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At least 649 records · Page 36

Fast Image Texture Classification Using Decision Trees

Texture analysis would permit improved autonomous, onboard science data interpretation for adaptive navigation, sampling, and downlink decisions. These analyses would assist with terrain analysis and instrument placement in both macroscopic and microscopic image data products. Unfortunately, most state-of-the-art texture analysis demands computationally expensive convolutions of filters involving many floating-point operations. This makes them infeasible for radiation- hardened computers and spaceflight hardware. A new method approximates traditional texture classification of each image pixel with a fast decision-tree classifier. The classifier uses image features derived from simple filtering operations involving integer arithmetic. The texture analysis method is therefore amenable to implementation on FPGA (field-programmable gate array) hardware. Image features based on the "integral image" transform produce descriptive and efficient texture descriptors. Training the decision tree on a set of training data yields a classification scheme that produces reasonable approximations of optimal "texton" analysis at a fraction of the computational cost. A decision-tree learning algorithm employing the traditional k-means criterion of inter-cluster variance is used to learn tree structure from training data. The result is an efficient and accurate summary of surface morphology in images. This work is an evolutionary advance that unites several previous algorithms (k-means clustering, integral images, decision trees) and applies them to a new problem domain (morphology analysis for autonomous science during remote exploration). Advantages include order-of-magnitude improvements in runtime, feasibility for FPGA hardware, and significant improvements in texture classification accuracy.

Thompson, David R.↗

Laser Range and Bearing Finder for Autonomous Missions

NASA has recently re-confirmed their interest in autonomous systems as an enabling technology for future missions. In order for autonomous missions to be possible, highly-capable relative sensor systems are needed to determine an object's distance, direction, and orientation. This is true whether the mission is autonomous in-space assembly, rendezvous and docking, or rover surface navigation. Advanced Optical Systems, Inc. has developed a wide-angle laser range and bearing finder (RBF) for autonomous space missions. The laser RBF has a number of features that make it well-suited for autonomous missions. It has an operating range of 10 m to 5 km, with a 5 deg field of view. Its wide field of view removes the need for scanning systems such as gimbals, eliminating moving parts and making the sensor simpler and space qualification easier. Its range accuracy is 1% or better. It is designed to operate either as a stand-alone sensor or in tandem with a sensor that returns range, bearing, and orientation at close ranges, such as NASA's Advanced Video Guidance Sensor. We have assembled the initial prototype and are currently testing it. We will discuss the laser RBF's design and specifications. Keywords: laser range and bearing finder, autonomous rendezvous and docking, space sensors, on-orbit sensors, advanced video guidance sensor

Granade, Stephen R.↗

Towards Vision-based Safe Autonomous Landing of MAVs at Night

This paper is about vision-based navigation of Micro Aerial Vehicles (MAVs) at night. Despite it being dark almost half of the time, most of the work to date has addressed only daytime operations. Enabling autonomous night-time operation of MAVs with low SWaP on-board sensing capabilities is still an open problem in current robotics research. In this work, we take a step in this direction and study the problem in the context of vision-based autonomous landing at night using thermal-infrared cameras.

Delaune, Jeff↗

Increased range and contrast in fog with circularly polarized imaging

Fogs, low lying clouds, and other highly scattering environments pose a challenge for many commercial and national security sensing systems. Current autonomous systems rely on optical sensors for navigation whose performance is degraded by highly scattering environments. In our previous simulation work, we have shown that polarized light can penetrate through a scattering environment such as fog. We have demonstrated that circularly polarized light maintains its initial polarization state better than linearly polarized light, even through large numbers of scattering events and thus ranges. This has recently been experimentally verified by other researchers. In this work, we present the design, construction, and testing of active polarization imagers at short-wave infrared and visible wavelengths. We explore multiple polarimetric configurations for the imagers, focusing on linear and circular polarization states. The polarized imagers were tested at the Sandia National Laboratories Fog Chamber under realistic fog conditions. Here, we show that active circular polarization imagers can increase range and contrast in fog better than linear polarization imagers. We show that when imaging typical road sign and safety retro-reflective films, circularly polarized imaging has enhanced contrast throughout most fog densities/ranges compared to linearly polarized imaging and can penetrate over 15 to 25 m into the fog beyond the range limit of linearly polarized imaging, with a strong dependence on the interaction of the polarization state with the target materials.

