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.

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Abstract-Mobile robots often operate in domains that are only incompletely known, for example, when they have to move from given start coordinates to given goal coordinates in unknown terrain. In this case, they need to be able to replan quickly as their knowledge of the terrain changes. Stentz' Focussed Dynamic A (D ) is a heuristic search method that repeatedly determines a shortest path from the current robot coordinates to the goal coordinates while the robot moves along the path. It is able to replan faster than planning from scratch since it modifies its previous search results locally. Consequently, it has been extensively used in mobile robotics. In this article, we introduce an alternative to D that determines the same paths and thus moves the robot in the same way but is algorithmically different. D Lite is simple, can be rigorously analyzed, extendible in multiple ways, and is at least as efficient as D . We believe that our results will make D -like replanning methods even more popular and enable robotics researchers to adapt them to additional applications.Index Terms-A , D (Dynamic A ), navigation in unknown terrain, planning with the freespace assumption, replanning, search, sensor-based path planning.

Abstract-Robotic path planning in static environments is a thoroughly studied problem that can typically be solved very efficiently. However, planning in the presence of dynamic obstacles is still computationally challenging because it requires adding time as an additional dimension to the search-space explored by the planner. In order to avoid the increase in the dimensionality of the planning problem, most real-time approaches to path planning treat dynamic obstacles as static and constantly re-plan as dynamic obstacles move. Although gaining efficiency, these approaches sacrifice optimality and even completeness. In this paper, we develop a planner that builds on the observation that while the number of safe timesteps in any configuration may be unbounded, the number of safe time intervals in a configuration is finite and generally very small. A safe interval is a time period for a configuration with no collisions and if it were extended one timestep in either direction, it would then be in collision. The planner exploits this observation and constructs a search-space with states defined by their configuration and safe interval, resulting in a graph that generally only has a few states per configuration. On the theoretical side, we show that our planner can provide the same optimality and completeness guarantees as planning with time as an additional dimension. On the experimental side, in simulation tests with up to 200 dynamic obstacles, we show that our planner is significantly faster, making it feasible to use in real-time on robots operating in large dynamic environments. We also ran several real robot trials on the PR2, a mobile manipulation platform.