Results for “Robotics simulation” — Page 11

DOE OSTI2022

Autonomous Aerial Power Plant Inspection in GPS-denied Environments

Inspection of coal-fired power plants is frequently dangerous, includes difficult places to reach, and can turn expensive due to the downtimes and cost of inspection crew. Robotic systems have shown capabilities to address some of these issues, but most of the current robotic inspection technology in power plants is designed for specific components. Conversely, recent advances in machine vision have empowered aerial platforms for long-range, remotely-controlled, GPS-based inspections of industrial plants. This capability has led to wide spread utilization of aerial robots (commonly termed Drones, UVS or UAS) platforms for inspection in less challenging environments where both collision avoidance, and GPS reception are not significant issues. The challenge in adapting airborne technology for power plant inspection lies in internal structures and the complex network of piping, and distribution systems, which impose significant risks for collision and can hinder the reception and transmission of GPS signals. The current state of the art in aerial inspection technology within the energy sector is controlled via radio control, and utilizes GPS-based navigation, for inspection of large-scale plants such as offshore platforms and wind turbine parks. Nevertheless, close-range and autonomous inspection in the GPS-denied environments of power plants has not yet been achieved, as it requires precise guidance and navigation with real-time situational awareness and obstacle avoidance capabilities. This endeavor introduced the use of rotary wing flying robots, due to their station keeping and vertical take-off capabilities for power plant components inspection. To enable close quarter inspection two methods were used. One method uses the 3D CAD (Three-dimensional Computer-Aided Design) model of the asset to inspect to generate the UAV’s inspection path. To acquire, analyze and process the 3D model, first, the STL file is produced to obtain surface points and vectors normal to the surface. Later, by introducing other variables such as wall offset and a controlled trajectory between each outline and each subsequent layer, the flight path is generated. The proposed framework will generate a path that will pass as close as desired from the surface and navigate in intricate environments. A second method, use advanced manufacturing techniques such as CNC (Computer Numerical Control) and additive manufacturing. Once the inspection flight path is obtained, vision-based navigation systems are employed to have the UAV autonomously tracking the provided trajectory. Finally, Artificial Intelligence-enabled developments are in charge of detecting cracks and corrosion in structural components of power plants. The proposed methods are validated in simulations, laboratory and industrial setups, where it is shown that the developed systems acting together enable close-quarter autonomous aerial inspection and mapping in power plant assets. The system can be further improved by adding more sensors to navigate in different GPS-denied environments, with non-homogeneous lighting conditions, dust and in general situations where vision-based systems may fail.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2024

Multi-Source Machine Learning and Thermoplastics Enhanced Aerostructure Manufacturing (mTEAM)

RTX Technology Research Center (RTRC), together with Collins Aerospace (Collins) and Oak Ridge National Laboratory (ORNL) has developed an Artificial Intelligence (AI) / Machine Learning (ML) guided solution to advance the manufacturing and assembly of high performance and lightweight thermoplastic composite (TPC) aerospace products. The solution aims to lower risk, cost and lead time for induction heating based welding and consolidation processes for TPC structure. The cost and lead time of part and material specific process development for induction welding (IW) and induction consolidation will be reduced by replacing traditional empirical methods with optimization methods that merge AI/ML and physics-based process simulations and process experiments with sensing and controls. TPC-IW process development is empirical in nature, and uncertainties in material & process behavior exist near & far from the induction coil. Physics-based simulations can be leveraged directly for process optimization but can be too computationally expensive to run in high fidelity and real time to do robust process optimization. The key impact of successful TPC induction consolidation and welding is cost & lead time reduction for part & material specific consolidation and welding recipes. This is an enabler for more rapid deployment of TPC structures via joining assembly, which can reduce energy & cost intensive usage of autoclaves & ovens. The solution aimed to advance the U.S. Department of Energy’s interests in using thermoplastics and automation in composite manufacturing for improvement of products for existing markets via increased production speeds, reduced costs, and lowered use of energy. Welded TPC structures can offer significant weight & energy savings for high-value commercial aerospace & industrial applications compared to metal & thermoset composite structures assembled by mechanical fastening and/or adhesive bonding. The project was organized into two Budget Periods. Budget Period 1 (BP1) was 15 months and its goal was to perform ML process optimization framework development & deployment on lab-coupon aerostructure components. A Go/No-Go Review was performed at the end of BP1 to verify fulfilment of key tasks & milestones to justify a Go Decision to move into the next Budget Period. Budget Period 2 (BP2) was 12 months and its goal was the deployment of the ML framework for ML process optimization of pilot industrial scale aerostructure components. The overall project aim was to develop & demonstrate ML-enhanced modeling framework that learns process-property mapping from multiple data sources at different fidelities. During BP1, the team accomplished key tasks & milestones to demonstrate the concept of multi-source ML for TPC aerostructure consolidation and assembly. First, the team completed documentation of induction based TPC heating requirements including baseline metrics to compare measured results against. Next the team completed demonstration of data generation from physics-based simulations for ML surrogate model generation and demonstrated the integration of physics-based simulation data into multi-source AI/ML algorithms. In parallel, the team established the lab-coupon scale induction welding system and completed a process to label and reduce generated data from physics-based simulation and experiments for ML surrogate models to enable multi-source ML model training & testing. To complete BP1, the team integrated physics-based simulation data and experimental data into multi-source ML algorithms. This was based on the team completing ML deployment of the induction welding on a lab system at RTRC and AI/ML deployment on existing induction welding line at Collins. ORNL visited both Collins and RTRC sites to witness the TPC induction welding process. Then, ORNL designed and constructed a new version of their vision-based sensing system better adapted to acquire process signals of the TPC induction welding process for process anomaly and defect detection. In BP2, the team accomplished key tasks & milestones to scale up multi-source ML for TPC aerostructure consolidation and assembly from the lab-coupon scale to the pilot-industrial scale. In BP2, the team demonstrated real time anomaly & defect detection via experiments performed by ORNL & RTRC. The team completed ML-optimization heating trials for TPC induction consolidation at Collins, and the team confirmed pilot industrial scale experimental data from Collins was compatible with the developed ML pipeline from RTRC. The team completed sub-element scale ML process optimization demonstration at RTRC, where the team leveraged RTRC’s robotic TPC welding setup to de-risk the ML process optimization by performing ML analysis of recorded temperatures to account for complex part features. Then, the team applied its ML-derived control strategies and ML process optimization framework at Collins to the pilot-industrial scale on a demo skin-stiffener part representative of a nacelle aerostructure fan cowl section. The key innovation is the AI/ML framework enabling effective process development of high performance, lightweight, energy efficient TPCs for composite aircraft structures.