47 OTHER INSTRUMENTATION↗

Reasoning and planning in dynamic domains: An experiment with a mobile robot

Progress made toward having an autonomous mobile robot reason and plan complex tasks in real-world environments is described. To cope with the dynamic and uncertain nature of the world, researchers use a highly reactive system to which is attributed attitudes of belief, desire, and intention. Because these attitudes are explicitly represented, they can be manipulated and reasoned about, resulting in complex goal-directed and reflective behaviors. Unlike most planning systems, the plans or intentions formed by the system need only be partly elaborated before it decides to act. This allows the system to avoid overly strong expectations about the environment, overly constrained plans of action, and other forms of over-commitment common to previous planners. In addition, the system is continuously reactive and has the ability to change its goals and intentions as situations warrant. Thus, while the system architecture allows for reasoning about means and ends in much the same way as traditional planners, it also posseses the reactivity required for survival in complex real-world domains. The system was tested using SRI's autonomous robot (Flakey) in a scenario involving navigation and the performance of an emergency task in a space station scenario.

Georgeff, M. P.↗

Plenoptic Imager for Automated Surface Navigation

An electro-optical imaging device is capable of autonomously determining the range to objects in a scene without the use of active emitters or multiple apertures. The novel, automated, low-power imaging system is based on a plenoptic camera design that was constructed as a breadboard system. Nanohmics proved feasibility of the concept by designing an optical system for a prototype plenoptic camera, developing simulated plenoptic images and range-calculation algorithms, constructing a breadboard prototype plenoptic camera, and processing images (including range calculations) from the prototype system. The breadboard demonstration included an optical subsystem comprised of a main aperture lens, a mechanical structure that holds an array of micro lenses at the focal distance from the main lens, and a structure that mates a CMOS imaging sensor the correct distance from the micro lenses. The demonstrator also featured embedded electronics for camera readout, and a post-processor executing image-processing algorithms to provide ranging information.

Zollar, Byron↗

Uncovering Hazards Using a Multi-Objective Optimization to Explore the Faulty State-Space

Considering resilience when designing complex engineered systems is crucial to ensure the system is safe under unexpected hazardous scenarios. Traditional risk-based approaches, such as Failure Modes and Effects Analysis (FMEA) are useful for designing the system to mitigate hazardous scenarios that can be identified by the designer, but often require experience or prior knowledge of system failures to generate. More recently, researchers have developed simulation tools that enable the designer to model large sets of hazardous scenarios (driven by both internal faults and external factors) through simulation. While these tools enable a wider scope of fault modes to be evaluated (e.g., by injecting combined set of fault modes or injecting modes at different times), the resulting assessments (like FMEA) still require knowledge of the specific modes to be evaluated. However, failure to analyze a wide variety of fault scenarios can lead to an incomplete picture of the system resilience, especially to "surprise events'' which may be difficult for the designer to identify and predict beforehand. To overcome this challenge, previous work developed a fault sampling approach for resilience simulations which would procedurally-generate a wide variety of potential faults by systematically perturbing the health states of the system. While the resulting fault modes generated covered a much larger space hazards than would be otherwise considered (and identified many unique failure trajectories which would not have otherwise been identified), it also significantly increased the computational cost of the analysis and resulted in the simulation and analysis of a large set of essentially duplicate scenarios. Additionally, as the number of dimensions in the faulty state-space increases, the full elaboration of possible modes becomes computationally infeasible, justifying the use of a more targeted search. To resolve this limitation, this work proposes the use of a multiobjective optimization algorithm to search the health state space for potential fault modes that are both (1) hazardous and (2) unique. To solve this type of problem, this work proposes the use of a cooperative co-evolutionary algorithm. To demonstrate this approach, it will be applied to a model of an autonomous rover which uses line markings to navigate, focusing on potential hazards in the drive system which could cause the rover to crash. To determine the merit of the approach, it will further be compared with the previously-presented range elaboration approach and a random mode generation approach on the basis of computational efficiency and found modes.

