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Autonomous navigation by a mobile robot through L natural, unstructured terrain is one of the premier k challenges in field robotics. Tremendous advances V in autonomous navigation have been made recently in field robotics. Machine learning has played an increasingly important role in these advances. The Defense Advanced Research Projects Agency (DARPA) UGCV-Perceptor Integration (UPI) program was conceived to take a fresh approach to all aspects of autonomous outdoor mobile robot design, from vehicle design to the design of perception and control systems with the goal of achieving a leap in performance to enable the next generation of robotic applications in commercial, industrial, and military applications. The essential problem addressed by the UPI program is to enable safe autonomous traverse of a robot from Point A to Point B in the least time possible given a series of waypoints in complex, unstructured terrain separated by 0.2-2 km. To accomplish this goal, machine learning techniques were heavily used to provide robust and adaptive performance, while simultaneously reducing the required development and deployment time. This article describes the autonomous system, Crusher, developed for the UPI program and the learning approaches that aided in its successful performance.

Many mobile robot applications require robots to act safely and intelligently in complex unfamiliarenvironments with little structure and limited or unavailable human supervision. As arobot is forced to operate in an environment that it was not engineered or trained for, various aspectsof its performance will inevitably degrade. Roboticists equip robots with powerful sensorsand data sources to deal with uncertainty, only to discover that the robots are able to make onlyminimal use of this data and still find themselves in trouble. Similarly, roboticists develop andtrain their robots in representative areas, only to discover that they encounter new situations thatare not in their experience base. Small problems resulting in mildly sub-optimal performance areoften tolerable, but major failures resulting in vehicle loss or compromised human safety are not.This thesis presents a series of online algorithms to enable a mobile robot to better deal withuncertainty in unfamiliar domains in order to improve its navigational abilities, better utilizeavailable data and resources and reduce risk to the vehicle. We validate these algorithms throughextensive testing onboard large mobile robot systems and argue how such approaches can increasethe reliability and robustness of mobile robots, bringing them closer to the capabilitiesrequired for many real-world applications.

Sensory perception for unmanned ground vehicle navigation has received great attention from the robotics community. However, sensors mounted on the vehicle are regularly viewpoint impaired. A vehicle navigating at high speeds in off- road environments may be unable to react to negative obstacles such as large holes and cliffs. One approach to address this problem is to complement the sensing capabilities of an unmanned ground vehicle with overhead data gathered from an aerial source. This paper presents techniques to achieve accurate terrain classification by utilizing high-density, colorized, three- dimensional laser data. We describe methods to extract relevant features from this sensor data in such a way that a learning algorithm can successfully train on a small set of labeled data in order to classify a much larger map and show experimental results. Additionally, we introduce a technique to significantly reduce classification errors through the use of context. Finally, we show how this algorithm can be customized for the intended vehicle’s capabilities in order to create more accurate a priori maps that can then be used for path planning.