Drift-Free Latent Space Representation for Soft Strain Sensors

Soft strain sensors are becoming increasingly popular for obtaining tactile information in soft robotic applications. Diverse technological solutions are being investigated to design these sensors. Simultaneously, new methods for modeling these sensor are being proposed due to their highly nonlinear, time varying properties. Among them, machine learning based approaches, particularly using dynamic recurrent neural networks look the most promising. However, these complex networks have large number of free parameters to be tuned, making it difficult to apply them for real-world applications. This paper introduces the concept of transfer learning for modelling soft strain sensors, which allows us to utilize information learned in one task to be applied to another task. We demonstrate this technique on a passive anthropomorphic finger with embedded strain sensors used for two regression tasks. We show how the transfer learning approach can drastically reduce the number of free parameters to be tuned for learning new skills. This work is an important step towards scaling of sensor networks (algorithm-wise) and for using soft sensor data for high-level control tasks

Multi-modal Sensor Fusion for Learning Rich Models for Interacting Soft Robots

Soft robots are typically approximated as low-dimensional systems, especially when learning-based methods are used. This leads to models that are limited in their capability to predict the large number of deformation modes and interactions that a soft robot can have. In this work, we present a deep-learning methodology to learn high-dimensional visual models of a soft robot combining multimodal sensorimotor information. The models are learned in an end-to-end fashion, thereby requiring no intermediate sensor processing or grounding of data. The capabilities and advantages of such a modelling approach are shown on a soft anthropomorphic finger with embedded soft sensors. We also show that how such an approach can be extended to develop higher level cognitive functions like identification of the self and the external environment and acquiring object manipulation skills. This work is a step towards the integration of soft robotics and developmental robotics architectures to create the next generation of intelligent soft robots

Distributed Fiber Ultrasonic Sensor and Pattern Recognition Analytics

Ultrasound interrogation and structural health monitoring technologies have found a wide array of applications in the health care, aerospace, automobile, and energy sectors. To achieve high spatial resolution, large array electrical transducers have been used in these applications to harness sufficient data for both monitoring and diagnoses. Electronic-based sensors have been the standard technology for ultrasonic detection, which are often expensive and cumbersome for use in large scale deployments. Fiber optical sensors have advantageous characteristics of smaller cross-sectional area, humidity-resistance, immunity to electromagnetic interference, as well as compatibility with telemetry and telecommunications applications, which make them attractive alternatives for use as ultrasonic sensors. A unique trait of fiber sensors is its ability to perform distributed acoustic measurements to achieve high spatial resolution detection using a single fiber. Using ultrafast laser direct-writing techniques, nano-reflectors can be induced inside fiber cores to drastically improve the signal-to-noise ratio of distributed fiber sensors. This dissertation explores the applications of laser-fabricated nano-reflectors in optical fiber cores for both multi-point intrinsic Fabry–Perot (FP) interferometer sensors and a distributed phase-sensitive optical time-domain reflectometry (φ-OTDR) to be used in ultrasound detection. Multi-point intrinsic FP interferometer was based on swept-frequency interferometry with optoelectronic phase-locked loop that interrogated cascaded FP cavities to obtain ultrasound patterns. The ultrasound was demodulated through reassigned short time Fourier transform incorporating with maximum-energy ridges tracking. With tens of centimeters cavity length, this approach achieved 20kHz ultrasound detection that was finesse-insensitive, noise-free, high-sensitivity and multiplex-scalability. The use of φ-OTDR with enhanced Rayleigh backscattering compensated the deficiencies of low inherent signal-to-noise ratio (SNR). The dynamic strain between two adjacent nano-reflectors was extracted by using 3×3 coupler demodulation within Michelson interferometer. With an improvement of over 35 dB SNR, this was adequate for the recognition of the subtle differences in signals, such as footstep of human locomotion and abnormal acoustic echoes from pipeline corrosion. With the help of artificial intelligence in pattern recognition, high accuracy of events’ identification can be achieved in perimeter security and structural health monitoring, with further potential that can be harnessed using unsurprised learning

