Depth sensors like LIDARs and Kinect use a fixed depth acquisition strategy that is independent of the scene of interest. Due to the low spatial and temporal resolution of these sensors, this strategy can undersample parts of the scene that are important (small or fast moving objects), or oversample areas that are not informative for the task at hand (a fixed planar wall). In this paper, we present an approach and system to dynamically and adaptively sample the depths of a scene using the principle of triangulation light curtains. The approach directly detects the presence or absence of objects at specified 3D lines. These 3D lines can be sampled sparsely, non-uniformly, or densely only at specified regions. The depth sampling can be varied in real-time, enabling quick object discovery or detailed exploration of areas of interest. These results are achieved using a novel prototype light curtain system that is based on a 2D rolling shutter camera with higher light efficiency, working range, and faster adaptation than previous work, making it useful broadly for autonomous navigation and exploration.

Accurate global localization is essential for planetary rovers to reach mission goals and mitigate operational risk. For initial exploration missions, it is inappropriate to deploy GPS or build other infrastructure for navigating. One way of determining global position is to use terrain relative navigation (TRN). TRN compares planetary rover-perspective images and 3D models to existing satellite orbital imagery and digital elevation models (DEMs) for absolute positioning. However, TRN is limited by the quality of orbital data and the presence and uniqueness of terrain features. This work presents a novel combination of belief space planning with terrain relative navigation. Additionally, we introduce a new method for increasing the robustness of belief space planning to noisy map data. The new algorithm provides a statistically significant reduction in localization uncertainty when tested on elevation data produced from lunar orbital imagery.

3D radiation source localization is a common task across applications such as decommissioning, disaster response, and security, but traditional count-based sensors struggle to efficiently disambiguate between symmetries in sensor, source, and environment configurations. Recent works have demonstrated successful passive source localization using a bearing sensor called the Compton gamma camera that can image radiation. This paper first presents an approach to mapping the spatial distribution of radiation with a gamma camera to estimate source locations. An active source localization framework is then developed that greedily selects new waypoints that maximize the Fisher Information provided by the camera's range and bearing observations for source localization. Finally the common assumption of a static step size in between waypoints is relaxed to allow step sizes to adapt online to the observed information. The proposed radiation mapping approach is evaluated in 5×4 m2and 14×6 m2laboratory environments, where multiple point sources were localized to within an average of 0.26 m or 0.6% of the environment dimensions. The active source localization approach is evaluated in simulation and an adaptive step size yields a 27% decrease in the localization time and a 16% decrease in the distance traveled to localize a source in a 15×15×15 m3environment.

Future robotic missions to the poles of the Moon and Mercury will face challenges not encountered by current and prior planetary rover missions. Careful energy-aware spatiotemporal path planning will be required to accomplish mission objectives at high cadence under changing illumination conditions. With attention to landing site and time, such spatiotemporal path planning may enable extended missions on order of months that would not otherwise be possible. This work presents improvements in energy-aware spatiotemporal path planning for multiple waypoints to significantly reduce planning time. A time-compression technique is used to simplify planning in areas where changes occur infrequently. Consideration of end-goal reachability reduces the search space. Finally, heuristics that use pre-computation with static obstacles speed up the search.

This paper presents a method for predicting slip using Gaussian process regression. Slip models are learned for visually classified terrain types as a function of terrain geometry. Spatial correlations between terrain properties are leveraged for on-line slip model adaptation. Results show that regression-based modeling using in-situ rover data outperforms the state-of-practice, terrestrially-calibrated slip curves in both mean prediction and uncertainty bounds. Local adaptation improves slip prediction results, particularly in high-slip sand areas that pose the greatest threat to rovers. Slip estimates made using a visual classifier to identify terrain type are compared to estimates using on-line model selection with only proprioceptive slip measurements as inputs. The proprioceptive results nearly match the visual results, showing that this approach could work even when a visual classifier is not available.

