TeMoto: A Software Framework for Adaptive and Dependable Robotic Autonomy With Dynamic Resource Management
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The Spaceport Command and Control System will be the National Aeronautics and Space Administration's newest system for launching commercial and government owned spacecraft. It's a large system with many parts all in need of testing. To improve upon testing already done by NASA engineers, the Engineering Directorate, Electrical Division (NE-E) of Kennedy Space Center has hired a group of interns each of the last few semesters to develop novel ways of improving the testing process.
This paper will discuss the project I was assigned during the 2018 Spring semester NIFS internship, what the project does for NASA, and what I contributed to the project. It will begin with a broad analysis of the types of software I worked on and how they were used to complete the project. Then, it will narrow in scope to concentrate on the group and parts of NASA assigned the project. Next, it will pay attention to the project itself and how it worked. After that, it will focus on my direct contributions to the project. Lastly, a few personal experiences I encountered to make my internship relatable. The initial points discussed will be an introduction to the basics of automated testing and the types of software used. Next, I will briefly discuss why these tools were chosen and whether or not they can be improved. Then, a look at where my group fits into the hierarchy of NASA, what the group working on my project did for NASA, and who were the entities working together for this project. Afterwards, a review of my involvement in the program, my development as an individual, and my new skills and abilities. Finally, a sense of involvement with the community through personal development will be demonstrated using unique experiences only available to me through this internship..
Monitoring accelerators in operation is a well known challenge due to the radiation environment. However, there are significant benefits in being able to deploy particular sensors in specific locations of accelerator enclosures for monitoring or troubleshooting purposes. Learning from experience at other labs, we used Commercial Off The Shelf (COTS) components and an open source robot control software framework (ROS) to build a remotely controlled robot platform including a standard suite of instruments such as cameras, LIDAR, and ultrasound, with the ability to incorporate other ad-hoc sensors for specific measurements, such as a Gamma radiation monitor. Special attention was given to the robot's ability to maintain safety in the high risk environment of an accelerator, with multiple failure contingencies in place to ensure collision free operation. Testing was performed to prove the platform's viability and showcase its capability for accelerator monitoring.
A variety of artificial intelligence techniques which could be used with regard to NASA space applications and robotics were evaluated. The techniques studied were decision tree manipulators, problem solvers, rule based systems, logic programming languages, representation language languages, and expert systems. The overall structure of a robotic simulation tool was defined and a framework for that tool developed. Nonlinear and linearized dynamics equations were formulated for n link manipulator configurations. A framework for the robotic simulation was established which uses validated manipulator component models connected according to a user defined configuration.
A robotic system for performing an autonomous task includes a humanoid robot having a plurality of compliant robotic joints, actuators, and other integrated system devices that are controllable in response to control data from various control points, and having sensors for measuring feedback data at the control points. The system includes a multi-level distributed control framework (DCF) for controlling the integrated system components over multiple high-speed communication networks. The DCF has a plurality of first controllers each embedded in a respective one of the integrated system components, e.g., the robotic joints, a second controller coordinating the components via the first controllers, and a third controller for transmitting a signal commanding performance of the autonomous task to the second controller. The DCF virtually centralizes all of the control data and the feedback data in a single location to facilitate control of the robot across the multiple communication networks.
The use of the cognitive capabilties of humans to help guide the autonomy of robotics platforms in what is typically called "supervised-autonomy" is becoming more commonplace in robotics research. The work discussed in this paper presents an approach to a human-in-the-loop mode of robot operation that integrates high level human cognition and commanding with the intelligence and processing power of autonomous systems. Our framework for a "Supervised Remote Robot with Guided Autonomy and Teleoperation" (SURROGATE) is demonstrated on a robotic platform consisting of a pan-tilt perception head, two 7-DOF arms connected by a single 7-DOF torso, mounted on a tracked-wheel base. We present an architecture that allows high-level supervisory commands and intents to be specified by a user that are then interpreted by the robotic system to perform whole body manipulation tasks autonomously. We use a concept of "behaviors" to chain together sequences of "actions" for the robot to perform which is then executed real time.
