As robotic software systems become larger and more complex, it is increasingly important to reuse existing software components to control development costs. For a broad class of relatively simple components, ranging from sensor and actuation interfaces to many simple perception and navigation algorithms, this can be a reasonably straightforward process, and many excellent frameworks have been developed in recent years that support reuse of such components in novel systems. However, there is also a class of more advanced software components, such as for modeling and interacting with complex environments, for which reuse can be a much more challenging problem. In particular, many advanced robotic algorithms can be highly sensitive to the perception and actuation capabilities of the specific robots they are deployed on, in turn requiring significant and invasive modifications to accommodate the specific capabilities of a different robotic system. This work examines the nature of this sensitivity and proposes a novel design methodology for isolating a stable, reusable "core" algorithm from any platform-specific enhancements, or "supplemental" effects. Modern software engineering techniques are used to encapsulate these supplemental effects separately from the core algorithm, allowing platform-specific details to be accommodated by modular substitution instead of direct modification of the "core" component. This methodology is experimentally evaluated on existing software for autonomous driving behaviors, yielding useful insights into the creation of highly adaptable robotic software components.

This paper describes the obstacle detection and tracking algorithms developed for Boss, which is Carnegie Mellon University 's winning entry in the 2007 DARPA Urban Challenge. We describe the tracking subsystem and show how it functions in the context of the larger perception system. The tracking subsystem gives the robot the ability to understand complex scenarios of urban driving to safely operate in the proximity of other vehicles. The tracking system fuses sensor data from more than a dozen sensors with additional information about the environment to generate a coherent situational model. A novel multiple-model approach is used to track the objects based on the quality of the sensor data. Finally, the architecture of the tracking subsystem explicitly abstracts each of the levels of processing. The subsystem can easily be extended by adding new sensors and validation algorithms.

We describe an autonomous robotic software subsystem for managing mission execution and discrete traffic interaction in the 2007 DARPA Urban Challenge. Its role is reviewed in the context of the software system that controls ldquoBossrdquo, Tartan Racingpsilas winning entry in the competition. Design criteria are presented, followed by the application of software design principles to derive an architecture well suited to the rigors of developing complex robotic systems. Combined with a discussion of robust behavioral algorithms, the designpsilas effectiveness is highlighted in its ability to manage complex autonomous driving behaviors while remaining adaptable to the systempsilas evolving capabilities.

Unknown, unexplored and abandoned subterranean voids threaten mining operations, surface developments and the environment. Hazards within these spaces preclude human access to create and verify extensive maps or to characterize and analyze the environment. To that end, we have developed a mobile robot capable of autonomously exploring and mapping abandoned mines. To operate without communications in a harsh environment with little chance of rescue, this robot must have a robust electro-mechanical platform, a reliable software system, and a dependable means of failure recovery. Presented are the mechanisms, algorithms, and analysis tools that enable autonomous mine exploration and mapping along with extensive experimental results from eight successful deployments into the abandoned Mathies coal mine near Pittsburgh, PA.

This paper describes two robotic systems developed for acquiring accurate volumetric maps of underground mines. One system is based on a cart instrumented by laser range finders, pushed through a mine by people. Another is a remotely controlled mobile robot equipped with laser range finders. To build consistent maps of large mines with many cycles, we describe an algorithm for estimating global correspondences and aligning robot paths. This algorithm enables us to recover consistent maps several hundreds of meters in diameter, without odometric information. We report results obtained in two mines, a research mine in Bruceton, PA, and an abandoned coal mine in Burgettstown, PA.

We present the software architecture of a robotic system for mapping abandoned mines. The software is capable of acquiring consistent 2D maps of large mines with many cycles, represented as Markov random £elds. 3D C-space maps are acquired from local 3D range scans, which are used to identify navigable paths using A* search. Our system has been deployed in three abandoned mines, two of which inaccessible to people, where it has acquired maps of unprecedented detail and accuracy.

This paper summarizes ongoing work with a mo- bile manipulator (Mobipulator ). We describe the sys- tem architecture of the latest version of the robot, a hierarchy of robot motion commands (the Mobipula- tion library) that can be snapped together to generate complicated paths easily, a configuration space plan- ner that plans wheel motions to manipulate paper, and a visual servoing system to monitor and correct errors in robot motion.