Resilience↗

Astrobee's Multi-year Activities at the International Space Station's Japanese Experimental Module

The Astrobee free-flying robots recently completed their third successful year of operations, housed in the Japanese Experimental Module (JEM) on the International Space Station. We summarize the three years of operation, giving special attention to JAXA's 1st and 2nd Kibo Robot Programming Challenge (RPC) and the mapping processes and tools that make Astrobees' autonomous operation possible. The JEM is an ever changing, dynamic environment where light settings, cargo, payloads, and crew members constantly move and interact with one another. The 1st JAXA Kibo RPC event, a collaboration between JAXA and NASA, was held in 2020. Students from several countries in the Asia-Pacific region competed in programming challenges with a simulated Astrobee. The finalists were then invited to run their code on an actual Astrobee in the JEM. For the final round, students programmed Astrobee to visit three different locations to obtain data that would instruct the robot to complete a final task with the participation of ISS crew. The first competition was a tremendous success, leading to an equally successful 2nd JAXA Kibo RPC in 2021 with even larger participation. The 3rd JAXA Kibo RPC will occur in 2022 expanding further to incorporate US participants. These activities led to several firsts in Astrobee’s history: operation of an Astrobee free-flying robot without crew supervision in preparation for on-orbit operations, autonomous image acquisition towards updates of the navigation map, non-NASA code running on the robot (both from JAXA and participating students), two heterogeneous free-flying robots from two different space agencies working together (Int-Ball and Astrobee) during the final event in 2020, the first payload using Astrobee, and having Astrobee controlled from a non-NASA location (Tsukuba Space Center). The preparation towards these activities involved constant evaluation of the different components of Astrobee's systems, specially mapping and localization. The paper describes the evolution of these systems such as the improvements made in localization to reduce localization drift by using graph-based optimization instead of the extended Kalman Filter localizer. Additionally, it reports on the mapping process and analysis tools created to validate map consistency across different activities in the constantly changing JEM environment. These enhancements have enabled the Astrobee facility to successfully execute over 100 ISS activities supporting over a dozen researchers and partners around the world.

Astrobee↗

A Long Distance Laser Altimeter for Terrain Relative Navigation and Spacecraft Landing

A high precision laser altimeter was developed under the Autonomous Landing and Hazard Avoidance (ALHAT) project at NASA Langley Research Center. The laser altimeter provides slant-path range measurements from operational ranges exceeding 30 km that will be used to support surface-relative state estimation and navigation during planetary descent and precision landing. The altimeter uses an advanced time-of-arrival receiver, which produces multiple signal-return range measurements from tens of kilometers with 5 cm precision. The transmitter is eye-safe, simplifying operations and testing on earth. The prototype is fully autonomous, and able to withstand the thermal and mechanical stresses experienced during test flights conducted aboard helicopters, fixed-wing aircraft, and Morpheus, a terrestrial rocket-powered vehicle developed by NASA Johnson Space Center. This paper provides an overview of the sensor and presents results obtained during recent field experiments including a helicopter flight test conducted in December 2012 and Morpheus flight tests conducted during March of 2014.

Pierrottet, Diego F.↗

Autonomous docking ground demonstration (category 3)

The NASA Johnson Space Center (JSC) is involved in the development of an autonomous docking ground demonstration. The demonstration combines the technologies, expertise and facilities of the JSC Tracking and Communications Division (EE), Structures and Mechanics Division (ES), and the Navigation, Guidance and Control Division (EG) and their supporting contractors. The autonomous docking ground demonstration is an evaluation of the capabilities of the laser sensor system to support the docking phase (12ft to contact) when operated in conjunction with the Guidance, Navigation and Control Software. The docking mechanism being used was developed for the Apollo Soyuz Test Program. This demonstration will be conducted using the Six-Degrees of Freedom (6-DOF) Dynamic Test System (DTS). The DTS environment simulates the Space Station Freedom as the stationary or target vehicle and the Orbiter as the active or chase vehicle. For this demonstration the laser sensor will be mounted on the target vehicle and the retroreflectors on the chase vehicle. This arrangement was used to prevent potential damage to the laser. The sensor system. GN&C and 6-DOF DTS will be operated closed-loop. Initial condition to simulate vehicle misalignments, translational and rotational, will be introduced within the constraints of the systems involved. Detailed description of each of the demonstration components (e.g., Sensor System, GN&C, 6-DOF DTS and supporting computer configuration) including their capabilities and limitations will be discussed. A demonstration architecture drawing and photographs of the test configuration will be presented.