In order to establish a common software framework for its robotic systems, the United States Department of Energy - Environmental Management (DOE-EM) is considering the possibility of extending the Robot Operating System (ROS) open source robotics software to address the varied, complex challenges facing DOE-EM. The effort, preliminarily called ROS-DOE, would enhance the reliability, robustness, security, and user friendliness of ROS to meet the requirements of the nuclear waste remediation mission, and those of DOE as a whole. As envisioned, the ROS DOE program would also support rapid progress to mature cross-cutting capabilities including supervised autonomy, inter-operability with off-the-shelf industrial equipment, and physics-based robot simulation (i.e., Gazebo). This paper provides background context about the current state of ROS software and its supporting community, surveys DOE-EM needs and requirements for robotic solutions, and analyzes the gaps between the current state of ROS and those needed for DOE-EM robotics. It then identifies new capabilities that are recommended for ROS-DOE and outlines software development practices that could be leveraged to blend the best of the open-source world with high-reliability software development practices. (authors)
This report documents the findings and analysis of a 60-day agency-wide Lunar Robotic Architecture Study (LRAS) conducted by the National Aeronautics and Space Administration (NASA). Work on this study began in January 2006. Its purpose was to: Define a lunar robotics architecture by addressing the following issues: 1) Do we need robotic missions at all? If so, why and under what conditions? 2) How would they be accomplished and at what cost? Are they within budget? 3) What are the minimum requirements? What is the minimum mission set? 4) Integrate these elements together to show a viable robotic architecture. 5) Establish a strategic framework for a lunar robotics program. The LRAS Final Report presents analysis and recommendations concerning potential approaches related to NASA s implementation of the President's Vision for Space Exploration. Project and contract requirements will likely be derived in part from the LRAS analysis and recommendations contained herein, but these do not represent a set of project or contract requirements and are not binding on the U.S. Government unless and until they are formally and expressly adopted as such. Details of any recommendations offered by the LRAS Final Report will be translated into implementation requirements. Moreover, the report represents the assessments and projects of the report s authors at the time it was prepared; it is anticipated that the concepts in this report will be analyzed further and refined. By the time some of the activities addressed in this report are implemented, certain assumptions on which the report s conclusions are based will likely evolve as a result of this analysis. Accordingly, NASA, and any entity under contract with NASA, should not use the information in this report for final project direction. Since the conclusion of this study, there have been various changes to the Agency's current portfolio of lunar robotic precursor activities. First, the Robotic Lunar Exploration Program (RLEP) has been renamed the Lunar Precursor and Robotic Program (LPRP). On May 17, 2006, the Lunar Reconnaissance Orbiter (LRO) was confirmed to enter its implementation phase. Last, a new low-cost secondary payload known as the Lunar Crater Observation and Sensing Satellite (LCROSS) was co-manifested to launch with LRO in 2008. These changes are consistent with the conclusions and recommendations of this study, but came too late to be specifically reflected in this report.
In this article, we will present an overview of the Coupled Layered Architecture for Robotic Autonomy. CLARAty develops a framework for generic and reusable robotic components that can be adapted to a number of heterogeneous robot platforms.
Integration of robots into scientific user facilities, such as the National Synchrotron Light Source II, improves their efficiency and capacity. Many such facilities use the opensource Bluesky project for experimental control and orchestration. However, there remains an open challenge in deploying robotic solutions at these facilities that are reconfigurable, extensible, and compatible with pre-existing software infrastructure. Herein, we introduce a framework that uses the Robotic Operating System 2 (ROS2) and Bluesky to provide extensible robotic applications, while working under the operational constraints of a large-scale user facility. We demonstrated this framework by integrating a robotic arm to pick and place a sample holder at a beamline, recording a 90% repeatability rate. This provides the groundwork for further new robotics applications at large-scale scientific user facilities that depend on Bluesky.
Incorporating multiple robotic manipulators into large-scale manufacturing systems enhances production efficiency and expands manufacturing capabilities beyond those of single-robot systems. Workpiece positioners in robotic manufacturing have demonstrated significant benefits for process optimization, but coordination strategies for multi-robot systems with shared positioners have received limited attention. This work presents a task-space coordinated trajectory-tracking control framework for multi-robot manufacturing systems, in which robots coordinate their motions within a shared, dynamic workpiece positioning frame. A workpiece positioner actively adjusts the pose of the manufactured component to enable greater operational concurrency and improve overall production efficiency. The proposed motion-coordination scheme employs a distributed and scalable architecture, supporting coordination across heterogeneous multi-robot systems. Two optimization methodologies are introduced to manage kinematic redundancies and maintain continuous, near-optimal operation throughout the manufacturing process. The first strategy exploits a task-space dimensionality reduction to achieve locally optimal configurations by leveraging symmetry-axis rotations of the tool. The second strategy utilizes the workpiece positioner to drive the coordinated robots toward stable and kinematically favorable configurations. For both optimization strategies, multiple objectives are defined to improve key performance metrics, including manipulability, configuration consistency, proximity to mechanical limits, and motion efficiency. Addressing a key limitation of existing coordination approaches, the framework is designed around online setpoint modification, allowing coordinated robots to respond effectively to in-situ process feedback. The proposed control framework is validated using the Robot Operating System (ROS) middleware on a combination of physical and simulated multi-robot system hardware.