40 K Neon liquid energy storage unit

Cryocoolers have been progressively replacing the use of the stored cryogens in cryogenic chains used for detector cooling, thanks to their higher and higher reliability. However, the mechanical vibrations, the electromagnetic interferences and the temperature fluctuations inherent to their functioning could reduce the sensor’s sensitivity. In order to minimize this problem, compact thermal energy storage units (ESU) are studied, devices able to store thermal energy without significant temperature increase. These devices can be used as a temporary cold source making it possible to turn the cryocooler OFF providing a proper environment for the sensor. A heat switch is responsible for the thermal decoupling of the ESU from the cryocooler’s temperature that increases when turned OFF. In this work, several prototypes working around 40 K were designed, built and characterized. They consist in a low temperature cell that contains the liquid neon connected to an expansion volume at room temperature for gas storage during the liquid evaporation phase. To turn this system insensitive to the gravity direction, the liquid is retained in the low temperature cell by capillary effect in a porous material. Thanks to pressure regulation of the liquid neon bath, 900 J were stored at 40K. The higher latent heat of the liquid and the inexistence of triple point transitions at 40 K turn the pressure control during the evaporation a versatile and compact alternative to an ESU working at the triple point transitions. A quite compact second prototype ESU directly connected to the cryocooler cold finger was tested as a temperature stabilizer. This device was able to stabilize the cryocooler temperature ((≈ 40K ±1 K) despite sudden heat bursts corresponding to twice the cooling power of the cryocooler. This thesis describes the construction of these devices as well as the tests performed. It is also shown that the thermal model developed to predict the thermal behaviour of these devices, implemented as a software,describes quite well the experimental results. Solutions to improve these devices are also proposed.Fundação para a Ciência e a Tecnologia - SFRH/BD/70427/2010 scholarship; PTDC/EMEMFE/ 66533/2006; PTDC/EME-MFE/101448/2008; PEst-OE/FIS/UI0068/2012-2014); FCT-Embaixada de França — Programa Pessoa 2011/201

Data-driven robotic manipulation of cloth-like deformable objects : the present, challenges and future prospects

Manipulating cloth-like deformable objects (CDOs) is a long-standing problem in the robotics community. CDOs are flexible (non-rigid) objects that do not show a detectable level of compression strength while two points on the article are pushed towards each other and include objects such as ropes (1D), fabrics (2D) and bags (3D). In general, CDOs’ many degrees of freedom (DoF) introduce severe self-occlusion and complex state–action dynamics as significant obstacles to perception and manipulation systems. These challenges exacerbate existing issues of modern robotic control methods such as imitation learning (IL) and reinforcement learning (RL). This review focuses on the application details of data-driven control methods on four major task families in this domain: cloth shaping, knot tying/untying, dressing and bag manipulation. Furthermore, we identify specific inductive biases in these four domains that present challenges for more general IL and RL algorithms.Publisher PDFPeer reviewe

Novel methods for active reservoir monitoring and flow rate allocation of intelligent wells

The value added by intelligent wells (I-wells) derives from real-time, reservoir and production performance monitoring together with zonal, downhole flow control. Unfortunately, downhole sensors that can directly measure the zonal flow rates and phase cuts required for optimal control of the well’s producing zones are not normally installed. Instead, the zonal, Multi-phase Flow Rates (MPFRs) are calculated from indirect measurements (e.g. from zonal pressures, temperatures and the total well flow rate), an approach known as soft-sensing. To-date all published techniques for zonal flow rate allocation in multi-zone I-wells are “passive” in that they calculate the required parameters to estimate MPFRs for a fixed given configuration of the completion. These techniques are subject to model error, but also to errors stemming from measurement noise when there is insufficient data duplication for accurate parameter estimation. This thesis describes an “active” soft-sensing technique consisting of two sequential optimisation steps. First step calculates MPFRs while the second one uses a direct search method based on Deformed Configurations to optimise the sequence of Interval Control Valve positions during a routine multi-rate test in an I-well. This novel approach maximises the accuracy of the calculated reservoir properties and MPFRs. Four “active monitoring” levels are discussed. Each one uses a particular combination of available indirect measurements from well performance monitoring systems. Level one is the simplest, requiring a minimal amount of well data. The higher levels require more data; but provide, in return, a greater understanding of produced fluids volumes and the reservoir’s properties at both a well and a zonal level. Such estimation of the reservoir parameters and MPFRs in I-wells is essential for effective well control strategies to optimise the production volumes. An integrated, control and monitoring (ICM) workflow is proposed which employs the active soft-sensing algorithm modified to maximise I-well oil production via real-time zonal production control based on estimates of zonal reservoir properties and their updates. Analysis of convergence rate of ICM workflow to optimise different objective functions shows that very accurate zonal properties are not required to optimise the oil production. The proposed reservoir monitoring and MPFR allocation workflow may also be used for designing in-well monitoring systems i.e. to predict which combination of sensors along with their measurement quality is required for effective well and reservoir monitoring

