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The huge advantage of in-pipe robots is that they are able to measure from inside the pipes, and to sense the geometry, appearance and radiometry directly. The downside is the inability to know precise, absolute position of the measurements in very long pipe runs. This paper develops the unprecedented localization required for this purpose., 12 pages, "Superior Paper Award" on WM Symposia 2019

Miles of contaminated pipe must be measured, foot by foot, as part of the decommissioning effort at deactivated gaseous diffusion enrichment facilities. The current method requires cutting away asbestos-lined thermal enclosures and performing repeated, elevated operations to manually measure pipe from the outside. The RadPiper robot, part of the Pipe Crawling Activity Measurement System (PCAMS) developed by Carnegie Mellon University and commissioned for use at the DOE Portsmouth Gaseous Diffusion Enrichment Facility, automatically measures U-235 in pipes from the inside. This improves certainty, increases safety, and greatly reduces measurement time. The heart of the RadPiper robot is a sodium iodide scintillation detector in an innovative disc-collimated assembly. By measuring from inside pipes, the robot significantly increases its count rate relative to external through-pipe measurements. The robot also provides imagery, models interior pipe geometry, and precisely measures distance in order to localize radiation measurements. Data collected by this system provides insight into pipe interiors that is simply not possible from exterior measurements, all while keeping operators safer. This paper describes the technical details of the PCAMS RadPiper robot. Key features for this robot include precision distance measurement, in-pipe obstacle detection, ability to transform for two pipe sizes, and robustness in autonomous operation. Test results demonstrating the robot's functionality are presented, including deployment tolerance tests, safeguarding tests, and localization tests. Integrated robot tests are also shown.

To decommission deactivated gaseous diffusion enrichment facilities, miles of contaminated pipe must be measured. The current method requires thousands of manual measurements, repeated manual data transcription, and months of manual analysis. The Pipe Crawling Activity Measurement System (PCAMS), developed by Carnegie Mellon University and in commissioning for use at the DOE Portsmouth Gaseous Diffusion Enrichment Facility, uses a robot to measure Uranium-235 from inside pipes and automatically log the data. Radiation measurements, as well as imagery, geometric modeling, and precise measurement positioning data are digitally transferred to the PCAMS server. On the server, data can be automatically processed in minutes and summarized for analyst review. Measurement reports are auto-generated with the push of a button. A database specially-configured to hold heterogeneous data such as spectra, images, and robot trajectories serves as archive. This paper outlines the features and design of the PCAMS Post-Processing Software, currently in commissioning for use at the Portsmouth Gaseous Diffusion Enrichment Facility. The analysis process, the analyst interface to the system, and the content of auto-generated reports are each described. Example pipe-interior geometric surface models, illustration of how key report features apply in operational runs, and user feedback are discussed.

Robotics Institute

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.

Robotic regolith excavation on the Moon and Mars enables outposts, fuel depots, and sustained space exploration. In any space mission, mass is always at a premium because it is the main driver behind launch costs. Low mass and reduced gravity (1/6 of Earth gravity on the Moon, 1/3 on Mars) results in machines with limited weight available to produce traction or plunge tools into regolith. Bucket-wheel excavators have been shown to produce low resistance forces that enable lightweight operation, but in the past have had difficulty transferring regolith from bucket-wheel to collection bin. Exposed conveyors and chains fare poorly in harsh lunar regolith and vacuum. A novel excavator configuration, with bucket-wheel mounted centrally and transverse to driving direction, achieves direct transfer into a collection bin. Experiments with a bucket-wheel digging in lunar simulant show that transverse bucket-wheel orientation does not increase resistance significantly. Excavation resistance is shown to depend mostly on the ratio of bucket-wheel rotation rate to forward advance rate.

Controlling slip is crucial to reliable descent of steep unconsolidated slopes such as those found in lunar craters. The ability to enter these craters provides opportunities to explore potential in-situ resources, such as water ice. We present a rover prototype with a novel, actuated, omni-directional plowing device and control method for maneuvering on steep slopes. Data from field experiments show reliable control during descent on loose sand slopes up to 40°, with twenty-fold reduction in downhill slip and a threefold reduction in slip during point turns. In particular, the data indicates that plowing eliminates slip caused by shear failure created in angle-of-repose material.

