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In this paper, we describe a framework for the autonomous capture and servicing of satellites. The work is based on laboratory experiments that illustrate the autonomy and remote‐operation aspects. The satellite‐capture problem is representative of most on‐orbit robotic manipulation tasks where the environment is known and structured, but it is dynamic since the satellite to be captured is in free flight. Bandwidth limitations and communication dropouts dominate the quality of the communication link. The satellite‐servicing scenario is implemented on a robotic test‐bed in laboratory settings. The communication aspects were validated in transatlantic tests. © 2007 Canadian Space Agency

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This paper describes a collection of 272 three-dimensional laser scans gathered at two unique planetary analogue rover test facilities in Canada, which offer emulated planetary terrain at manageable scales for algorithmic development. This dataset is subdivided into four individual subsets, each gathered using panning laser rangefinders on different mobile rover platforms. This data should be of interest to field robotics researchers developing rover navigation algorithms suitable for use in three-dimensional, unstructured, natural terrain. All of the data are presented in human-readable text files, and are accompanied by Matlab parsing scripts to facilitate use thereof. This paper provides an overview of the available data.

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In this paper, we present a robust framework suitable for conducting three‐dimensional simultaneous localization and mapping (3D SLAM) in a planetary work site environment. Operation in a planetary environment imposes sensing restrictions, as well as challenges due to the rugged terrain. Utilizing a laser rangefinder mounted on a rover platform, we have demonstrated an approach that is able to create globally consistent maps of natural, unstructured 3D terrain. The framework presented in this paper utilizes a sparse‐feature‐based approach and conducts data association using a combination of feature constellations and dense data. Because of feature scarcity, odometry measurements are also incorporated to provide additional information in feature‐poor regions. To maintain global consistency, these measurements are resolved using a batch alignment algorithm, which is reinforced with heterogeneous outlier rejection to improve its robustness to outliers in either measurement type (i.e., laser or odometry). Finally, a map is created from the alignment estimates and the dense data. Extensive validation of the framework is provided using data gathered at two different planetary analogue facilities, which consist of 50 and 102 3D scans, respectively. At these sites, root‐mean‐squared mapping errors of 4.3 and 8.9 cm were achieved. Relative metrics are utilized for localization accuracy and map quality, which facilitate detailed analysis of the performance, including failure modes and possible future improvements. © 2012 Wiley Periodicals, Inc.

Abstract-In this paper we present the approach for autonomous planetary exploration developed at the Canadian Space Agency. The goal of this work is to autonomously navigate to remote locations, well beyond the sensing horizon of the rover, with minimal interaction with a human operator. We employ LIDAR range sensors due to their accuracy, long range and robustness in the harsh lighting conditions of space. Irregular Triangular Meshes (ITMs) are used for representing the environment providing an accurate yet compact spatial representation. In this paper a novel path-planning technique through the ITM is introduced, which guides the rover through flatter terrain and safely away from obstacles. Experiments performed in CSA's Mars emulation terrain that validate our approach are also presented. I. INTRODUCTIONMobile robotics has enabled scientific breakthroughs in planetary exploration [1]. Recent accomplishments have demonstrated beyond doubt the necessity and feasibility of semi-autonomous rovers for conducting scientific exploration on other planets. Both Mars Exploration Rovers (MERs) "Spirit" and "Opportunity" have the ability to detect and avoid obstacles, picking a path that would take them along a safe trajectory. MER's have reached traverses of 300m/sol. On occasion, the rovers have had to travel to locations that were at the fringe of the horizon of their sensors or even slightly beyond.The next rover missions to Mars are the "Mars Science Laboratory" (MSL) [2] and ESA's ExoMars [3]. Both of these missions have set target traverse distances on the order of one kilometer per day. Both the MSL and ExoMars rovers are therefore expected to drive regularly a significant distance beyond the horizon of their environment sensors. Earthbased operators will therefore not know a-priori the detailed geometry of the environment and will thus not be able to select via-points for the rovers throughout their traverses.One of the key technologies that will be required is the ability to sense and model the 3D environment in which the rover has to navigate. To address the above mentioned issues, the Canadian Space Agency is developing a suite of technologies for long-range rover navigation. For the purposes of this paper, "long-range" is defined as a traverse that takes the rover beyond the horizon of the rover's environment sensors.In the next Section we discuss the state-of-the-art in robotic planetary exploration. Section II presents the overall process for planetary exploration together with a short description of our test-bed. Next we present a summary of our approach to environmental modelling, Section IV