36 MATERIALS SCIENCE↗

DOE OSTI2016

TH-EF-204-00: AAPM-AMPR (Russia)-SEFM (Spain) Joint Course On Challenges and Advantages of Small Field Radiation Treatment Techniques

Joanna E. Cygler, Jan Seuntjens, J. Daniel Bourland, M. Saiful Huq, Josep Puxeu Vaque, Daniel Zucca Aparicio, Tatiana Krylova, Yuri Kirpichev, Eric Ford, Caridad Borras Stereotactic Radiation Therapy (SRT) utilizes small static and dynamic (IMRT) fields, to successfully treat malignant and benign diseases using techniques such as Stereotactic Radiosurgery (SRS) and Stereotactic Body Radiation Therapy (SBRT). SRT is characterized by sharp dose gradients for individual fields and their resultant dose distributions. For appropriate targets, small field radiotherapy offers improved treatment quality by allowing better sparing of organs at risk while delivering the prescribed target dose. Specialized small field treatment delivery systems, such as robotic-controlled linear accelerators, gamma radiosurgery units, and dynamic arc linear accelerators may utilize rigid fixation, image guidance, and tumor tracking, to insure precise dose delivery to static or moving targets. However, in addition to great advantages, small field delivery techniques present special technical challenges for dose calibration due to unique geometries and small field sizes not covered by existing reference dosimetry protocols such as AAPM TG-51 or IAEA TRS 398. In recent years extensive research has been performed to understand small field dosimetry and measurement instrumentation. AAPM, IAEA and ICRU task groups are expected to provide soon recommendations on the dosimetry of small radiation fields. In this symposium we will: 1] discuss the physics, instrumentation, methodologies and challenges for small field radiation dose measurements; 2] review IAEA and ICRU recommendations on prescribing, recording and reporting of small field radiation therapy; 3] discuss selected clinical applications and technical aspects for specialized image-guided, small field, linear accelerator based treatment techniques such as IMRT and SBRT. Learning Objectives: To learn the physics of small fields in contrast to dosimetry of conventional fields To learn about detectors suitable for small fields To learn about the role of Monte Carlo simulations in determination of small field output factors To provide an overview of the IAEA small field dosimetry recommendations To provide an overview of the content of the ICRU report on Prescribing, Reporting and Recording of Small Field Radiation Therapy. To learn about special technical considerations in delivering IMRT and SBRT treatments To appreciate specific challenges of IMRT implementation J. Seuntjens, Natural Sciences and Engineering Research Council; Canadian Institutes of Health Research.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-EF-204-01: Introduction