Consumer time-of-flight depth cameras like Kinect and PMD are cheap, compact and produce video-rate depth maps in short-range applications. In this paper we apply energy-efficient epipolar imaging to the ToF domain to significantly expand the versatility of these sensors: we demonstrate live 3D imaging at over 15 m range outdoors in bright sunlight; robustness to global transport effects such as specular and diffuse inter-reflections---the first live demonstration for this ToF technology; interference-free 3D imaging in the presence of many ToF sensors, even when they are all operating at the same optical wavelength and modulation frequency; and blur-free, distortion-free 3D video in the presence of severe camera shake. We believe these achievements can make such cheap ToF devices broadly applicable in consumer and robotics domains.

This paper presents a real-time, kinodynamic planning and information-theoretic exploration framework that enables high-resolution mapping of three-dimensional environments featuring complex concavities and disjoint objects. The proposed approach targets planetary exploration applications and seeks to achieve real-time operation on computationally constrained systems while ensuring energy-efficient information acquisition. Trajectories are selected by maximizing a measure of information gain per an expected execution cost (e.g., time or energy). The proposed trajectory generation formulation is based on state-lattice motion primitives and evaluation of the Cauchy-Schwarz quadratic mutual information (CSQMI) at each lattice state. An expanded search structure is proposed that extends the state-lattice to a finite horizon to enable expansive space coverage while remaining real-time viable. Additionally, compression techniques are employed to reduce the computational burden associated with the CSQMI calculation over expansive environments while preserving fidelity. The performance of the proposed methodology is evaluated through simulated exploration of a three-dimensional terrestrial pit environment by a quadrotor aerial robot which acts as a surrogate for a propulsive vehicle when operating on an airless body.

The inability of current robotic perception techniques to adequately detect non-geometric terrain hazards is a primary cause of failure for robots operating in natural terrain on Mars, the Moon, and Earth. Classical approaches detect surface appearance but do not measure the underlying mechanical properties that determine wheel-terrain interaction. Diurnal temperature variations of a granular material, however, are strongly correlated with both its surface appearance and subsurface geophysical properties. This paper presents a technique for determining relative differences in looseness and traversability of granular terrain through analysis of thermal imagery. Terrain compaction and traversability are predicted by estimating a material's thermal inertia from observations of thermal transients. Results from a set of experiments in sandy terrain demonstrate the ability of this approach to differentiate between safe, compact and hazardous, loose terrain.

This paper presents a method that applies connected component analysis to plan routes that keep robots continuously illuminated and on traversable slopes while reaching one or more goal locations. Such routes promise to extend the lifespan, range, and scientific return of solar-powered robots exploring environments with changing but predictable lighting conditions, particularly those of the Moon and Mercury. Maps of lighting and ground slope that describe these constraints in position and time are computed, and all distinct interconnected regions that have both direct sunlight and safe slope are found using connected component analysis. These three-dimensional connected components are pruned of roots that violate time constraints and branches that dead-end in discontinuous routes. Each component is the basis for a graph that includes all feasible routes from the initial time to the final time of that component. The shortest feasible route between a pair of start and goal positions within the same component is found using A* search and is characterized by its total length and average speed. Malapert Peak and Shackleton Crater, both near the Moon's South Pole, serve as examples throughout this paper due to their highly-relevant, dynamic, and predictable lighting caused by the Moon's motion relative to the Sun.

Affordable test articles for descent and landing are crucial for developing commercial lunar landing capability. To ensure successful lunar landing, flight software must be tested over mission-length durations on hardware exhibiting dynamics analogous to those of true flight articles. Energetic and structural constraints typically preclude affordable long-duration lander tests.

This paper compares a broad cross-section of range sensing technologies for underground void modeling. In this family of applications, a tunnel environment is incrementally mapped with range sensors from a mobile robot to recover scene geometry. Distinguishing contributions of this work include an unprecedented number of configurations evaluated utilizing common methodology and metrics as well as a significant in situ environmental component lacking in prior characterization work. Sensors are experimentally compared against both an ideal geometric target and in example void environments such as a mine and underground tunnel. Three natural groupings of sensors were identified from these results and performances were found to be strongly cost-correlated. While the results presented are specific to the experimental configurations tested, the generality of tunnel environments and the metrics of reconstruction are extensible to a spectrum of outdoor and surface applications.

