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Aim: Comprehensive, global information on species' occurrences is an essential biodiversity variable and central to a range of applications in ecology, evolution, biogeography and conservation. Expert range maps often represent a species' only available distributional information and play an increasing role in conservation assessments and macroecology. We provide global range maps for the native ranges of all extant mammal species harmonised to the taxonomy of the Mammal Diversity Database (MDD) mobilised from two sources, the Handbook of the Mammals of the World (HMW) and the Illustrated Checklist of the Mammals of the World (CMW). Location: Global. Taxon: All extant mammal species. Methods: Range maps were digitally interpreted, georeferenced, error-checked and subsequently taxonomically aligned between the HMW (6253 species), the CMW (6431 species) and the MDD taxonomies (6362 species). Results: Range maps can be evaluated and visualised in an online map browser at Map of Life (mol.org) and accessed for individual or batch download for non-commercial use. Main conclusion: Expert maps of species' global distributions are limited in their spatial detail and temporal specificity, but form a useful basis for broad-scale characterizations and model-based integration with other data. We provide georeferenced range maps for the native ranges of all extant mammal species as shapefiles, with species-level metadata and source information packaged together in geodatabase format. Across the three taxonomic sources our maps entail, there are 1784 taxonomic name differences compared to the maps currently available on the IUCN Red List website. The expert maps provided here are harmonised to the MDD taxonomic authority and linked to a community of online tools that will enable transparent future updates and version control.

Since the end of the Apollo missions to the lunar surface in December 1972, humanity has exclusively conducted scientific studies on distant planetary surfaces using teleprogrammed robots. Operations and science return for all of these missions are constrained by two issues related to the great distances between terrestrial scientists and their exploration targets: high communication latencies and limited data bandwidth. Despite the proven successes of in-situ science being conducted using teleprogrammed robotic assets such as Spirit, Opportunity, and Curiosity rovers on the surface of Mars, future planetary field research may substantially overcome latency and bandwidth constraints by employing a variety of alternative strategies that could involve: 1) placing scientists/astronauts directly on planetary surfaces, as was done in the Apollo era; 2) developing fully autonomous robotic systems capable of conducting in-situ field science research; or 3) teleoperation of robotic assets by humans sufficiently proximal to the exploration targets to drastically reduce latencies and significantly increase bandwidth, thereby achieving effective human telepresence. This third strategy has been the focus of experts in telerobotics, telepresence, planetary science, and human spaceflight during two workshops held from October 3–7, 2016, and July 7–13, 2017, at the Keck Institute for Space Studies (KISS). Based on findings from these workshops, this document describes the conceptual and practical foundations of low-latency telepresence (LLT), opportunities for using derivative approaches for scientific exploration of planetary surfaces, and circumstances under which employing telepresence would be especially productive for planetary science. An important finding of these workshops is the conclusion that there has been limited study of the advantages of planetary science via LLT. A major recommendation from these workshops is that space agencies such as NASA should substantially in

The Autonomous Exploration for Gathering Increased Science System (AEGIS) will soon provide automated targeting for remote sensing instruments on the Mars Exploration Rover (MER) mission, which currently has two rovers exploring the surface of Mars. Targets for rover remote-sensing instruments, especially narrow field-of-view instruments (such as the MER Mini-TES spectrometer or the 2011 Mars Science Laboratory (MSL) Mission ChemCam Spectrometer), are typically selected manually based on imagery already on the ground with the operations team. AEGIS enables the rover flight software to analyze imagery onboard in order to autonomously select and sequence targeted remote-sensing observations in an opportunistic fashion. In this paper, we first provide background information on the larger autonomous science framework in which AEGIS was developed. We then describe how AEGIS was specifically developed and tested on the JPL FIDO rover. Finally we discuss how AEGIS will be uploaded and used on the Mars Exploration Rover (MER) mission in early 2009.

The autonomous exploration for gathering increased science system (AEGIS) will soon provide automated targeting for remote sensing instruments on the Mars exploration rover (MER) mission, which currently has two rovers exploring the surface of Mars. Targets for rover remote sensing instruments, especially narrow field of view instruments (such as the MER Mini-TES spectrometer or the 2011 Mars Science Laboratory (MSL) Mission ChemCam Spectrometer), are typically selected manually based on imagery already on the ground with the operations team. AEGIS enables the rover flight software to analyze imagery onboard in order to autonomously select and sequence targeted remote sensing observations in an opportunistic fashion. In this paper, we first provide background information on the larger autonomous science framework in which AEGIS was developed. We then describe how AEGIS was specifically developed and tested on the JPL FIDO rover. Finally we discuss how AEGIS will be uploaded and used on the Mars exploration rover (MER) mission in mid 2009.