Joanna E. Cygler, Jan Seuntjens, J. Daniel Bourland, M. Saiful Huq, Josep Puxeu Vaque, Daniel Zucca Aparicio, Tatiana Krylova, Yuri Kirpichev, Eric Ford, Caridad Borras Stereotactic Radiation Therapy (SRT) utilizes small static and dynamic (IMRT) fields, to successfully treat malignant and benign diseases using techniques such as Stereotactic Radiosurgery (SRS) and Stereotactic Body Radiation Therapy (SBRT). SRT is characterized by sharp dose gradients for individual fields and their resultant dose distributions. For appropriate targets, small field radiotherapy offers improved treatment quality by allowing better sparing of organs at risk while delivering the prescribed target dose. Specialized small field treatment delivery systems, such as robotic-controlled linear accelerators, gamma radiosurgery units, and dynamic arc linear accelerators may utilize rigid fixation, image guidance, and tumor tracking, to insure precise dose delivery to static or moving targets. However, in addition to great advantages, small field delivery techniques present special technical challenges for dose calibration due to unique geometries and small field sizes not covered by existing reference dosimetry protocols such as AAPM TG-51 or IAEA TRS 398. In recent years extensive research has been performed to understand small field dosimetry and measurement instrumentation. AAPM, IAEA and ICRU task groups are expected to provide soon recommendations on the dosimetry of small radiation fields. In this symposium we will: 1] discuss the physics, instrumentation, methodologies and challenges for small field radiation dose measurements; 2] review IAEA and ICRU recommendations on prescribing, recording and reporting of small field radiation therapy; 3] discuss selected clinical applications and technical aspects for specialized image-guided, small field, linear accelerator based treatment techniques such as IMRT and SBRT. Learning Objectives: To learn the physics of small fields in contrast to dosimetry of conventional fields To learn about detectors suitable for small fields To learn about the role of Monte Carlo simulations in determination of small field output factors To provide an overview of the IAEA small field dosimetry recommendations To provide an overview of the content of the ICRU report on Prescribing, Reporting and Recording of Small Field Radiation Therapy. To learn about special technical considerations in delivering IMRT and SBRT treatments To appreciate specific challenges of IMRT implementation J. Seuntjens, Natural Sciences and Engineering Research Council; Canadian Institutes of Health Research.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-EF-204-02: Small Field Radiation Therapy: Physics and Recent Recommendations From IAEA and ICRU

Joanna E. Cygler, Jan Seuntjens, J. Daniel Bourland, M. Saiful Huq, Josep Puxeu Vaque, Daniel Zucca Aparicio, Tatiana Krylova, Yuri Kirpichev, Eric Ford, Caridad Borras Stereotactic Radiation Therapy (SRT) utilizes small static and dynamic (IMRT) fields, to successfully treat malignant and benign diseases using techniques such as Stereotactic Radiosurgery (SRS) and Stereotactic Body Radiation Therapy (SBRT). SRT is characterized by sharp dose gradients for individual fields and their resultant dose distributions. For appropriate targets, small field radiotherapy offers improved treatment quality by allowing better sparing of organs at risk while delivering the prescribed target dose. Specialized small field treatment delivery systems, such as robotic-controlled linear accelerators, gamma radiosurgery units, and dynamic arc linear accelerators may utilize rigid fixation, image guidance, and tumor tracking, to insure precise dose delivery to static or moving targets. However, in addition to great advantages, small field delivery techniques present special technical challenges for dose calibration due to unique geometries and small field sizes not covered by existing reference dosimetry protocols such as AAPM TG-51 or IAEA TRS 398. In recent years extensive research has been performed to understand small field dosimetry and measurement instrumentation. AAPM, IAEA and ICRU task groups are expected to provide soon recommendations on the dosimetry of small radiation fields. In this symposium we will: 1] discuss the physics, instrumentation, methodologies and challenges for small field radiation dose measurements; 2] review IAEA and ICRU recommendations on prescribing, recording and reporting of small field radiation therapy; 3] discuss selected clinical applications and technical aspects for specialized image-guided, small field, linear accelerator based treatment techniques such as IMRT and SBRT. Learning Objectives: To learn the physics of small fields in contrast to dosimetry of conventional fields To learn about detectors suitable for small fields To learn about the role of Monte Carlo simulations in determination of small field output factors To provide an overview of the IAEA small field dosimetry recommendations To provide an overview of the content of the ICRU report on Prescribing, Reporting and Recording of Small Field Radiation Therapy. To learn about special technical considerations in delivering IMRT and SBRT treatments To appreciate specific challenges of IMRT implementation J. Seuntjens, Natural Sciences and Engineering Research Council; Canadian Institutes of Health Research.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-EF-204-04: Experience of IMRT and Other Conformal Techniques in Russia

