We address the problem of adaptive information-optimal data collection in time series. Here a remote sensor or explorer agent throttles its sampling rate in order to track anomalous events while obeying constraints on time and power. This problem is challenging because the agent has limited visibility - all collected datapoints lie in the past, but its resource allocation decisions require predicting far into the future. Our solution is to continually fit a Gaussian process model to the latest data and optimize the sampling plan on line to maximize information gain. We compare the performance characteristics of stationary and nonstationary Gaussian process models. We also describe an application based on geologic analysis during planetary rover exploration. Here adaptive sampling can improve coverage of localized anomalies and potentially benefit mission science yield of long autonomous traverses.

Robotic exploration is an excellent method for obtaining information about sites too dangerous for people to explore. The operator's understanding of the environment depends on the rover returning useful information. Robotic mission bandwidth is frequently constrained, limiting the amount of information the rover can return. This paper explores the tradeoff between information and bandwidth based on two years of observations during a robotic astrobiology field study. The developed theory begins by analyzing the search task conducted by robot operators. This analysis leads to an information optimization model. Important parameters in the model include the value associated with detecting a target, the probability of locating a target, and the bandwidth required to collect the information from the environment. Optimizing the information return between regions creates an image and provides the necessary information while reducing bandwidth. Application of the model to the analyzed field study results in an optimized image that requires 48.3% less bandwidth to collect. The model also predicts several data collection patterns that could serve as the basis of data collection templates for improving mission effectiveness. The developed optimization model reduces the bandwidth necessary to collect information, thus aiding missions in collecting more information from the environment.

During two weeks of study in September and October of 2004, a science team directed a rover and explored the arid Atacama Desert in Chile. The objective of the mission was to search for life. Over the course of the mission the team gained experience with the rover and the rover became more reliable and autonomous. As a result, the rover/operator system became more effective. Several factors likely contributed to the improvement in science effectiveness including increased experience, more effective search strategies, different science team composition, different science site locations, changes in rover operational capabilities, and changes in the operation interface. However, it is difficult to quantify this effectiveness because science is a largely creative and unstructured task. This study considers techniques that quantify science team performance leading to an understanding of which features of the human-rover system are most effective and which features need further development. Continuous observation of the scientists throughout the mission led to coded transcripts enumerating each scientific statement. This study considers whether six variables correlate with scientific effectiveness. Several of these variables are metrics and ratios related to the daily rover plan, the time spent programming the rover, the number of scientific statements made and the data returned. The results indicate that the scientists created more complex rover plans without increasing the time to create the plans. The total number of scientific statements was approximately equal (2187 versus 2415) for each week. There was a 50% reduction in bytes of returned data between the two weeks resulting in an increase in scientific statements per byte of returned data ratio. Of the original six, the most successful proxies for science effectiveness were the time to program each rover task and the number of scientific statements related to data delivered by the rover. Although both these measures have face validity and were consistent with the results of this experiment, their ultimate empirical utility must be measured further.

When searching a remote environment with a robot, the fundamental constraint on the operator is the bandwidth available to the mission. This paper examines how a science team uses a high bandwidth panorama during a two-year astrobiology field test. Bandwidth directly controls the type and amount of information received by the operator. Given the significance of information and bandwidth to mission success, it is important for human robot interaction analyses to consider bandwidth usage during robotic operations. Insight gained from these analyses can help future missions efficiently use their bandwidth to collect the necessary information from the environment. The analysis first shows that the science team preferentially views certain areas of the panorama based on tile elevation but not based on azimuth. This finding led the analysis to look into what tasks the science team completes while using the panorama. In the context of an astrobiology mission, the most important role of the panorama is to determine the robot's position on orbital images. The secondary task is to determine the general geologic context of the environment. Based on the viewing patterns and associated tasks the analysis produces a list of targets important to mission success. Future research efforts will focus on methods for collecting the information contained in these targets from the environment at a reduced bandwidth cost.

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.

Researchers have previously described a mobile robot, or rover, operator's difficulty in accurately perceiving the rover's tilt and roll, which can lead to rollover accidents. Safe mobile robot navigation and effective mission planning also require an operator to accurately interpret and understand the geometry and scale of features in the rover's environment. This work presents an experiment that measures an observer's ability to estimate height of distant (5-15 m) obstacles given an accurate local model (e.g., within 0-5 m of the rover), a panoramic image, and a physical mock-up of the local terrain. The experimental conditions were intended to represent a best-case scenario for a stopped rover equipped with short base-line stereoscopic cameras. The participants' task was to extrapolate the well-modeled local geometry to monoscopic images of the more distant terrain. The experiment compared two estimation techniques. With the first technique, each observer physically indicated his or her direct estimates of the obstacle distance and height. With the second estimation technique, which we call horizon analysis, the observer indicated the position of the top and bottom of each rock on an image and the height was calculated by measuring the visual angle between the theoretical horizon and the points indicated by the observer. The direct estimation technique overestimated the height of the rocks by an average of 190%; the horizon analysis technique overestimated by 80%. The results suggest that even when provided with a rich set of supplementary and context information, rover operators have significant difficulty in vertically perceiving the scale of distant terrain. The results also suggest that horizon analysis is a more accurate method for determining the height of distant rover navigation obstacles, when the local terrain is nearly level.