Recent technological advances have significantly improved the capabilities of micro-air vehicles (MAV). This is evidenced by their expanding use by government, police, and military forces. A MAV can provide a real-time surveillance capability to even the smallest units, which provides commanders with a significant advantage. This capability is a result of the availability of miniaturized autopilot systems which typically combine inertial, pitot-static, and GPS sensors into a feedback flight-control system. While these autopilots can provide an autonomous flight capability, they have some limitations which impact their operational effectiveness. One of the primary issues is poor image geolocation performance, which limits the use of these systems for direct measurements of target locations. This poor geolocation performance is primarily a consequence of the relatively large attitude errors characteristic of low-performance inertial sensors. In previous efforts, we have developed a tightly-coupled image-aided inertial navigation system to operate in areas not serviced by GPS. This system extracts navigation information by automatically detecting and tracking stationary optical features of opportunity in the environment. One characteristic of this system is vastly reduced attitude errors, even with consumer-grade inertial sensors. In this paper, the benefits of incorporating image-based navigation techniques with inertial and GPS measurements is explored. After properly integrating GPS with the image-aided inertial architecture, the system is tested using a combination of Monte-Carlo simulation and flight test data. The flight test data was flown over Edwards AFB using representative hardware. The experimental results are compared with validated truth data., Prepared in cooperation with National Geospatial-Intelligence Agency (NGA), and Air Force Test Pilot School (USAF/TPS).

The onboard autonomous science investigation system (OASIS) evaluates geologic data gathered by a planetary 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 science opportunities. A planning and scheduling component of the system enables the rover to take advantage of identified science opportunities. In this paper, we provide a brief overview of the entire OASIS system, and then describe new system capabilities with an emphasis on the identification of novel features during a traverse. This capability has been integrated into the full system and validated in field testing. In addition, the system has been integrated with the visual target tracking (VTT) capability recently uploaded to the Mars exploration rovers. VTT enables the system to robustly track a specified target. By integrating this with the autonomous science system, the rover can approach targets identified onboard and acquire targeted measurements both from additional viewing angles as well as from positions in close proximity to the target.

The Onboard Autonomous Science Investigation System (OASIS) was used in the first formal demonstration of closed loop opportunistic detection and reaction during a rover traverse on the FIDO rover at NASA's Jet Propulsion Laboratory. In addition to hardware demonstrations, the system has been demonstrated and exercised in simulation using the Rover Analysis, Modeling, and Simulation (ROAMS) planetary rover simulator, A. Jain et al (2003). We discuss several system enhancements including new planning and scheduling capabilities and image prioritization. We also describe the new end-of-traverse capability that includes taking a partial panorama of images, assessing these for targets of interest, and collecting narrow angle images of selected targets. Finally, we present several methods for estimating properties of rocks and provide a comparative assessment. Understanding the relationship of these methods is important to correctly interpret autonomous rock analyses performed during a traverse.

The goal of the Onboard Autonomous Science Investigation System (OASIS) project at NASA's Jet Propulsion Laboratory (JPL) is to evaluate, and autonomously act upon, science data gathered by in-situ spacecraft, such as planetary landers and rovers. Using the FIDO rover in the Mars yard at JPL, we have successfully demonstrated a closed loop system test of the rover acquiring image data, finding rocks in the image, analyzing rock properties and identifying rocks that merit further investigation. When the system on the rover alerts the rover to take additional measurements of interesting rocks, the planning and scheduling component determines if there are enough resources to meet this additional science data request. The rover is then instructed to either turn toward the rock, or to actually move closer to the rock to take an additional, close up, picture. In addition to these hardware integration successes, the OASIS team has also continued its autonomous science research by collaboratively working with other scientists and technologists to identify and react to other scientific phenomena - such as clouds and dust devils. Prototype dust devil and cloud detection algorithms were delivered to an infusion task which has refined the algorithms specifically for Mars exploration rovers (MER) and is integrating the code into the next release of MER flight software.