Joanna E. Cygler, Jan Seuntjens, J. Daniel Bourland, M. Saiful Huq, Josep Puxeu Vaque, Daniel Zucca Aparicio, Tatiana Krylova, Yuri Kirpichev, Eric Ford, Caridad Borras Stereotactic Radiation Therapy (SRT) utilizes small static and dynamic (IMRT) fields, to successfully treat malignant and benign diseases using techniques such as Stereotactic Radiosurgery (SRS) and Stereotactic Body Radiation Therapy (SBRT). SRT is characterized by sharp dose gradients for individual fields and their resultant dose distributions. For appropriate targets, small field radiotherapy offers improved treatment quality by allowing better sparing of organs at risk while delivering the prescribed target dose. Specialized small field treatment delivery systems, such as robotic-controlled linear accelerators, gamma radiosurgery units, and dynamic arc linear accelerators may utilize rigid fixation, image guidance, and tumor tracking, to insure precise dose delivery to static or moving targets. However, in addition to great advantages, small field delivery techniques present special technical challenges for dose calibration due to unique geometries and small field sizes not covered by existing reference dosimetry protocols such as AAPM TG-51 or IAEA TRS 398. In recent years extensive research has been performed to understand small field dosimetry and measurement instrumentation. AAPM, IAEA and ICRU task groups are expected to provide soon recommendations on the dosimetry of small radiation fields. In this symposium we will: 1] discuss the physics, instrumentation, methodologies and challenges for small field radiation dose measurements; 2] review IAEA and ICRU recommendations on prescribing, recording and reporting of small field radiation therapy; 3] discuss selected clinical applications and technical aspects for specialized image-guided, small field, linear accelerator based treatment techniques such as IMRT and SBRT. Learning Objectives: To learn the physics of small fields in contrast to dosimetry of conventional fields To learn about detectors suitable for small fields To learn about the role of Monte Carlo simulations in determination of small field output factors To provide an overview of the IAEA small field dosimetry recommendations To provide an overview of the content of the ICRU report on Prescribing, Reporting and Recording of Small Field Radiation Therapy. To learn about special technical considerations in delivering IMRT and SBRT treatments To appreciate specific challenges of IMRT implementation J. Seuntjens, Natural Sciences and Engineering Research Council; Canadian Institutes of Health Research.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-EF-204-05: Application of Small-Field Treatment: The Promises and Pitfalls of SBRT

Joanna E. Cygler, Jan Seuntjens, J. Daniel Bourland, M. Saiful Huq, Josep Puxeu Vaque, Daniel Zucca Aparicio, Tatiana Krylova, Yuri Kirpichev, Eric Ford, Caridad Borras Stereotactic Radiation Therapy (SRT) utilizes small static and dynamic (IMRT) fields, to successfully treat malignant and benign diseases using techniques such as Stereotactic Radiosurgery (SRS) and Stereotactic Body Radiation Therapy (SBRT). SRT is characterized by sharp dose gradients for individual fields and their resultant dose distributions. For appropriate targets, small field radiotherapy offers improved treatment quality by allowing better sparing of organs at risk while delivering the prescribed target dose. Specialized small field treatment delivery systems, such as robotic-controlled linear accelerators, gamma radiosurgery units, and dynamic arc linear accelerators may utilize rigid fixation, image guidance, and tumor tracking, to insure precise dose delivery to static or moving targets. However, in addition to great advantages, small field delivery techniques present special technical challenges for dose calibration due to unique geometries and small field sizes not covered by existing reference dosimetry protocols such as AAPM TG-51 or IAEA TRS 398. In recent years extensive research has been performed to understand small field dosimetry and measurement instrumentation. AAPM, IAEA and ICRU task groups are expected to provide soon recommendations on the dosimetry of small radiation fields. In this symposium we will: 1] discuss the physics, instrumentation, methodologies and challenges for small field radiation dose measurements; 2] review IAEA and ICRU recommendations on prescribing, recording and reporting of small field radiation therapy; 3] discuss selected clinical applications and technical aspects for specialized image-guided, small field, linear accelerator based treatment techniques such as IMRT and SBRT. Learning Objectives: To learn the physics of small fields in contrast to dosimetry of conventional fields To learn about detectors suitable for small fields To learn about the role of Monte Carlo simulations in determination of small field output factors To provide an overview of the IAEA small field dosimetry recommendations To provide an overview of the content of the ICRU report on Prescribing, Reporting and Recording of Small Field Radiation Therapy. To learn about special technical considerations in delivering IMRT and SBRT treatments To appreciate specific challenges of IMRT implementation J. Seuntjens, Natural Sciences and Engineering Research Council; Canadian Institutes of Health Research.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-EF-204-06: Closing