Lamkin, Steve L.↗

Flight Test Results from the Low Power Transceiver Communications and Navigation Demonstration on Shuttle (CANDOS)

The Low Power Transceiver (LPT) is an advanced signal processing platform that offers a configurable and reprogrammable capability for supporting communications, navigation and sensor functions for mission applications ranging from spacecraft TT&C and autonomous orbit determination to sophisticated networks that use crosslinks to support communications and real-time relative navigation for formation flying. The LPT is the result of extensive collaborative research under NASNGSFC s Advanced Technology Program and ITT Industries internal research and development efforts. Its modular, multi-channel design currently enables transmitting and receiving communication signals on L- or S-band frequencies and processing GPS L-band signals for precision navigation. The LPT flew as a part of the GSFC Hitchhiker payload named Fast Reaction Experiments Enabling Science Technology And Research (FREESTAR) on-board Space Shuttle Columbia s final mission. The experiment demonstrated functionality in GPS-based navigation and orbit determination, NASA STDN Ground Network communications, space relay communications via the NASA TDRSS, on-orbit reconfiguration of the software radio, the use of the Internet Protocol (IP) for TT&C, and communication concepts for space based range safety. All data from the experiment was recovered and, as a result, all primary and secondary objectives of the experiment were successful. This paper presents the results of the LPTs maiden space flight as a part of STS- 107.

Rush, John↗

Automation and robotics technology for intelligent mining systems

The U.S. Bureau of Mines is approaching the problems of accidents and efficiency in the mining industry through the application of automation and robotics to mining systems. This technology can increase safety by removing workers from hazardous areas of the mines or from performing hazardous tasks. The short-term goal of the Automation and Robotics program is to develop technology that can be implemented in the form of an autonomous mining machine using current continuous mining machine equipment. In the longer term, the goal is to conduct research that will lead to new intelligent mining systems that capitalize on the capabilities of robotics. The Bureau of Mines Automation and Robotics program has been structured to produce the technology required for the short- and long-term goals. The short-term goal of application of automation and robotics to an existing mining machine, resulting in autonomous operation, is expected to be accomplished within five years. Key technology elements required for an autonomous continuous mining machine are well underway and include machine navigation systems, coal-rock interface detectors, machine condition monitoring, and intelligent computer systems. The Bureau of Mines program is described, including status of key technology elements for an autonomous continuous mining machine, the program schedule, and future work. Although the program is directed toward underground mining, much of the technology being developed may have applications for space systems or mining on the Moon or other planets.

Welsh, Jeffrey H.↗

AI-Enabled Robots for Automated Nondestructive Evaluation and Repair of Power Plant Boilers. Final Report

Boiler failure could cause loss of life and safety issues, cost hundreds of thousands of dollars in equipment repairs, property damage and production losses, and drive up the cost of electric power. Boiler maintenance is challenging and risky for inspectors working on scaffolding in confined hazardous spaces inside of a boiler and sometimes the space is hard to access. The operation is also time-consuming due to the large area of vertical structures for inspection and the tremendous effort needed for scaffolding. Recently, the use of robotics (e.g., drones and crawlers) in power plants for maintenance is growing rapidly. However, the existing robotics solutions show two notable technological gaps: no live repair capability, and no Artificial Intelligence (AI) for smart autonomy. The objective of this project is to develop an integrated autonomous robotic platform that is equipped with compact non-destructive evaluation (NDE) sensors to perform live inspection, operates onboard repair devices to perform live repair, and uses AI for intelligent data fusion and predictive analysis for automated and smart spatiotemporal inspection, analysis and repair of the furnace walls in coal-fired boilers. The approach to achieve the objective includes developing NDE sensors with signal processing techniques, designing and evaluating repair devices for robots based on fusion and solid-state technologies, and an autonomous robotic platform that can attach to and navigate on boiler furnace walls using magnetic drive tracks. The robot is also powered by AI to automate data gathering (e.g., 3D mapping and damage localization) and predictive analysis. This project has advanced the state-of-the-art by providing technological breakthroughs including compact NDE and repair tools for robots, AI capabilities for smart autonomy, and a robotic platform for automated boiler maintenance. This project has great potential to result in significant benefits including limiting or eliminating the need to send operators to assess difficult-to-access or hazardous areas, enabling automated live inspection and repair, avoiding time consuming scaffolding (especially for partial maintenance during unplanned outage), collecting comprehensive and well-organized data smartly, and avoiding or limiting the need for onsite or remote piloting technicians. The impacts can be tremendous in terms of the time and cost savings, reducing the risk for human operators, and increasing boiler reliability, usability, and efficiency. In addition, by developing the new technologies on the autonomous inspection and repair robot, by involving multiple undergraduate and graduate students working together with the faculty members on this project, and by generating knowledge and building up collaborations with industrial partners, this effort will significantly update the education capabilities, support long-term fundamental research, and maintain the leadership of Colorado School of Mines and Michigan State University in energy fields.

