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.

Abstract-We present a novel approach for determining robot movements that efficiently accomplish the robot's tasks while not hindering the movements of people within the environment. Our approach models the goal-directed trajectories of pedestrians using maximum entropy inverse optimal control. The advantage of this modeling approach is the generality of its learned cost function to changes in the environment and to entirely different environments. We employ the predictions of this model of pedestrian trajectories in a novel incremental planner and quantitatively show the improvement in hindrancesensitive robot trajectory planning provided by our approach. I. INTRODUCTIONDetermining appropriate robotic actions in environments with moving people is a well-studied [15], [2], [5], but often difficult task due to the uncertainty of each person's future behavior. Robots should certainly never collide with people [11], but avoiding collisions alone is often unsatisfactory because the disruption of almost colliding can be burdensome to people and sub-optimal for robots. Instead, robots should predict the future locations of people and plan routes that will avoid such hindrances (i.e., situations where the person's natural behavior is disrupted due to a robot's proximity) while still efficiently achieving the robot's objectives. For example, given the origins and target destinations of the robot and person in Figure 1, the robot's hindrance-minimizing trajectory would take the longer way around the center obstacle (a table), leaving a clear path for the pedestrian.One common approach for predicting trajectories is to project the prediction step of a tracking filter [9], [13], [10] forward over time. For example, a Kalman filter's [7] future positions are predicted according to a Gaussian distribution with growing uncertainty and, unfortunately, often high probability for physically impossible locations (e.g., behind walls, within obstacles). Particle filters [16] can incorporate more sophisticated constraints and non-Gaussian distributions, but degrade into random walks of feasible motion over large time horizons rather than purposeful, goal-based motion. Closer to our research are approaches that directly model the policy [6]. These approaches assume that previously observed trajectories capture all purposeful behavior, and the only uncertainty involves determining to which previously observed class of trajectories the current behavior belongs. Models based on mixtures of trajectories and conditioned action distribution modeling (using hidden Markov models) have been employed [17]. This approach often suffers from over-fitting to the particular training trajectories and context of those trajectories. When changes to the environment occur (e.g., rearrangement of the furniture), the model will confidently predict incorrect trajectories through obstacles.

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.

The summit of Mauna Kea in Hawaii, the area near Cerro Chajnantor in Chile, and the South Pole are sites of large millimeter or submillimeter wavelength telescopes. We have placed 860 GHz sky brightness monitors at all three sites and present a comparative study of the measured submillimeter brightness due to atmospheric thermal emission. We report the stability of that quantity at each site.1.