Joanna E. Cygler, Jan Seuntjens, J. Daniel Bourland, M. Saiful Huq, Josep Puxeu Vaque, Daniel Zucca Aparicio, Tatiana Krylova, Yuri Kirpichev, Eric Ford, Caridad Borras Stereotactic Radiation Therapy (SRT) utilizes small static and dynamic (IMRT) fields, to successfully treat malignant and benign diseases using techniques such as Stereotactic Radiosurgery (SRS) and Stereotactic Body Radiation Therapy (SBRT). SRT is characterized by sharp dose gradients for individual fields and their resultant dose distributions. For appropriate targets, small field radiotherapy offers improved treatment quality by allowing better sparing of organs at risk while delivering the prescribed target dose. Specialized small field treatment delivery systems, such as robotic-controlled linear accelerators, gamma radiosurgery units, and dynamic arc linear accelerators may utilize rigid fixation, image guidance, and tumor tracking, to insure precise dose delivery to static or moving targets. However, in addition to great advantages, small field delivery techniques present special technical challenges for dose calibration due to unique geometries and small field sizes not covered by existing reference dosimetry protocols such as AAPM TG-51 or IAEA TRS 398. In recent years extensive research has been performed to understand small field dosimetry and measurement instrumentation. AAPM, IAEA and ICRU task groups are expected to provide soon recommendations on the dosimetry of small radiation fields. In this symposium we will: 1] discuss the physics, instrumentation, methodologies and challenges for small field radiation dose measurements; 2] review IAEA and ICRU recommendations on prescribing, recording and reporting of small field radiation therapy; 3] discuss selected clinical applications and technical aspects for specialized image-guided, small field, linear accelerator based treatment techniques such as IMRT and SBRT. Learning Objectives: To learn the physics of small fields in contrast to dosimetry of conventional fields To learn about detectors suitable for small fields To learn about the role of Monte Carlo simulations in determination of small field output factors To provide an overview of the IAEA small field dosimetry recommendations To provide an overview of the content of the ICRU report on Prescribing, Reporting and Recording of Small Field Radiation Therapy. To learn about special technical considerations in delivering IMRT and SBRT treatments To appreciate specific challenges of IMRT implementation J. Seuntjens, Natural Sciences and Engineering Research Council; Canadian Institutes of Health Research.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Formation of helices with controllable chirality in gel-fiber composites

While microscale helices with specified chirality are pervasive in biological systems, driving purely synthetic materials to self-organize into such useful structures remains a significant challenge. Through theory and simulations, we model the behavior of initially flat ribbons of gel-fiber composites and show that these materials self-organize into three-dimensional helices with the application of heat. We specifically focus on thermo-responsive gels, which shrink when heated to a point near the lower critical solubility temperature (LCST). Within the composite, regions of the LCST gel that lie far from the fibers can readily shrink in response to the increased temperature; however, near the fibers the network is inhibited from undergoing the heat-induced collapse. This competition between the constrained and the unconstrained regions of the heated sample plays a vital role in the structural evolution of the material. Additionally, we find that this dynamic process can be controlled through the careful placement of the fibers on the outer surface of the gel. We specifically identify fiber arrangements that lead to the formation of helical structures with controlled chirality. Moreover, we isolate scenarios where constraining the movement of one or both the free ends of the sample promotes and stabilizes the formation of helical structures with specified handedness. Such structurally tailored composites are useful for creating mechanically robust actuators, and “appendages” for soft robots that can controllably curl and uncurl with variations in temperature.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Design and Development of a Robotic Exoskeleton Device for use in Glove Boxes - 20374

Glovebox operators in nuclear facilities often operate under radioactive environment and typically perform monotonous, dull and repetitive work. This can result in muscle fatigue leading to increased levels of accidents. Current research work aims in assisting the glovebox operators by designing a novel upper limb exoskeleton device to reduce injuries and increase worker efficiency. Almost none of the commercially available hand and upper body exoskeletons are directly suitable for use in gloveboxes. This explains the novelty of the present work in developing a wearable powered robotic exoskeleton device. The device consists of electronics, motors and spring systems mounted on adjustable plates and is supported by a stationary frame behind the worker. The device is intended to be portable and adjustable according to the operator height. The elbow has a flexion movement assisted with motor control by way of a Bowden cable system. The system is floated on a spring and pulley combination that allows for extra safety and smoothness of operation. The end points are fixed near the pivot points to help minimize unwanted movement/torque. The extension movement of the upper and lower arm are assisted by gravity and a counter spring. The shoulder and upper arm movement are held in a neutral position with a spring system for counter balance allowing small movements within the glovebox. This initial concept considers only the vertical movement of the arm. The abduction and adduction movement will not be assisted or impeded by the over shoulder plate or at the forearm plate allowing for full range of motion. Several glovebox aspects such as the level of task assistance, ergonomic design, minimum hindrance in dexterity/operator movements and tear resistance for hazardous safety reasons will be considered in the design of the exoskeleton. Computer models will be simulated to test the load levels and stress conditions on the exoskeleton to enhance the designs. The exoskeleton focusses on major muscular involvement reduction through weight offset and active assistance with minimal parts and hence the device is derived with several joint links and contains both passive and active joints. The paper details the various stages in the design development and prototype testing of the exoskeleton device. Upon successful prototype device development, human testing will be conducted in a replica of the SRS-235F hot cell facility constructed at FIU-ARC. The final stage of the project will be potential testing/deployment of the exoskeleton device in one of the glovebox facilities in Savannah River Nuclear Waste site. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Colloidal State Machines as Smart Tracers for Chemical Reactor Analysis