The Onboard Autonomous Science Investigation System (OASIS) evaluates geologic data gathered by a planetary 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 science opportunities. A planning and scheduling component of the system enables the rover to take advantage of the identified science opportunity. OASIS is a NASA-funded research project that is currently being tested on the FIDO rover at JPL for use on future missions. In this paper, we provide a brief overview of the OASIS system, and then describe our recent successes in integrating with and using rover hardware. OASIS currently works in a closed loop fashion with onboard control software (e.g., navigation and vision) and has the ability to autonomously perform the following sequence of steps: analyze gray scale images to find rocks, extract the properties of the rocks, identify rocks of interest, retask the rover to take additional imagery of the identified target and then allow the rover to continue on its original mission. We also describe the early 2004 ground test validation of specific OASIS components on selected Mars exploration rover (MER) images. These components include the rock-finding algorithm, RockIT, and the rock size feature extraction code. Our team also developed the RockIT GUI, an interface that allows users to easily visualize and modify the rock-finder results. This interface has allowed us to conduct preliminary testing and validation of the rock-finder's performance.

With each new rover mission to Mars, rovers are traveling significantly longer distances. This distance increase allows not only the collection of more science data, but enables a number of new and different science collection opportunities. Current mission operations, such as that on the 2003 Mars exploration rovers (MER), require all rover commands to be determined on the ground, which is a time-consuming and largely manual process. However, many science opportunities can be efficiently handled by performing intelligent decision-making onboard the rover itself. This paper describes how dynamic planning and scheduling techniques can be used onboard a rover to autonomously adjust rover activities in support of science goals. These goals could be identified by scientists on the ground or could be identified by onboard data-analysis software. Several different types of dynamic decisions are described, including the handling of opportunistic science goals identified during rover traverses, preserving high priority science targets when resources, such as power, are unexpectedly oversubscribed, and dynamically adding additional, ground-specified science targets when rover actions are executed more quickly than expected. After describing our system approach, we discuss some of the particular challenges we have examined to support autonomous rover decision-making. These include interaction with rover navigation and path-planning software and handling large amounts of uncertainty in state and resource estimations. Finally, we describe our experiences in testing this work using several Mars rover prototypes in a realistic environment.

The Onboard Autonomous Science Investigation System (OASIS) is a technology for increasing science return during rover traverses by prioritizing data onboard, and identifying and reacting to unanticipated science opportunities. Rovers of the future will have the capacity to collect more data than can be downlinked back to Earth. OASIS can increase mission science return by carefully selecting the data with the highest science interest for downlink. These rovers may also be required to traverse long distances with little to no interaction with the science team on Earth. OASIS can act as a geologist's assistant and can autonomously direct the rover to take additional measurements of "interesting" rocks. The importance of characterizing the terrain along these traverses, a study that is now becoming known as traverse science, increases with the distances the rover must travel. This paper provides a brief overview of the entire OASIS system and how it analyzes one type of data - grayscale images taken by the rover for engineering and hazard avoidance purposes. Although the OASIS system can apply the same type of analysis to different data types, such as color images, hyperspectral images or point spectrometer data, we will only focus on grayscale images here. The paper also describes the latest advances in two key aspects of the system: image prioritization and the science alert. In image prioritization, we combine the results from three distinct prioritization methods to arrive at an overall downlink ranking of the images collected during a traverse. The science alert is a capability that enables the rover to identify and react to a pre-specified, and scientifically significant, signature. Once this signature has been detected via the onboard science analysis component, the planning and scheduling module updates the rover command sequence to stop the traverse and signal Earth of the find. If there is sufficient time and onboard resources before the next downlink opport

Rover traverse distances are increasing at a faster rate than downlink capacity is increasing. As this trend continues, the quantity of data that can he 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 gather 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 science opportunities. A planning and scheduling component of the system enables the rover to take advantage of the identified science opportunity. Although our tcchniqucs arc applicable to a wide range of data modalities, our initial emphasis has focused on image analysis, since images consume a large percentage of downlink bandwidth. We have further focused our foundational work on rocks. Rocks are among the primary features populating the Martian landscape. Characterization and understanding of rocks on the surface is a first step leading towards more complex in situ regional geological assessments by the rover.