Langley aerospace test highlights, 1985

The role of the Langley Research Center is to perform basic and applied research necessary for the advancement of aeronautics and space flight, to generate new and advanced concepts for the accomplishment of related national goals, and to provide research advice, technological support, and assistance to other NASA installations, other government agencies, and industry. Significant tests which were performed during calendar year 1985 in Langley test facilities, are highlighted. Both the broad range of the research and technology activities at the Langley Research Center and the contributions of this work toward maintaining United States leadership in aeronautics and space research, are illustrated. Other highlights of Langley research and technology for 1985 are described in Research and Technology-1985 Annual Report of the Langley Research Center

On robust and adaptive soft sensors.

In process industries, there is a great demand for additional process information such as the product quality level or the exact process state estimation. At the same time, there is a large amount of process data like temperatures, pressures, etc. measured and stored every moment. This data is mainly measured for process control and monitoring purposes but its potential reaches far beyond these applications. The task of soft sensors is the maximal exploitation of this potential by extracting and transforming the latent information from the data into more useful process knowledge. Theoretically, achieving this goal should be straightforward since the process data as well as the tools for soft sensor development in the form of computational learning methods, are both readily available. However, contrary to this evidence, there are still several obstacles which prevent soft sensors from broader application in the process industry. The identification of the sources of these obstacles and proposing a concept for dealing with them is the general purpose of this work. The proposed solution addressing the issues of current soft sensors is a conceptual architecture for the development of robust and adaptive soft sensing algorithms. The architecture reflects the results of two review studies that were conducted during this project. The first one focuses on the process industry aspects of soft sensor development and application. The main conclusions of this study are that soft sensor development is currently being done in a non-systematic, ad-hoc way which results in a large amount of manual work needed for their development and maintenance. It is also found that a large part of the issues can be related to the process data upon which the soft sensors are built. The second review study dealt with the same topic but this time it was biased towards the machine learning viewpoint. The review focused on the identification of machine learning tools, which support the goals of this work. The machine learning concepts which are considered are: (i) general regression techniques for building of soft sensors; (ii) ensemble methods; (iii) local learning; (iv) meta-learning; and (v) concept drift detection and handling. The proposed architecture arranges the above techniques into a three-level hierarchy, where the actual prediction-making models operate at the bottom level. Their predictions are flexibly merged by applying ensemble methods at the next higher level. Finally from the top level, the underlying algorithm is managed by means of metalearning methods. The architecture has a modular structure that allows new pre-processing, predictive or adaptation methods to be plugged in. Another important property of the architecture is that each of the levels can be equipped with adaptation mechanisms, which aim at prolonging the lifetime of the resulting soft sensors. The relevance of the architecture is demonstrated by means of a complex soft sensing algorithm, which can be seen as its instance. This algorithm provides mechanisms for autonomous selection of data preprocessing and predictive methods and their parameters. It also includes five different adaptation mechanisms, some of which can be applied on a sample-by-sample basis without any requirement to store the on-line data. Other, more complex ones are started only on-demand if the performance of the soft sensor drops below a defined level. The actual soft sensors are built by applying the soft sensing algorithm to three industrial data sets. The different application scenarios aim at the analysis of the fulfilment of the defined goals. It is shown that the soft sensors are able to follow changes in dynamic environment and keep a stable performance level by exploiting the implemented adaptation mechanisms. It is also demonstrated that, although the algorithm is rather complex, it can be applied to develop simple and transparent soft sensors. In another experiment, the soft sensors are built without any manual model selection or parameter tuning, which demonstrates the ability of the algorithm to reduce the effort required for soft sensor development. However, if desirable, the algorithm is at the same time very flexible and provides a number of parameters that can be manually optimised. Evidence of the ability of the algorithm to deploy soft sensors with minimal training data and as such to provide the possibility to save the time consuming and costly training data collection is also given in this work