A widely utilized tool in reactor analysis is passive tracers that report the residence time distribution, allowing estimation of the conversion and other properties of the system. Recently, advances in microrobotics have introduced powered and functional entities with sizes comparable to some traditional tracers. This has motivated the concept of Smart Tracers that could record the local chemical concentrations, temperature, or other conditions as they progress through reactors. Herein, the design constraints and advantages of Smart Tracers by simulating their operation in a laminar flow reactor model conducting chemical reactions of various orders are analyzed. It is noted that far fewer particles are necessary to completely map even the most complex concentration gradients compared with their conventional counterparts. Design criteria explored herein include sampling frequency, memory storage capacity, and ensemble number necessary to achieve the required accuracy to inform a reactor model. Cases of severe particle diffusion and sensor noise appear to bind the functional upper limit of such probes and require consideration for future design. The results of the study provide a starting framework for applying the new technology of microrobotics to the broad and impactful set of problems classified as chemical reactor analysis.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Motion Planning Algorithms for Safety and Quantum Computing Efficiency

Motion planning remains a fundamental problem in robotics. Sampling-based algorithms use randomization to allow efficient solutions to this complex problem. As mobile robots and autonomous vehicles become more prevalent in everyday life, motion planning must be applied to increasingly challenging scenarios. Safety has become a paramount concern in motion planning for ensuring robotic applications enrich human lives. To date, many motion planning techniques to increase safety in the face of uncertain and dynamic environments have been developed. This dissertation first addresses distributional safety of Rapidly-Exploring Random Trees (RRT) through our algorithm W-Safe RRT. To acknowledge distributional uncertainty and poor modeling, W-Safe RRT uses the Wasserstein metric to provide a probabilistic bound on the distributional distance between a robot and obstacles. Human-interpretable environmental agent classification allows online safety margin adaptation. We propose and analyze an integrating region method for online classification that increases actor labeling accuracy based on behavioral feature values when compared to state of the art methods. The method performs class assignments based on local maximum likelihood in a created behavioral feature-space, allowing a notion of classification uncertainty. Model-based methods with safety guarantees can quickly become computationally in tractable, especially with multiple agents, higher dimensions, and plentiful unknowns. Sampling based algorithms have been parallelized for computation with multi-core computers and GPUs. We consider the use of quantum algorithms and computers for sampling-based motion planning for the first time. Quantum computing performs operations on superpositions of states and can solve certain problems much more efficiently than classical computers, but introduces previously unseen challenges. With Quantum-RRT, we recast the motion planning problem into a database-search structure and use Quantum Amplitude Amplification to find reachable states in the database with a quadratic performance increase over classical methods. We address two error sources with this method: quantum measurement and quantum oracle errors. We then extend this method to Parallel Quantum-RRT, which uses a manager-worker architecture with multiple parallel quantum workers to increase database search efficiency. We compare algorithm architectures and characterize probabilities of multiple workers finding solutions. Lastly, we test in simulation the quantum algorithms against classical versions in a wide variety of scenarios, concluding that a similar parallelization improvement is to be found in the quantum case as was found in the parallelization of classical RRT.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