This paper presents an approach that achieves robust high-speed navigation of prescribed routes. We present the mechatronic and software considerations that resulted in two robots that have completed over 4000 km of autonomous navigation of trails and off-road terrain at an average speed of approximately 30 km/h, with sustained top speeds of 55 km/h

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.

This paper describes two robotic systems developed for acquiring accurate volumetric maps of underground mines. One system is based on a cart instrumented by laser range finders, pushed through a mine by people. Another is a remotely controlled mobile robot equipped with laser range finders. To build consistent maps of large mines with many cycles, we describe an algorithm for estimating global correspondences and aligning robot paths. This algorithm enables us to recover consistent maps several hundreds of meters in diameter, without odometric information. We report results obtained in two mines, a research mine in Bruceton, PA, and an abandoned coal mine in Burgettstown, PA.

We present the software architecture of a robotic system for mapping abandoned mines. The software is capable of acquiring consistent 2D maps of large mines with many cycles, represented as Markov random £elds. 3D C-space maps are acquired from local 3D range scans, which are used to identify navigable paths using A* search. Our system has been deployed in three abandoned mines, two of which inaccessible to people, where it has acquired maps of unprecedented detail and accuracy.

Presents a probabilistic algorithm for simultaneously estimating the pose of a mobile robot and the positions of nearby people in a previously mapped environment. This approach, called the conditional particle filter, tracks a large distribution of person locations conditioned upon a smaller distribution of robot poses over time. This method is robust to sensor noise, occlusion, and uncertainty in robot localization. In fact, conditional particle filters can accurately track people in situations with global uncertainty over robot pose. The number of samples required by this filter scales linearly with the number of people being tracked, making the algorithm feasible to implement in real-time in environments with large numbers of people. Experimental results illustrate the accuracy of tracking and model selection, as well as the performance of an active following behavior based on this algorithm.

Describes TEMPEST, a planner that enables a solar-powered rover to reason about path selection and event placement in terms of available solar energy and anticipated power draw. Unlike previous path planners, TEMPEST solves the coupled path, path timing and resource management problem. It combines information about mission objectives, operational constraints, the planetary environment and rover performance, and employs the Incremental Search Engine, a search algorithm that produces optimal paths through high-dimensional spaces. In July 2001, TEMPEST supported the Sun-Synchronous Navigation Field Experiment on Devon Island in the Canadian Arctic. The planner successfully selected time-sequenced, closed-circuit paths that enabled a solar-powered planetary rover prototype to traverse a multi-kilometer path over 24 hours with battery energy reserve. The field trial results motivate future work in mission re-planning, multiple resource constraint analysis and improved speed and memory performance. Our objective is to fulfill a need for resource-cognizant autonomy that is critical for future long-distance planetary surface missions.

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.

Enables robot explorers to maximize the total information gained while minimizing costs such as driving, sensing and planning. The paper presents a general methodology for solving complex exploration tasks which employs multiple sources of information. The paper also develops a specific instantiation of the method to solve the exploration problem of creating a complete traversability map of an known region. Simulation results showing the solution of this exploration task are included.

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.

This research considers the sun's motion and terrain in navigational planning. Robotic exploration of remote areas depends heavily on efficient use of power resources. An orbital ephemeris and terrain data are incorporated into a robotic planner to predict shadowing and solar power generation. The simulation and evaluation of coverage patterns are described, as well as searches for continually sunlit paths or for nearby locations suitable for recharging or communicating with Earth. Aspects of the research were implemented as part of the Robotic Antarctic Meteorite Search project, for which simulations and field test results are given.

Future space facilities that could power our planet and expand our horizons will differ vastly from the satellites and space stations familiar today. Characterized by their immense size and the difficulties of human construction in orbit, future space facilities will be assembled in part by robots. This paper profiles Skyworker, a prototype assembly, inspection, and maintenance (AIM) robot designed for large mass payload transport and assembly tasks. Skyworker is an attached mobile manipulator (AMM) capable of walking and working on the structure it is building.

