We propose a method for generating interpretable descriptions of inputs that cause faults in high-dimensional software interfaces. Our method models the set of fault-triggering inputs as a Cartesian product and identifies this set by actively querying the system under test. The active sampling scheme is very efficient in the common case that few fields in the interface are relevant to causing the fault. This scheme also solves the problem of efficiently finding sufficient examples to model rare faults, which is problematic for other learning-based methods. Compared to other techniques, ours requires no parameter turning or post-processing in order to produce useful results. We analyze the method qualitatively, theoretically, and empirically. An experimental evaluation demonstrates superior performance and reliability compared to a basic decision tree approach. We also briefly discuss how the method has assisted in debugging a commercial autonomous ground vehicle system.

The Atacama Desert of northern Chile may be the most lifeless place on Earth, yet where the desert meets the Pacific coastal range desiccation-tolerant micro-organisms are known to exist. The gradient of biodiversity and habitats in the Atacama’s subregions remain unexplored and are the focus of the Life in the Atacama project. To conduct this investigation, long traverses must be made across the desert with instruments for geologic and biologic measurements. In this paper we motivate the Life in the Atacama project from both astrobiologic and robotic perspectives. We focus on some of the research challenges we are facing to enable endurance navigation, resource cognizance, and long-term survivability. We conducted our first scientific investigation and technical experiments in Chile with the mobile robot Hyperion. We describe the experiments and the results of our analysis. These results give us insight into the design of an effective robotic astrobiologist and into the methods by which we will conduct scientific investigation in the next field season.

Sun-synchronous exploration is accomplished by reasoning about sunlight: where the Sun is in the sky, where and when shadows will fall, and how much power can be obtained through various courses of action. In July 2001 a solar-powered rover, named Hyperion, completed two sun-synchronous exploration experiments in the Canadian high arctic (75/spl deg/N). Using knowledge of orbital mechanics, local terrain, and expected power consumption, Hyperion planned a sun-synchronous route to visit designated sites while obtaining the necessary solar power for continuous 24-hour operation. Hyperion executed its plan and returned to its starting location with batteries fully charged after traveling more than 6 kilometers in barren, Mars-analog terrain. We describe the concept of sun-synchronous exploration. We overview the design of the robot Hyperion and the software system that enables it to operate sun-synchronously. We then discuss results from analysis of our first experiment in sun-synchronous exploration and conclude with observations.

Automation of the search for and classification of Antarctic meteorites offers a unique case for early demonstration of robotics in a scenario analogous to geological exploratory missions to other planets and to the Earth's extremes. Moreover, the discovery of new meteorite samples is of great value because meteorites are the only significant source of extraterrestrial material available to scientists. In this paper we focus on the primary outcomes and technical lessons learned from the first field demonstration of autonomous search and in situ classification of Antarctic meteorites by a robot. Using a novel autonomous control architecture, specialized science sensing, combined manipulation and visual servoing, and Bayesian classification, the Nomad robot classified five indigenous meteorites during an expedition to the remote site of Elephant Moraine in January 2000. Nomad's expedition proved the rudiments of science autonomy and exemplified the merits of machine learning techniques for autonomous geological classification in real-world settings. On the other hand, the expedition showcased the difficulty in executing reliable robotic deployment of science sensors and a limited performance in the speed and coverage of autonomous search.

This paper describes the development and implementation of a Bayes network based system for distinguishing terrestrial rocks from meteorites from onboard the mobile robotic rover Nomad. Equipped with a color camera and spectrometer, Nomad autonomously made the first robotic identification of a meteorite, in January 2000 at the Elephant Moraine, Antarctica. This paper discusses rock classification from a robotic platform and the challenges of autonomously obtaining good sensor data in the field, while focusing on Nomad implementation.

The Science Autonomy System (SAS) is a hierarchical control architecture for exploration and in situ science that integrates sensing, navigation, classification and mission planning. The Nomad robot demonstrated the capabilities of the SAS during a January 2000 expedition to Elephant Moraine, Antarctica where it accomplished the first meteorite discoveries made by a robot. In the paper, the structure and functionality of the three-tiered SAS are detailed. Results and lessons learned are presented with a focus on important future research.