Christopher R. Baker

A system for volumetric robotic mapping of abandoned mines

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.

Obstacle Detection and Tracking for the Urban Challenge

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.

Autonomous Driving in Traffic: Boss and the Urban Challenge

The DARPA Urban Challenge was a competition to develop autonomous vehicles capable of safely, reliably and robustly driving in traffic. In this article we introduce Boss, the autonomous vehicle that won the challenge. Boss is complex artificially intelligent software system embodied in a 2007 Chevy Tahoe. To navigate safely, the vehicle builds a model of the world around it in real time. This model is used to generate safe routes and motion plans in both on roads and in unstructured zones. An essential part of Boss’ success stems from its ability to safely handle both abnormal situations and system glitches.

A campaign in autonomous mine mapping

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.

Recent developments in subterranean robotics

Robotic systems exhibit remarkable capability for exploring and mapping subterranean voids. Information about subterranean spaces has immense value for civil, security, and commercial applications where problems, such as encroachment, collapse, flooding and subsidence can occur. Contemporary method for underground mapping, such as human surveys and geophysical techniques, can provide estimates of void location, but cannot achieve the coverage, quality, or economy of robotic approaches. This article presents the challenges, mechanisms, sensing, and software of subterranean robots. Results obtained from operations in active, abandoned, and submerged subterranean spaces will also be shown.

A reasoning framework for autonomous urban driving

Urban driving is a demanding task for autonomous vehicles as it requires the development and integration of several challenging capabilities, including high-level route planning, interaction with other vehicles, complex maneuvers, and ultra-reliability. In this paper, we present a reasoning framework for an autonomous vehicle navigating through urban environments. Our approach combines route-level planning, context-sensitive local decision making, and sophisticated motion planning to produce safe, intelligent actions for the vehicle. We provide examples from an implementation on an autonomous passenger vehicle that has driven over 3000 autonomous kilometers and competed in, and won, the Urban Challenge.

Traffic interaction in the urban challenge: Putting boss on its best behavior

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.

Street smarts for boss

The Urban Challenge was an autonomous vehicle competition sponsored by the U.S. Defense Advanced Research Projects Agency (DARPA) in November 2007. Contestant robots were required to autonomously execute a series of navigation missions through a simplified urban environment consisting of roads, intersections, and parking lots while obeying road rules and interacting safely and correctly with other traffic. In contrast to previous DARPA challenges, which focused on rough-terrain navigation, this competition required a system capable of complex autonomous behaviors, such as waiting for precedence at an intersection or passing a slow-moving vehicle on a multilane road. These behaviors were managed by a software subsystem called the behavioral executive in Boss, Tartan Racings winning entry in the Urban Challenge. The fulfillment of this role required the carefully structured integration and management of many disparate capabilities in a highly flexible manner over the course of the development, accommodating whatever changes were necessary to win the competition amidst a continuously evolving software system. These requirements, among many others, are reflected in the behavioral executives architecture, the flexibility and adaptability of which played an important role in the teams success.

Software Infrastructure for an Autonomous Ground Vehicle

The DARPA Urban Challenge required robots to drive 60 miles on suburban roads while following the rules of the road in interactions with human drivers and other robots. Tartan Racing’s Boss won the competition, completing the course in just over 4 hours. This paper describes the software infrastructure developed by the team to support the perception, planning, behavior generation, and other artificial intelligence components of Boss. We discuss the organizing principles of the infrastructure, as well as details of the operator interface, interprocess communications, data logging, system configuration, process management, and task framework, with attention to the requirements that led to the design. We identify the requirements as valuable re-usable artifacts of the development process.

Experiments with nonholonomic manipulation

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