CES-515 Towards Localization and Mapping of Autonomous Underwater Vehicles: A Survey

Autonomous Underwater Vehicles (AUVs) have been used for a huge number of tasks ranging from commercial, military and research areas etc, while the fundamental function of a successful AUV is its localization and mapping ability. This report aims to review the relevant elements of localization and mapping for AUVs. First, a brief introduction of the concept and the historical development of AUVs is given; then a relatively detailed description of the sensor system used for AUV navigation is provided. As the main part of the report, a comprehensive investigation of the simultaneous localization and mapping (SLAM) for AUVs are conducted, including its application examples. Finally a brief conclusion is summarized

Augmented reality (AR) for surgical robotic and autonomous systems: State of the art, challenges, and solutions

Despite the substantial progress achieved in the development and integration of augmented reality (AR) in surgical robotic and autonomous systems (RAS), the center of focus in most devices remains on improving end-effector dexterity and precision, as well as improved access to minimally invasive surgeries. This paper aims to provide a systematic review of different types of state-of-the-art surgical robotic platforms while identifying areas for technological improvement. We associate specific control features, such as haptic feedback, sensory stimuli, and human-robot collaboration, with AR technology to perform complex surgical interventions for increased user perception of the augmented world. Current researchers in the field have, for long, faced innumerable issues with low accuracy in tool placement around complex trajectories, pose estimation, and difficulty in depth perception during two-dimensional medical imaging. A number of robots described in this review, such as Novarad and SpineAssist, are analyzed in terms of their hardware features, computer vision systems (such as deep learning algorithms), and the clinical relevance of the literature. We attempt to outline the shortcomings in current optimization algorithms for surgical robots (such as YOLO and LTSM) whilst providing mitigating solutions to internal tool-to-organ collision detection and image reconstruction. The accuracy of results in robot end-effector collisions and reduced occlusion remain promising within the scope of our research, validating the propositions made for the surgical clearance of ever-expanding AR technology in the future

Evaluation of Manually Completed Manufacturing Assembly Processes Through a Wearable Force and Motion Sensing System Integrated Into a Glove

The objective of this research is to model the relationship between force, sound, and motion signals in manual assembly environments through a wearable sensor glove and the resultant quality of vehicle connections made on the assembly line. Many tasks in production assembly are still completed manually due to the intuition needed by the associate, complex automation steps, or time constraints. This is largely observed in automotive assembly environments. With the amount of variability in manually completed processes, the possibility for error increases. These processes include hose and electrical connections which can loosen over time after passing initial quality testing, resulting in costly, time-consuming rework and a diminished brand image. It is the intent of this work to utilize multidimensional operator force signatures and movements exhibited to understand the primary forces acting in the direction of the connector locking and additional measured forces acting in other directions. The sensor signals feed into the classification algorithm for rapid postprocessing to enable real-time feedback indicating a completed connection or a connection that needs further investigation. These classifications can later act as a steppingstone for automating manually completed manufacturing processes by implementing the findings into autonomous systems to yield an automatic verification of the process. This research captured data physically exerted by the operator as a means of accountable process quality evaluation where there are limited marketable products and research. The work also introduced a sensor glove system capable of capturing operator applied shear force in a robust and durable way fit for a manufacturing environment. Marketed products and research shear force sensing are extremely limited in breadth, and force sensing gloves are unsuitable for an assembly environment due to cost, measurement capabilities, durability, and/or operator encroachment. The sensing system developed in this research is coupled with a classification algorithm capable of discerning incomplete or rework connections from successful ones demonstrated on an OEM assembly line. The developed sensor glove capable of capturing shear and normal force, acceleration, and gyroscopic information was successfully tested on an OEM assembly line for 250+ vehicles of work. This includes the completion of hard plastic connections, tool usage, and tasks completed outside of the takt. Five classification models using the gathered data yielded accuracies of 91% or above using a 60/40 train/test split. The best performing model, Na¨ıve Bayes, achieved a balanced accuracy of 97.6%

Study of artificial intelligence and computer vision methods for tracking transmission lines with the AID of UAVs