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.

A Bayes network based classifier for distinguishing terrestrial rocks from meteorites is implemented onboard the Nomad robot. Equipped with a camera, spectrometer and eddy current sensor, this robot searched the ice sheets of Antarctica and autonomously made the first robotic identification of a meteorite, in January 2000 at the Elephant Moraine. This paper discusses rock classification from a robotic platform, and describes the system onboard Nomad.

This paper discusses the challenge of achieving high bandwidth, distant range wireless communication from mobile robots by way of antenna tracking. In the case of robots traversing rough terrain at moderate speeds, tracking demands high slew rates and large motion ranges due to vehicle motion disturbances. Attaining tracking accuracy, particular with the low mass and power inherent to mobile robots, requires an innovative approach. This paper presents the requirements analysis, mechanism design, sensor configuration and some experimental results for an antenna pointing mechanism that was developed for Nomad, a planetary-relevant mobile robot. The mechanism was demonstrated during the summer of 1997 in Nomad's 200 km traverse in the Atacama Desert of Chile.

In June and July 1997, Nomad, a planetary-relevant mobile robot, traversed more than 220 kilometers across the barren Atacama Desert in Chile, exploring a landscape analogous to the surfaces of the Moon and Mars. In this unprecedented demonstration, Nomad operated both autonomously and under the control of operators thousands of kilometers away, addressing issues of robot configuration, communication, position estimation, and navigation in rugged, natural terrain. The field experiment also served to test technologies for remote geological investigation, paving the way for new exploration strategies on Earth and beyond. Finally, by combining safeguarded teleoperation with panoramic visualization and a novel user interface, the Atacama Desert Trek provided the general public a compelling interactive experience an opportunity to remotely drive an exploratory robot. Nomad's performance in the Atacama Desert Trek set new benchmark in high performance robotics operations relevant to terrestrial and planetary exploration. This paper presents an overview of the experiment, describes technologies key to Nomad's success, and discusses outcomes and implications.

During the Summer of 1997, Nomad, a planetary-relevant mobile robot, was driven via satellite link for more than 125 miles in the Atacama Desert of Chile by novice operators in North America, demonstrating many technologies relevant to robotic exploration of the planets. An innovative "transforming" chassis was demonstrated which uses a simple linkage to change the footprint of the vehicle from a stowed to a deployed position. This linkage also enables both double Ackennan and point-turn steering. This paper presents details on the design of the transforming chassis including kinematic analysis used in low and high level control.

Reliable navigation is critical for a lunar rover, both for autonomous traverses and safeguarded remote teleoperation. This paper describes an implemented system that has autonomously driven a prototype wheeled lunar rover over a kilometer in natural, outdoor terrain. The navigation system uses stereo terrain maps to perform local obstacle avoidance, and arbitrates steering recommendations from both the user and the rover. The paper describes the system architecture, each of the major components, and the experimental results to date.

The walker/terrain interaction phenomena for the control of a statically stable walking machine are described. The algorithms, measures, and knowledge of walker/terrain interaction phenomena are then combined to form a prescription for how to walk on general terrain. This prescription consists of two parts: nominal control and reactive control. The function of nominal control is the evaluation and execution of planned motions, based on predicted foot force redistributions, to achieve reliable locomotion. The function of reactive control is the monitoring of walker/terrain interaction in real-time to detect anomalous conditions and then respond with the appropriate reflexive actions. Simulations and experiments have been used to test and verify various aspects of the walking prescription.>

This paper presents a framework for autonomous robots that reason from range data. We argue for spatial reasoning as a basic cognition mode for robots operating in unpredictable work environments and present a three-level architecture for modeling and planning from range data. Two implementations, robotic excavation with sonar ranging and mine navigation with laser ranging, illustrate the techniques and provide two experiences to evaluate the architecture.