacles at speeds up to 15 m/s. The onboard navigation system leverages a human-based preplanning system to improve reliability and robustness. The robots have been extensively tested, traversing over 3500 km of desert trails prior to completing the challenge. This article describes the mechanisms, algorithms, and testing methods used to achieve this performance.

No abstract

The task of teleoperating a robot over a wireless video link is known to be very difficult. Teleoperation becomes even more difficult when the robot is surrounded by dense obstacles, or speed requirements are high, or video quality is poor, or wireless links are subject to latency. Due to high quality lidar data, and improvements in computing and video compression, virtualized reality has the capacity to dramatically improve teleoperation performance -even in high speed situations that were formerly impossible. In this paper, we demonstrate the conversion of dense geometry and appearance data, generated on-the-move by a mobile robot, into a photorealistic rendering database that gives the user a synthetic exterior line-of-sight view of the robot including the context of its surrounding terrain. This technique converts remote teleoperation into line-of-sight remote control. The underlying metrically consistent environment model also introduces the capacity to remove latency and enhance video compression. Display quality is sufficiently high that the user experience is similar to driving a video game where the surfaces used are textured with live video.