A multiscale anisotropic polymer network model coupled with phase field fracture

The study of polymers has continued to gain substantial attention due to their expanding range of applications, spanning essential engineering fields to emerging domains like stretchable electronics, soft robotics, and implantable sensors. These materials exhibit remarkable properties, primarily stemming from their intricate polymer chain network, which, in turn, increases the complexity of precisely modeling their behavior. Especially for modeling elastomers and their fracture behavior, accurately accounting for the deformations of the polymer chains is vital for predicting the rupture in highly stretched chains. Despite the importance, many robust multiscale continuum frameworks for modeling elastomer fracture tend to simplify network deformations by assuming uniform behavior among chains in all directions. Recognizing this limitation, our study proposes a multiscale fracture model that accounts for the anisotropic nature of elastomer network responses. At the microscale, damage in the chains is assumed to be driven by both the chain's entropy and the internal energy due to molecular bond distortions. In order to bridge the stretching in the chains to the macroscale deformation, we employ the maximal advance path constraint network model, inherently accommodating anisotropic network responses. As a result, chains oriented differently can be predicted to exhibit varying stretch and, consequently, different damage levels. To drive macroscale fracture based on damages in these chains, we utilize the micromorphic regularization theory, which involves the introduction of dual local-global damage variables at the macroscale. The macroscale local damage variable is obtained through the homogenization of the chain damage values, resulting in the prediction of an isotropic material response. The macroscale global damage variable is subjected to nonlocal effects and boundary conditions in a thermodynamically consistent phase field continuum formulation. Moreover, the total dissipation in the system is considered to be mainly due to the breaking of the molecular bonds at the microscale. To validate our model, we employ the double-edge notched tensile test as a benchmark, comparing simulation predictions with existing experimental data. Additionally, to enhance our understanding of the fracturing process, we conduct uniaxial tensile experiments on a square film made up of polydimethylsiloxane (PDMS) rubber embedded with a hole and notches and then compare our simulation predictions with the experimental observations. Furthermore, we visualize the evolution of stretch and damage values in chains oriented along different directions to assess the predictive capacity of the model. In conclusion, the results are also compared with another existing model to evaluate the utility of our model in accurately simulating the fracture behavior of rubber-like materials.

42 ENGINEERING↗

DOE OSTI2017

Sampling-Based Motion Planning Algorithms for Replanning and Spatial Load Balancing

The common theme of this dissertation is sampling-based motion planning with the two key contributions being in the area of replanning and spatial load balancing for robotic systems. Here, we begin by recalling two sampling-based motion planners: the asymptotically optimal rapidly-exploring random tree (RRT*), and the asymptotically optimal probabilistic roadmap (PRM*). We also provide a brief background on collision cones and the Distributed Reactive Collision Avoidance (DRCA) algorithm. The next four chapters detail novel contributions for motion replanning in environments with unexpected static obstacles, for multi-agent collision avoidance, and spatial load balancing. First, we show improved performance of the RRT* when using the proposed Grandparent-Connection (GP) or Focused-Refinement (FR) algorithms. Next, the Goal Tree algorithm for replanning with unexpected static obstacles is detailed and proven to be asymptotically optimal. A multi-agent collision avoidance problem in obstacle environments is approached via the RRT*, leading to the novel Sampling-Based Collision Avoidance (SBCA) algorithm. The SBCA algorithm is proven to guarantee collision free trajectories for all of the agents, even when subject to uncertainties in the knowledge of the other agents’ positions and velocities. Given that a solution exists, we prove that livelocks and deadlock will lead to the cost to the goal being decreased. We introduce a new deconfliction maneuver that decreases the cost-to-come at each step. This new maneuver removes the possibility of livelocks and allows a result to be formed that proves convergence to the goal configurations. Finally, we present a limited range Graph-based Spatial Load Balancing (GSLB) algorithm which fairly divides a non-convex space among multiple agents that are subject to differential constraints and have a limited travel distance. The GSLB is proven to converge to a solution when maximizing the area covered by the agents. The analysis for each of the above mentioned algorithms is confirmed in simulations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Big Data Framework with Machine Learning for D and D Applications - 19108

The nuclear industry is experiencing a steady increase in maintenance costs even though plants are maintained under high levels of safety, capability and reliability. Nuclear power plants are expected to run every unit at maximum capacity at all times, efficiently utilizing assets with minimal downtime. Surveillance and maintenance of nuclear decommissioning infrastructure provides lot challenges with respect to maintenance or decommissioning of the buildings. There is a need for a framework to analyze the huge amount of data generated by the sensors on the nuclear reactor components as well as structures, to monitor the conditions of these building over a period of time. Emerging technologies such as big data analytics have become a requirement in the nuclear industry to improve structural health monitoring and diagnostics. FIU will make use of existing mature technologies in the areas of imaging, robotics, big data, and machine learning/deep learning to assess the structural integrity of aging facilities at DOE sites. As these facilities await decommissioning, there is a need to understand the structural health of these structures. Many of these facilities were built over 50 years ago and in some cases these facilities have gone beyond the operational life expectancy. In other cases, the facilities have been placed in a state of 'cold and dark' and they are sitting unused, awaiting decommissioning. Finally, some older facilities are one-of-a-kind operational/production facilities supporting critical DOE missions and cannot be shut down. In any of these scenarios, the structural integrity of these facilities may be compromised, so it is imperative that adequate inspections and data collection/analysis be performed on a continuous and ongoing basis. The primary goals of the research include collecting various formats of data such as structured, unstructured and streaming data from the various sensors deployed in buildings, collect videos/pictures from various imaging sources, ingest them using a Hadoop distributed file system and process the data using Spark to perform batch processing and real time analytics. Research and development on various machine learning/deep learning algorithms will also be performed to analyze the heterogeneous data collected from nuclear decommission infrastructure. FIU will design the big data framework to ingest, store and process huge amounts of heterogeneous data collected from many sources and optimize the algorithms to provide insights into the data and predict anomalies observed when compared against baseline conditions. Various modules of this framework will include heterogeneous data sources, message broker, Hadoop distributed file system, Spark for stream and batch processing, machine learning/ deep learning, Cassandra - persistent data store and visualization. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