Scarab is a demonstration of a lunar rover design to explore polar cold traps for water ice as a potential resource. The envisioned mission scenario lands the rover on the floor of a permanently shadowed crater. The radioisotope powered rover then traverses kilometers in darkness, stopping to drill into the near subsurface and take data. The vehicle design employs a passive kinematic suspension with an active adjustability to lower for drilling and aid in driving. Scarab was designed and built in 2007 and is currently in lab and field testing and further development.

The primary challenge of descending steep, loose slopes is avoiding avalanche, tipover and maintaining control of locomotion. Theory suggests that for loose, granular soil,the strength lies under, not on, the surface. This has not hitherto been exploited for controlling robot descent. In order to take advantage of this, a prototype robot capable of utilizing sub-surface strength was developed and tested. Two unique features are low center-of gravity and an actuated plow capable of penetrating the ground for controlling descent and facilitating pivot. Tests show unprecedented control during descent on loose soil slopes up to 40 degrees. Avalanche sliding is reduced twenty-fold , pivot turning is improved threefold.

The Urban Challenge represents a technological leap beyond the previous Grand Challenges. The challenge encompasses three primary behaviors: driving on roads, handling intersections and maneuvering in zones. In implementing urban driving we have decomposed the problem into five components. Mission Planning determines an efficient route through an urban network of roads. A behavioral layer executes the route through the environment, adapting to local traffic and exceptional situations as necessary. A motion planning layer safeguards the robot by considering the feasible trajectories available, and selecting the best option. Perception combines data from lidar, radar and vision systems to estimate the location of other vehicles, static obstacles and the shape of the road. Finally, the robot is a mechatronic system engineered to provide the power, sensing and mobility necessary to navigate an urban course. Rigorous component and system testing evaluates progress using standardized tests. Observations from these experiments shape the design of subsequent development spirals and enable the rapid detection and correction of bugs. The system described in the paper exhibits a majority of the basic navigation and traffic skills required for the Urban Challenge. From these building blocks more advanced capabilities will quickly develop.

Good human drivers adjust radii, favor lanes and inherently set speeds while racing. They gracefully enter and exit turns, and "read the terrain" or use foreknowledge of the course to slow down for harsh terrain features. Robots do not yet do this without the benefit of preplanning. This paper describes technologies and methodologies for preplanning including: path detailing, speed setting, terrain knowledge, and verification. The result of preplanning is the generation of two high performance, successful routes for two autonomous robots in the 2005 Grand Challenge traverse of 132 miles in about 7 hours.

In January 2000 the Nomad robot searched an area of blue ice in Antarctica and autonomously classified 5 in-situ meteorites. The robotic capabilities of search and target identification, coupled with the scientific capabilities of analysis and classification of rocks in an extreme environment were made possible by the integration of many different technologies for both hardware and software. This paper focuses on the development and integration of the sensor arm used to deploy a spectrometer from a multi-meter scale robot to centimeter scale rocks. The sensor arm combines off the shelf hardware for motion control, actuation, and sensing. Available techniques were applied in the areas of kinematics, visual servoing and image segmentation. The successful demonstration of the robotic search for Antarctic meteorites serves as a benchmark for the advancement of both custom designed and off the shelf robotic technologies.

Meteorites are the only significant source of material from other planets and asteroids, and therefore are of immense scientific value. Antarctica’s frozen and pristine environment has proven to be the best place on Earth to harvest meteorite specimens. The lack of melting and surface erosion keep meteorite falls visible on the ice surface in pristine condition for thousands of years. In this article we describe the robotic technologies and field demonstration that enabled the first discovery 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 found and classified five indigenous meteorites during an expedition to the remote site of Elephant Moraine in January 2000. This article first overviews Nomad’s mechatronic systems, and details the control architecture that governs the robot’s autonomy and classifier that enables the autonomous interpretation of scientific data. It then focuses on the technical results achieved during field demonstrations at Elephant Moraine. Finally, the article discusses the benefits and limitations of robotic autonomy in science missions. Science autonomy is shown as a capable and expandable architecture for exploration and in situ classification. Inefficiencies in the existing implementation are explained with a focus on important lessons that outline future work.