20 FOSSIL-FUELED POWER PLANTS↗

HERMIES-3: A step toward autonomous mobility, manipulation, and perception

HERMIES-III is an autonomous robot comprised of a seven degree-of-freedom (DOF) manipulator designed for human scale tasks, a laser range finder, a sonar array, an omni-directional wheel-driven chassis, multiple cameras, and a dual computer system containing a 16-node hypercube expandable to 128 nodes. The current experimental program involves performance of human-scale tasks (e.g., valve manipulation, use of tools), integration of a dexterous manipulator and platform motion in geometrically complex environments, and effective use of multiple cooperating robots (HERMIES-IIB and HERMIES-III). The environment in which the robots operate has been designed to include multiple valves, pipes, meters, obstacles on the floor, valves occluded from view, and multiple paths of differing navigation complexity. The ongoing research program supports the development of autonomous capability for HERMIES-IIB and III to perform complex navigation and manipulation under time constraints, while dealing with imprecise sensory information.

Weisbin, C. R.↗

Flight Testing ALHAT Precision Landing Technologies Integrated Onboard the Morpheus Rocket Vehicle

A suite of prototype sensors, software, and avionics developed within the NASA Autonomous precision Landing and Hazard Avoidance Technology (ALHAT) project were terrestrially demonstrated onboard the NASA Morpheus rocket-propelled Vertical Testbed (VTB) in 2014. The sensors included a LIDAR-based Hazard Detection System (HDS), a Navigation Doppler LIDAR (NDL) velocimeter, and a long-range Laser Altimeter (LAlt) that enable autonomous and safe precision landing of robotic or human vehicles on solid solar system bodies under varying terrain lighting conditions. The flight test campaign with the Morpheus vehicle involved a detailed integration and functional verification process, followed by tether testing and six successful free flights, including one night flight. The ALHAT sensor measurements were integrated into a common navigation solution through a specialized ALHAT Navigation filter that was employed in closed-loop flight testing within the Morpheus Guidance, Navigation and Control (GN&C) subsystem. Flight testing on Morpheus utilized ALHAT for safe landing site identification and ranking, followed by precise surface-relative navigation to the selected landing site. The successful autonomous, closed-loop flight demonstrations of the prototype ALHAT system have laid the foundation for the infusion of safe, precision landing capabilities into future planetary exploration missions.

Carson, John M. III↗

Positional estimation techniques for an autonomous mobile robot

Techniques for positional estimation of a mobile robot navigation in an indoor environment are described. A comprehensive review of the various positional estimation techniques studied in the literature is first presented. The techniques are divided into four different types and each of them is discussed briefly. Two different kinds of environments are considered for positional estimation; mountainous natural terrain and an urban, man-made environment with polyhedral buildings. In both cases, the robot is assumed to be equipped with single visual camera that can be panned and tilted and also a 3-D description (world model) of the environment is given. Such a description could be obtained from a stereo pair of aerial images or from the architectural plans of the buildings. Techniques for positional estimation using the camera input and the world model are presented.

Nandhakumar, N.↗