R and DE Robotic Sensor Intern Team: Remote 3D Mapping Sensor Suite

The primary goal of the sensor suite is to provide an internal 3D map of the HCAEX tunnel. When looking for a sensor that would provide XYZ point cloud data for mapping the decision was between a stereo camera, a 3D Lidar, or a 2D Lidar that would need to be spun around. The issues with the stereo camera were that it does not have a large field-of-view and that its depth information was not nearly as accurate as even the baseline Lidars. The selection between Lidars is detailed. The Hokuyo UTM-30LX-EW shown provides a 270 deg. FOV with high accuracy and resolution while the largest FOV on a 3D Lidar is 40 deg.. This was the reason a 2D Lidar was chosen over the 3D Lidar. Even though the 2D Lidar requires additional components and software to generate a 3D map, its FOV advantage is too great given that the HCAEX tunnel is a relatively confined space. The rotating planar scan from the 2D Lidar can be stitched into a 3D scan as shown in the graphic below. Along with XYZ data, the Lidar provides return intensity data in color which can be used to distinguish between different materials within the tunnel. To provide visual feedback a camera system needed to be identified. The common solution for this is a high resolution camera with a small FOV mounted on a pan-tilt system. But, given the critical nature of the visual feedback we looked into solutions without the need for moving parts. Two panospheric cameras give a FOV large enough to get a full 360 deg. view.. Camera color data (RGB) can also be combined with the 3D mapping data from the Lidar and overlaid on the map to give the user the ability to see inside the entire length of the tunnel as if they were inside. Situational awareness is required for the crawler in the tunnel in order for it to be able to register the 3D scans to create a final map. For this we procured a Microstrain 3DM-GX5-25 Inertial Measurement Unit (IMU). To perform the Lidar rotation we have two servos driving the pan and tilt. The servo controls and most data processing will be performed via an onboard computer. The sensor components are to be packaged together using 3D printed mounts into a standalone unit.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

R and DE Robotic Sensor Intern Team: Sensor Suite Software and User Interface

The sensor tree developed for the H-Canyon tunnel inspection project produces a large quantity of camera and Lidar data. A postprocessing pipeline was created to facilitate the analysis of these data. The pipeline follows the following routine: 1) Registration - Salient local features in subsequent scans are identified and used to align the scans with one another. This allows production of a single cohesive map of the tunnel. 2) Sensor Fusion - RGB camera data is 'painted' onto the depth data created by the Lidar. 3) Segmentation - Individual surfaces from within the scene are segmented out separately for individual analysis. 4) Degradation Analysis - Automatic mapping of local deviation from the plane in position or surface normal is performed, as is detection of exposed rebar using Lidar return intensity. 5) Obstacle Detection - Lidar data is also used in real time to detect obstacles in the environment. This could give feedback to the operator to successfully drive in the tunnel. 6) Graphic User Interface - a custom GUI was created to facilitate customer use of the system. The system provides real-time feedback from both the cameras and Lidar. During alignment, local features are used to recognize points within the walls that are especially unique and noticeable (salient) based on their deviation from the expected surface of the wall. These are used as landmarks to register the clouds, which also informs precise localization of the base at each scan point. After registration, clouds are 'painted' with RGB data from the spherical cameras, as shown below. The painted clouds provide a 3D, colorized model of the environment which improves the realism of the experience for the user. Painting also labels high-resolution color imagery from the 3D cameras with depth values. Following sensor fusion, individual walls and cylindrical duct surfaces are segmented from the scene for individual analysis using a Random Sample Consensus algorithm. This algorithm allows segmentation of planar walls and cylindrical ducts. Once individual walls are identified, each point within the wall can be compared to its neighbors and the wall overall in order to detect local areas with damaged concrete. Automatic rebar detection is also performed using the contrast in Lidar return intensity between iron and concrete. The Lidar point-cloud data is also used for obstacle detection. As the Lidar spins, various algorithms are run to analyze the current 'slice' and obstacles in the environment are determined and registered on a cost-map. A Graphical User Interface (GUI) was developed to allow the user to easily command the crawler to set the rotational speed of the Lidar, perform a scan, and register and save the 3D generated cloud after a scan is done. For the good of the crawler, a critical aspect of the GUI design is to allow the user to cancel a scan any time after it is commanded and before it is saved.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