An Automated Approach for Acquiring Onboard Rover Science
Rover traverse distances are increasing at a faster rate than downlink capacity is increasing. As this trend continues, the quantity of data that can be returned to Earth per meter traversed is reduced. The capacity of the rover to collect data, however, remains high. This circumstance leads to an opportunity to increase mission science return by carefully selecting the data with the highest science interest for downlink. We have developed an onboard science analysis technology for increasing science return from missions. Our technology evaluates the geologic data gathered by the rover. This analysis is used to prioritize the data for transmission, so that the data with the highest science value is transmitted to Earth. In addition, the onboard analysis results are used to identify additional science gathering opportunities. A planning and scheduling component of the system enables the rover to take advantage of the identified science opportunity. Introduction: With the continued expansion of planetary exploration, future missions will cause an increase in the volume of data that can be acquired for transmission to Earth via the Deep Space Network (DSN). Missions will have to make critical decisions regarding the quantity and quality of the downlink data. Although the DSN’s transmission capability improves every year, the DSN cannot keep pace with the vast number of missions it must service. New methods must be developed to maximize the science return for the available bandwidth. We are developing an approach to counteract this transmission limitation by improving the quality of useful information conveyed per bit. We have developed onboard analysis methods to autonomously prioritize data for downlink, thus maximizing the science return for the available bandwidth. By carefully selecting key data for transmission, the quality of data returned is increased. Selecting the most interesting data requires encapsulation of characteristics of data with high science value in a form that can be analyzed and evaluated onboard. The spacecraft must have the capability of automatically recognizing data that contains information about targets with high science interest. Since images are among the highest bandwidth data that a rover collects, images represent one of the greatest opportunities for data prioritization. Methodology: To assess and subsequently prioritize the scientific value of a set of collected images, we must first extract the information found within the images. A geologist in the field would extract the information from a site by identifying geologic features including the albedo, texture, shape, size, color, and arrangement of rocks, and features of the topography such as layers in a cliff face. Our system begins by first evaluating each image and locating the rocks within it. Next, we extract the properties of the rocks, including albedo, texture and shape features. These features are assigned a science value, and the importance of the image is evaluated based on these values as compared to the features extracted from rocks identified in other images. Rock Classification: Images with interesting features, such as rocks with unusual shapes or textures, should be ranked higher than images without distinctive features. We have developed three different prioritization methods that use the extracted rock features to rank the rocks in terms of scientific importance. Once the rocks are prioritized, the images containing the rocks are ranked based on the rocks contained within them. Extraction: Our technique for locating rocks is based on finding objects above the ground plane. We begin by determining the ground plane from the stereo range data, which is already calculated for navigation purposes. We then produce a height image, in which the value of each pixel represents the elevation of the point above the ground plane. Level contours in the height image are calculated and then these contours are connected from peaks to the ground plane to identify the rocks [1]. Rock properties including albedo, visual texture and shape, are then extracted from the rocks identified. We measure albedo, an indicator of the reflectance properties of a surface, by computing the average gray-scale value of the pixels that comprise the image of the rock. The reflectance properties of a rock provide information about its mineralogical composition. The second rock property extracted is visual texture . Visual texture can provide valuable clues to both the mineral composition and geological history of a rock. Visual texture can be described by gray-scale intensity variations at different orientations and spatial frequencies within the image. We measure texture using a bank of Gabor filters [2, 3]. Gabor filters are scale and orientation specific, thus the results of convolving an image with these filters can be successfully used to discriminate between different textures. Another important and geologically useful feature of rocks is their inherent shape . For example, a rock that is highly rounded may have undergone fluvial processing and traveled far from its source. Conversely, a rock that is highly angular is likely to be close to its source and to have undergone minimal secondary processing. We begin by fitting an ellipse to the boundary points of the identified rock in the image [4]. Our first shape measure is the eccentricity of this ellipse. Our second measure is the error between the boundary points and the ellipse. The third and final measure is angularity, which is measured as the standard deviation of the angle of the edge at each boundary point. Prioritization: The features extracted from a group of images are then used to rank the images using the three distinct prioritization algorithms. The first technique recognizes Lunar and Planetary Science XXXIV (2003) 1845.pdf