Robotic search for meteorites in Antarctica is an ideal test case for demonstration and field validation of planetary science rovers. Antarctica’s lengthy diurnal cycle, its harshness and its remote location present conditions and challenges similar to those encountered in missions to the poles of the Moon and Mars. This project has researched and developed the technologies and capabilities of an autonomous robot for the search of Antarctic meteorites. Nomad, a robot that explored 220 km of the Atacama desert in 1997, was winterized and outfitted with sensors and onboard intelligence for detection and in situ classification of rocks and meteorites. Nomad’s autonomous perception and navigation capabilities are appropriate for excursions in polar environments. This article first introduces the science and search for Antarctic meteorites and overviews Nomad’s robotic technologies. It then details Science Autonomy, Nomad’s control architecture and its functionality. The remainder of the article discusses Nomad’s performance in a meteorite search as it traversed ice terrains and endured harsh conditions in Patriot Hills, western Antarctica, during a six week expedition in late fall of 1998.

The goal of the Robotic Antarctic Meteorite Search is to enable discovery of meteorites in Antarctica by a mobile robot. The extreme environment makes it one of the best places to find meteorites, but one of the worst places for humans to work. The meteorite-finding robot will traverse an ice field in a pattern designed to cover the area completely, stopping to investigate potential meteorites with an array of sensors. High level autonomy is needed for this project in many areas: scientific sensing, scientific analysis, navigational sensing, navigational planning, and mission planning. Navigational planning for this project primarily involves generating coverage patterns for traversing the ice fields as completely as possible. Multiple types of patterns are considered, and some of their benefits and problems are discussed. Several of the coverage patterns have been tested in a gravel slag heap in Pittsburgh and on the ice in Antarctica, and the results of these tests are described. The affect of different environments and robot locomotion configurations on control issues, such as the implementation of the pure pursuit algorithm, and maintaining and regaining a path, are discussed.

"This report describes progress in research on an autonomous robot for planetary exploration performed during 1989 at the Robotics Institute, Carnegie Mellon University. The report begins with an introduction, summary of achievements, and lists of personnel and publications. It also includes several papers resulting from the research."

"This report describes progress in development of an integrated mobile robot system at the Robotics Institute of Carnegie Mellon University from July 1988 to December 1989."

Robotics Institute

"This report describes progress in development of an integrated mobile robot system at the Carnegie Mellon Robotics Institute from July 1987 to June 1988. This research was sponsored by the Defense Advanced Research Projects Agency and monitored by the US Army Engineer Topographic Laboratories under contract DACA76-86-C-0019. Our program includes a broad agenda of research in the development of moble robot vehicles, focused on the NAVLAB computer-controlled van. In the year covered by this report, we addressed major issues in both hardware and software for autonomous mobile robots: [a] Evolution of the NAVLAB vehicle. We built the NAVLAB mobile robot vehicle in our previous work under this contract, by outfitting a commercial truck chassis with computer-controlled drive and steering controls and a set of on-board computer workstations.The NAVLAB serves as a mobile navigation laboratory that allows researchers to interact intensively with the system during testing and execution. This year has seen a continued evolution and improvement of the NAVLAB mechanism, sensors, controller, and Virtual Vehicle interface to higher-level planning and perception software. [b] Evolution of the CODGER Blackboard. Last year, as part of this research program, we designed and implemented the CODGER blackboard system for robot perception and reasoning on a distributed collection of processors. This year, in response to our experience in using CODGER for mobile robot control, we have upgraded it to deal with geometric models and uncertainty in perception and map data.[c] Experiments With the Driving Pipeline. To control the NAVLABand Terregator mobile robot vehicles, we developed the Driving Pipeline architecture last year for coordinating road following, obstacle avoidance, and vehicle motion control. In our ongoing research, we have performed numerous experiments with this system that demonstrate its value. This hardware and software is the basis for the New Generation System (NGS) for robot vision and navigation, which integrates many independent technologies to produce an integrated mobile robot system."