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An electromechanical hand-and-forearm assembly has been developed for incorporation into an anthropomorphic robot that would be used in outer space. The assembly is designed to offer manual dexterity comparable to that of a hand inside an astronaut s suit; thus, the assembly may also be useful as a prosthesis or as an end effector on an industrial robot.
Modified Maltese-cross force sensor larger and more sensitive than earlier designs. Measures inertial forces and torques exerted on large robot arms during free movement as well as those exerted by claw on manipulated objects. Large central hole of sensor allows claw drive mounted inside arm instead of perpendicular to its axis, eliminating potentially hazardous projection. Originally developed for Space Shuttle, sensor finds applications in large industrial robots.
Many manufacturing assembly tasks require fine compliant motion and fast, accurate positioning. Conventional robots perform poorly in these tasks because of their large mass, friction and backlash in gears, cogging in drive motors and other deleterious effects. Even robots equipped with special control systems enabling compliant operation offer only partial solutions. It is difficult or impossible to automate many product assemblies requiring fine, compliant motion. This problem can be greatly alleviated by dividing the manipulation system into coarse and fine domains. In this scenario, a standard industrial robot can serve as a coarse positioner which in turn carries a six degrees of freedom fine motion wrist. Thus the robot can access a workspace measured in meters at low bandwidth and low resolution while the wrist can move over millimeters at high bandwidth and high resolution during the final phase of the assembly operation. Work indicates that fine motion wrists using Lorentz levitation can greatly augment the accuracy and dexterity of robots because they are frictionless, have high bandwidths and have a single back drivable moving part. Also, since there is no contact between the moving and stationary parts, wear and contamination can be eliminated. The use of six Lorentz force actuators in combination with real time position and orientation sensing offers several important advantages over magnetic bearing approaches.
Improved method of mathematical modeling of dynamics of flexible robotic manipulators developed for use in controlling motions of manipulators. Involves accounting for effect, upon modes of vibration of manipulator, of changes in configuration of manipulator and manipulated payload(s). Flexible manipulator has one or more long, slender articulated link(s), like those used in outer space, method also applicable to terrestrial industrial robotic manipulators with relatively short, stiff links, or to such terrestrial machines as construction cranes.
A relatively simple, inexpensive, and generic technique that could be used in both laboratories and some operation site environments is introduced at the Robotics Applications and Development Laboratory (RADL) at Kennedy Space Center (KSC). In addition, this report gives a detailed explanation of the set up procedure, data collection, and analysis using this new technique that was developed at the State University of New York at Farmingdale. The technique was used to evaluate the repeatability, accuracy, and overshoot of the Unimate Industrial Robot, PUMA 500. The data were statistically analyzed to provide an insight into the performance of the systems and components of the robot. Also, the same technique was used to check the forward kinematics against the inverse kinematics of RADL's PUMA robot. Recommendations were made for RADL to use this technique for laboratory calibration of the currently existing robots such as the ASEA, high speed controller, Automated Radiator Inspection Device (ARID) etc. Also, recommendations were made to develop and establish other calibration techniques that will be more suitable for site calibration environment and robot certification.
In this work, we present two robotic sample changers integrated into the experimental stations for the macromolecular crystallography (MX) beamlines AMX and FMX, and the biological small-angle scattering (bioSAXS) beamline LiX. They enable fully automated unattended data collection and remote access to the beamlines. The system designs incorporate high-throughput, versatility, high-capacity, resource sharing and robustness. All systems are centered around a six-axis industrial robotic arm coupled with a force torque sensor and in-house end effectors (grippers). They have the same software architecture and the facility standard EPICS-based BEAST alarm system. The MX system is compatible with SPINE bases and Unipucks. It comprises a liquid nitrogen dewar holding 384 samples (24 Unipucks) and a stay-cold gripper, and utilizes machine vision software to track the sample during operations and to calculate the final mount position on the goniometer. The bioSAXS system has an in-house engineered sample storage unit that can hold up to 360 samples (20 sample holders) which keeps samples at a user-set temperature (277 K to 300 K). The MX systems were deployed in early 2017 and the bioSAXS system in early 2019.