Currently, Unmanned Aerial Vehicles (UAVs) have been used in the most diverse applications in both the civil and military sectors. In the civil sector, aerial inspection services have been gaining a lot of attention, especially in the case of inspections of high voltage electrical systems transmission lines. This type of inspection involves a helicopter carrying three or more people (technicians, pilot, etc.) flying over the transmission line along its entire length which is a dangerous service especially due to the proximity of the transmission line and possible environmental conditions (wind gusts, for example). In this context, the use of UAVs has shown considerable interest due to their low cost and safety for transmission line inspection technicians. This work presents research results related to the application of UAVs for transmission lines inspection, autonomously, allowing the identification of invasions of the transmission line area as well as possible defects in components (cables, insulators, connection, etc.) through the use of Convolutional Neural Networks (CNN) for fault detection and identification. This thesis proposes the development of an autonomous system to track power transmission lines using UAVs efficiently and with low implementation and operation costs, based exclusively on rea-time image processing that identifies the structure of the towers and transmission lines durin the flight and controls the aircraft´s movements, guiding it along the closest possible path. A sumary of the work developed will be presented in the next sections.Atualmente, os Veículos Aéreos Não Tripulados – VANTs têm sido utilizados nas mais diversas aplicações tanto no setor civil quanto militar. No setor civil, os serviços de inspeção aérea vêm ganhando bastante atenção, principalmente no caso de inspeções de linhas de transmissão de sistemas elétricos de alta tensão. Este tipo de inspeção envolve um helicóptero transportando três ou mais pessoas (técnicos, pilotos, etc.) sobrevoando a linha de transmissão em toda a sua extensão, o que constitui um serviço perigoso principalmente pela proximidade da linha de transmissão e possíveis condições ambientais (rajadas de vento, por exemplo). Neste contexto, a utilização de VANTs tem demonstrado considerável interesse devido ao seu baixo custo e segurança para técnicos de inspeção de linhas de transmissão. Este trabalho apresenta resultados de pesquisas relacionadas à aplicação de VANTs para inspeção de linhas de transmissão, de forma autônoma, permitindo a identificação de invasões da área da linha de transmissão bem como possíveis defeitos em componentes (cabos, isoladores, conexões, etc.) através do uso de Convolucional. Redes Neurais - CNN para detecção e identificação de falhas. Esta tese propõe o desenvolvimento de um sistema autônomo para rastreamento de linhas de transmissão de energia utilizando VANTs de forma eficiente e com baixos custos de implantação e operação, baseado exclusivamente no processamento de imagens em tempo real que identifica a estrutura das torres e linhas de transmissão durante o voo e controla a velocidade da aeronave. movimentos, guiando-o pelo caminho mais próximo possível. Um resumo do trabalho desenvolvido será apresentado nas próximas seções

Identifying Unsafe Behavior of Construction Workers: A Dynamic Approach Combining Skeleton Information and Spatiotemporal Features

Data Availability Statement: Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.Vision-based methods for action recognition are valuable for supervising construction workers’ unsafe behaviors. However, current monitoring methods have limitations in extracting dynamic information about workers. Identifying hazardous actions based on the spatiotemporal relationships between workers’ skeletal points remains a significant challenge in construction sites. This paper proposed an automated method for recognizing dynamic hazardous actions. The method used the OpenPose network to extract workers’ skeleton information from the video and applied a spatiotemporal graph convolutional network (ST-GCN) to analyze the dynamic spatiotemporal relationships between workers’ body skeletons, enabling automatic recognition of hazardous actions. A novel human partitioning strategy and nonlocal attention mechanism were designed to assign appropriate weight parameters to different joints involved in actions, thereby improving the recognition accuracy of complex construction actions. The enhanced model is called the attention module spatiotemporal graph convolutional network (AM-STGCN). The method achieved a test accuracy of 90.50% and 87.08% in typical work scenarios, namely high-altitude scaffolding scenes with close-up and far views, surpassing the performance of the original ST-GCN model. The high-accuracy test results demonstrate that the model can accurately identify workers’ hazardous actions. The newly proposed model is inferred to have promising application prospects and the potential to be applied in broader construction scenarios for on-site monitoring of hazardous actions.National Natural Science Foundation of China (Grant No. 72071097); MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Grant No.20YJAZH034); Foundation of Jiangsu University (Grant No. SZCY-014)

NASA Thesaurus. Volume 1: Hierarchical listing

There are 16,713 postable terms and 3,716 nonpostable terms approved for use in the NASA scientific and technical information system in the Hierarchical Listing of the NASA Thesaurus. The generic structure is presented for many terms. The broader term and narrower term relationships are shown in an indented fashion that illustrates the generic structure better than the more widely used BT and NT listings. Related terms are generously applied, thus enhancing the usefulness of the Hierarchical Listing. Greater access to the Hierarchical Listing may be achieved with the collateral use of Volume 2 - Access Vocabulary

Smart automotive technology adherence to the law: (de)constructing road rules for autonomous system development, verification and safety