This report addresses the problem of path planning and control of robotic manipulators which have joint-position limits and joint-rate limits. The manipulators move autonomously and carry out variable tasks in a dynamic, unstructured and cluttered environment. The issue considered is whether the robotic manipulator can achieve all its tasks, and if it cannot, the objective is to identify the closest achievable goal. This problem is formalized and systematically solved for generic manipulators by using inverse kinematics and forward kinematics. Inverse kinematics are employed to define the subspace, workspace and constrained workspace, which are then used to identify when a task is not achievable. The closest achievable goal is obtained by determining weights for an optimal control redistribution scheme. These weights are quantified by using forward kinematics. Conditions leading to joint rate limits are identified, in particular it is established that all generic manipulators have singularities at the boundary of their workspace, while some have loci of singularities inside their workspace. Once the manipulator singularity is identified the command redistribution scheme is used to compute the closest achievable Cartesian velocities. Two examples are used to illustrate the use of the algorithm: A three link planar manipulator and the Unimation Puma 560. Implementation of the derived algorithm is effected by using a supervisory expert system to check whether the desired goal lies in the constrained workspace and if not, to evoke the redistribution scheme which determines the constraint relaxation between end effector position and orientation, and then computes optimal gains.
A paradigm for the specification of robotic behavior is proposed, based on a template of control and data elements replicated in a hierarchical lattice. Considerations from physiological psychology are proposed as control elements for the model. The resulting model allows freedom in implementational details within a symmetrical design, and should also provide a framework for evolving robotic behavior from relatively primitive to extremely complex. The model's implications for realizing learning capabilities in robotic systems are discussed, and its implementation and demonstration are described.
An adaptive state space architecture is combined with diktiometric representation to provide the framework for designing a robot mapping system with flexible navigation planning tasks. This involves indexing waypoints described as expectations, geometric indexing, and perceptual indexing. Matching and updating the robot's projected position and sensory inputs with indexing waypoints involves matchers, dynamic priorities, transients, and waypoint restructuring. The robot's map learning can be opganized around the principles of passive mapping.
This paper describes a control system framework using G-Code-based 3D printing paths on a serial link robot manipulator with multiple degrees of freedom. Usually, G-Code is created by a software application, commonly referred to as a slicer, meant for gantry systems. However, G-Code does not address the kinematic complexity nor take advantage of the flexibility available in serial link robot manipulators. This paper provides an overview of the additive manufacturing process and G-Code, types of additive manufacturing deposition movements, common terminology used, the roles of parsers and translators, step-by-step instructions on how to implement this control system, and results and findings from this research. The presented framework can be used for a number of additive manufacturing methods, hybrid solutions, or applications not directly related to additive manufacturing. The implementation was successfully tested on a manipulator with seven degrees of freedom that successfully performed hundreds of hours of large-scale wire arc metal deposition.
The Deep Underground Neutrino Experiment (DUNE) is a flagship international collaboration designed to study neutrinos tiny, nearly massless particles that may hold answers to fundamental questions about the Universe. Fermilab s Robotic Test Stand (RTS) plays a critical role in ensuring the quality of approximately 50,000 Application-Specific Integrated Circuit (ASIC) chips that will be used in DUNE s massive liquid argon detectors. These electronics will be inside the cryostat; therefore, they will need to have a high yield of working chips and low noise. To improve the automation and reliability of the RTS, this project focused on designing and implementing a Python-based finite state machine (FSM) to manage chip handling workflows. The FSM was developed as a modular software framework to coordinate robotic arm movements, manage chip tray positions, and monitor system states during testing. Key features include robust error handling routines, a pause/resume system for safe mid-cycle interruptions, and a simulation mode for iterative testing without hardware dependencies. The system was designed to prepare for seamless integration with RTS hardware components such as the robotic arm and vision system. This integration will streamline collaboration and enable efficient deployment of updates across the six total institutions performing testing. The outcomes of this internship contribute to Fermilab s mission to advance high-energy physics and support the DOE s national goals by directly improving the testing of equipment to be used in DUNE. The project also provided valuable experience in software design and contributing to the success of DUNE.
The Deep Underground Neutrino Experiment (DUNE) is a flagship international collaboration designed to study neutrinos—tiny, nearly massless particles that may hold answers to fundamental questions about the Universe. Fermilab’s Robotic Test Stand (RTS) plays a critical role in ensuring the quality of approximately 50,000 Application-Specific Integrated Circuit (ASIC) chips that will be used in DUNE’s massive liquid argon detectors. These electronics will be inside the cryostat; therefore, they will need to have a high yield of working chips and low noise. To improve the automation and reliability of the RTS, this project focused on designing and implementing a Python-based finite state machine (FSM) to manage chip handling workflows. The FSM was developed as a modular software framework to coordinate robotic arm movements, manage chip tray positions, and monitor system states during testing. Key features include robust error handling routines, a pause/resume system for safe mid-cycle interruptions, and a simulation mode for iterative testing without hardware dependencies. The system was designed to prepare for seamless integration with RTS hardware components such as the robotic arm and vision system. This integration will streamline collaboration and enable efficient deployment of updates across the six institutions performing testing. The outcomes of this internship contribute to Fermilab’s mission to advance high-energy physics and support the DOE’s national goals by directly improving the testing of equipment to be used in DUNE. The project also provided valuable experience in software design and contributing to the success of DUNE.