Much progress has been reported in the areas of modeling and control of nonlinear dynamic systems in a continuous-time framework. From implementation point of view, however, it is essential to study these nonlinear systems directly in a discrete setting that is amenable for interfacing with digital computers. But to develop discrete models and discrete controllers for a nonlinear system such as robot is a nontrivial task. Robot is also inherently a variable-inertia dynamic system involving additional complications. Not only the computer-oriented models of these systems must satisfy the usual requirements for such models, but these must also be compatible with the inherent capabilities of computers and must preserve the fundamental physical characteristics of continuous-time systems such as the conservation of energy and/or momentum. Preliminary issues regarding discrete systems in general and discrete models of a typical industrial robot that is developed with full consideration of the principle of conservation of energy are presented. Some research on the pertinent tactile information processing is reviewed. Finally, system control methods and how to integrate these issues in order to complete the task of discrete control of a robot manipulator are also reviewed.
Space Shuttle's Remote Manipulator System (Canadarm) is a 50 foot robot arm used to deploy, retrieve or repair satellites in orbit. Initial spinoff version is designed to remove, inspect and replace large components of Ontario Hydro's CANDU nuclear reactors, which supply 50 percent of Ontario Hydro's total power reduction. CANDU robot is the first of SPAR's Remote Manipulator Systems intended for remote materials handling operations in nuclear servicing, chemical processing, smelting and manufacturing. Inco Limited used remote manipulator for remote control mining equipment to enhance safety and productivity of Inco's hardrock mining operations. System not only improves safety in a hazardous operation that costs more than a score of lives annually, it also increases productivity fourfold. Remote Manipulator System Division is also manufacturing a line of industrial robots and developing additional system for nuclear servicing, mining, defense and space operations.
A simple approach to configuration control of redundant robots is presented. The redundancy is utilized to control the robot configuration directly in task space, where the task will be performed. A number of task-related kinematic functions are defined and combined with the end-effector coordinates to form a set of configuration variables. An adaptive control scheme is then utilized to ensure that the configuration variables track the desired reference trajectories as closely as possible. Simulation results are presented to illustrate the control scheme. The scheme has also been implemented for direct online control of a PUMA industrial robot, and experimental results are presented. The simulation and experimental results validate the configuration control scheme for performing various realistic tasks.
Autonomous industrial assembly relies on the precise measurement of spatial constraints as designed by computer-aided design (CAD) software such as SolidWorks. This paper proposes a framework for an intelligent industrial robot to understand the spatial constraints for model assembly. An extended generative adversary network (GAN) with a 3D long short-term memory (LSTM) network was designed to composite 3D point clouds from a single RGB-D scan. The spatial constraints of the segmented point clouds are identified by a neural-logic network that incorporates general knowledge of spatial constraints in terms of first-order logic. The model was designed to comprehend a complete set of spatial constraints that are consistent with industrial CAD software, including left, right, above, below, front, behind, parallel, perpendicular, concentric, and coincident relations. The accuracy of 3D model composition and spatial constraint identification was evaluated by the RGB-D scans and 3D models in the ABC dataset. The proposed model achieved 57.23% intersection over union (IoU) in 3D model composition, and over 99% in comprehending all spatial constraints.
Robotics has become a key technology consideration for the Space Station project to enable enhanced crew productivity and to maximize safety. There are many robotic functions currently being studied, including Space Station assembly, repair, and maintenance as well as satellite refurbishment, repair, and retrieval. Another area of concern is that of providing ground based experimenters with a natural interface that they might directly interact with their hardware onboard the Space Station or ancillary spacecraft. The state of the technology is such that the above functions are feasible; however, considerable development work is required for operation in this gravity-free vacuum environment. Furthermore, a program plan is evolving within NASA that will capitalize on recent government, university, and industrial robotics research and development (R and D) accomplishments. A brief summary is presented of the primary technology issues and physical examples are provided of the state of the technology for the initial operational capability (IOC) system as well as for the eventual final operational capability (FOC) Space Station.