Driving is an intuitive task that requires skill, constant alertness and vigilance for unexpected events. The driving task also requires long concentration spans, focusing on the entire task for prolonged periods, and sophisticated negotiation skills with other road users including wild animals. Modern motor vehicles include an array of smart assistive and autonomous driving systems capable of subsuming some, most, or in limited cases, all of the driving task. Building these smart automotive systems requires software developers with highly technical software engineering skills, and now a lawyer’s in-depth knowledge of traffic legislation as well. This article presents an approach for deconstructing the complicated legalese of traffic law and representing its requirements and flow. Our approach (de)constructs road rules in legal terminology and specifies them in ‘structured English logic’ that is expressed as ‘Boolean logic’ for automation and ‘Lawmaps’ for visualization. We demonstrate an example using these tools leading to the construction and validation of a ‘Bayesian Network model’. We strongly believe these tools to be approachable by programmers and the general public, useful in development of Artificial Intelligence to underpin motor vehicle smart systems, and in validation to ensure these systems are considerate of the law when making decisions.fals

NASA Tech Briefs, August 1991

Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences

NASA thesaurus. Volume 1: Hierarchical Listing

There are over 17,000 postable terms and nearly 4,000 nonpostable terms approved for use in the NASA scientific and technical information system in the Hierarchical Listing of the NASA Thesaurus. The generic structure is presented for many terms. The broader term and narrower term relationships are shown in an indented fashion that illustrates the generic structure better than the more widely used BT and NT listings. Related terms are generously applied, thus enhancing the usefulness of the Hierarchical Listing. Greater access to the Hierarchical Listing may be achieved with the collateral use of Volume 2 - Access Vocabulary and Volume 3 - Definitions

Dynamic Analysis, Identification and Control Studies of Aero-Engine Model Rotor-Bearing Systems

Aero-engines have high speed rotors carrying multi-stage turbine and compressor discs. Such systems need continuous monitoring during the operating regime. These rotors are mounted on ball bearings supported with squeeze film dampers and connected to stator casings. The motions of bearings and rotor are influenced by each other and therefore such a system requires structural dynamic studies. These rotors involve several nonlinear factors including contact forces, varying compliance vibration of ball bearing, nonlinear oil-film force of squeeze film damper etc Solving such nonlinear dynamic problems using the traditional transfer matrix method, modal synthesis approach, finite element method or impedance coupling technique is therefore a challenging task. Present work focuses on modelling of rotors using ball bearing nonlinearities along with nonlinear secondary transient excitations using finite element modelling. In order to validate the finite element model, preliminary dynamic analysis is carried out using linear spring-damper bearing elements. Results are illustrated both for LP rotor model and twin-spool rotor. Initially, the natural frequencies obtained from the computer program based on Timoshenko beam elements are validated with ANSYS results. Further, the results are also validated with those obtained from impact hammer tests on a scaled dual disk rotor-bearing system. To utilize this finite element model, the time and frequency-domain response studies are conducted with double-row ball bearing forces, rub-impact forces, Muszynska’s gas transients along with squeeze-film forces. In all the cases, differences from simple rotor supported by single-row ball bearings with only unbalance excitations have been reported. Using the fundamental frequency and its amplitude, an inverse modelling approach is applied to predict the parameters of rotor bearing system such as increased bearing clearance, changes in disc unbalances and the centralizing spring constants in squeeze-film damper. In this regard, a trained model of 3-layer perceptron neural network model is employed. In the second study, changes in dynamic response due to waviness and race-way defects in ball-bearings are first studied using modified contact force relations. Using this data, type of bearing fault is estimated from the statistical parameters of the time-domain signal by training an unsupervised Kohenen’s neural network model. Here, the simulated data is collected from the rotor over an operating speed range. In the third study, the additional stiffness of rotor due to rub-impact forces is identified from optimization modelling. Such identification of rotor stiffening effect using finite element modelling is a new concept. Two types of control studies are proposed to minimize the amplitudes of rotor during the critical operating conditions. Semi active electromagnetic damper design helps in reducing vibration amplitudes of the LP rotor over a frequency range of interest. Here, the damper comprises of an electro-magnet and a spring. The required current and spring stiffness are identified from the basic relations and the results of control are illustrated with a two-disc LP rotor model. In active controller design, an electromagnetic actuator model is employed. The nominal gap maintained between the rotor and actuator coils is used in computing the actuator force. A proportional derivative (PD) control strategy is used to estimate the required forces. A neural network based alternate control scheme also proposed to compute the required actuator forces. In overall, the work focussed on the dynamic analysis of dual disc rotor model subjected to parametric nonlinear bearing loads under the action of various external forces and some controller design aspects applicable to this rotor