In the future, robotic systems will be expected to perform important tasks in space, in orbit and in planetary exploration. In orbit, current technology requires that tasks such as the repair, construction and maintenance of space stations and satellites be performed by astronaut Extra Vehicular Activity (EVA). Eliminating the need for astronaut EVA through the use of space manipulators would greatly reduce both mission costs and hazards to astronauts. In planetary exploration, cost and logistical considerations clearly make the use of autonomous and telerobotic systems also very attractive, even in cases where an astronaut explorer might be in the area. However, such applications introduce a number of technical problems not found in conventional earth-bound industrial robots. To design useful and practical systems to meet the needs of future space missions, substantial technical development is required, including in the areas of the design, control and planning. The objectives of this research program were to develop such design paradigms and control and planning algorithms to enable future space robotic systems to meet their proposed mission objectives. The underlying intellectual focus of the program is to construct a set of integrated design, planning and control techniques based on an understanding of the fundamental mechanics of space robotic systems. This work was to build upon the results obtained in our previous research in this area supported by NASA Langley Research Center in which we have made important contributions to the area of space robotics.
In the future, robotic systems will be expected to perform important tasks in space, in orbit and in planetary exploration. In orbit, current technology requires that tasks such as the repair, construction and maintenance of space stations and satellites be performed by astronaut Extra Vehicular Activity (EVA). Eliminating, the need for astronaut EVA through the use of space manipulators would greatly reduce both mission costs and hazards to astronauts. In planetary exploration, cost and logistical considerations clearly make the use of autonomous and telerobotic systems also very attractive, even in cases where an astronaut explorer might be in the area. However, such applications introduce a number of technical problems not found in conventional earth-bound industrial robots. To design useful and practical systems to meet the needs of future space missions, substantial technical development is required, including in the areas of the design, control and planning. The objectives of this research program were to develop such design paradigms and control and planning algorithms to enable future space robotic systems to meet their proposed mission objectives. The underlying intellectual focus of the program is to construct a set of integrated design, planning and control techniques based on an understanding of the fundamental mechanics of space robotic systems. This work was to build upon the results obtained in our previous research in this area supported by NASA Langley Research Center in which we have made important contributions to the area of space robotics. This program was proposed and accepted as a three year research program, a period of time necessary to make the type of fundamental developments to make a significant contributions to space robotics. Unfortunately, less than a year into the program it became clear that the NASA Langley Research Center would be forced by budgetary constraints to essentially leave this area of research. As a result, the total funding we received under this grant represented approximately one year of the original, proposed and approved, funding. For some time, there was substantial uncertainty that even this very reduced level of funding would be provided. The spending of the reduced available funds was spread just over two years to provide the support to permit the MS students who had joined the program to receive their master's degree and terminate their studies in this area.
Abstract Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available at https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf ). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas.
A compilation of several mechanized systems is presented. The articles are contained in three sections: robotics, industrial mechanical systems, including several on linear and rotary systems and lastly mechanical control systems, such as brakes and clutches.
Analog circuit is employed to smooth change between levels of quantized voltage signal without adversely affecting its fidelity. Circuit is applicable to units requiring interface between digital and analog systems such as automated manufacturing systems or industrial robots.
Advanced teleoperators are discussed, with emphasis on the remote manipulation system designed to perform such actions as grasping, orienting, moving, placing, and inserting objects. Geometrical performance dimensions are considered, indicating that a manipulator is limited to three-orders-of-magnitude ratio of workspace extension to positioning accuracy. The control system is examined, showing that a manipulative task requires coordination of several joints, with the relationship between the task definers and the joint variables given by complex trigonometrical transformations. Control modes developed in the last 30 years are noted, including program controlled industrial 'robots' which can endlessly repeat a fixed sequence of motions without operator intervention, and the sensor-referenced/computer-controlled mode manipulators. Advanced proximity sensor systems are taken into account, with emphasis on the applications of the force-torque and slip models. The proximity sensor system for the shuttle-size manipulator is described.
A process sequence which can be used in conjunction with automated equipment for the mass production of solar cell modules for terrestrial use was developed. The process sequence was then critically analyzed from a technical and economic standpoint to determine the technological readiness of certain process steps for implementation. The steps receiving analysis were: back contact metallization, automated cell array layup/interconnect, and module edge sealing. For automated layup/interconnect, both hard automation and programmable automation (using an industrial robot) were studied. The programmable automation system was then selected for actual hardware development.