Online Interval Type-2 Fuzzy Extreme Learning Machine Applied to 3D Path Following for Remotely Operated Underwater Vehicles

In marine missions that involve 3D path following tasks, the overall goal of Underwater Vehicles (UVs) is the successful completion of a path previously specified by the operator. This implies that the path must be followed by the UV as closely as possible and arrive at a location for collection by a vessel. In this paper, an Online Interval Type-2 Fuzzy Extreme Learning Machine (OIT2-FELM) is suggested to achieve a robust following behaviour along a predefined 3D path using a Remotely Operated Underwater Vehicle (ROV). The proposed machine is a fast sequential learning scheme to the training of a more generalised model of TSK Interval Type-2 Fuzzy Inference Systems (TSK IT2 FISs) equivalent to Single Layer Feedforward Neural Networks (SLFNs). Learning new input data in the OIT2-FELM can be done one-by-one or chunk-by-chunk with a fixed or varying size. The OIT2-FELM is implemented in a hierarchical navigation strategy (HNS) as the main guidance mechanism to infer local control motions and to provide the ROV with the necessary autonomy to complete a predefined 3D path. For local path-planning, the OIT2-FELM performs signal classification for obstacle avoidance and target detection based on data collected by an on-board scan sonar. To evaluate the performance of the proposed OIT2-FELM, two different experiments are suggested. First, a number of benchmark problems in the field of non-linear system identification, regression and classification problems are used. Secondly, a number of experiments to the completion of a predefined 3D path using an ROV is implemented. Compared to other fuzzy strategies, the OIT2-FELM offered two significant capabilities. On the one hand, the OIT2-FELM provides a better treatment of uncertainty and noisy signals in underwater environments while improving the ROV's performance. Secondly, online learning in OIT2-FELM allows continuous knowledge discovery from survey data to infer the surroundings of the ROV. Experiment results to the completion of 3D paths show the effectiveness of the proposed approach to handle uncertainty and produce reasonable classification predictions (∼90.5% accuracy in testing data).</p

A Linear Criterion to sort Color Components in Images

The color and its representation play a basic role in Image Analysis process. Several methods can be beneficial whenever they have a correct representation of wave-length variations used to represent scenes with a camera. A wide variety of spaces and color representations is founded in specialized literature. Each one is useful in concrete circumstances and others may offer redundant color information (for instance, all RGB components are high correlated). This work deals with the task of identifying and sorting which component from several color representations offers the majority of information about the scene. This approach is based on analyzing linear dependences among each color component, by the implementation of a new sorting algorithm based on entropy. The proposal is tested in several outdoor/indoor scenes with different light conditions. Repeatability and stability are tested in order to guarantee its use in several image analysis applications. Finally, the results of this work have been used to enhance an external algorithm to compensate the camera random vibrations.El color y su representación juegan un papel fundamental en el proceso de análisis de imagen. Varios métodos pueden ser beneficiosos siempre que tengan una representación correcta de las variaciones de longitud de onda usadas para representar la escena. Una amplia variedad de espacios y representaciones de color se basa en la literatura especializada. Cada uno de ellos es útil en circunstancias concretas y puede ofrecer información de color redundante (por ejemplo, todos los componentes RGB están altamente correlacionados). En este trabajo se identifica y clasifica cuál componente ofrece la mayor cantidad de información acerca de la escena, a partir de varias representaciones de color. Este enfoque se basa en el análisis de las dependencias lineales entre cada canal y la implementación de un nuevo algoritmo para clasificar los componentes en base a la entropía. La propuesta se pone a prueba en varias escenas al aire libre y en interiores con diferentes condiciones de luz. La repetitividad y la estabilidad son probadas para garantizar su uso en aplicaciones de análisis de imágenes. Finalmente, los resultados de este trabajo son usados para mejorar un algoritmo externo para la compensación de vibraciones

Modelling, Design and Robust Control of a Remotely Operated Underwater Vehicle

Underwater remotely operated vehicles (ROVs) play an important role in a number of shallow and deep-water missions for marine science, oil and gas extraction, exploration and salvage. In these applications, the motions of the ROV are guided either by a human pilot on a surface support vessel through an umbilical cord providing power and telemetry, or by an automatic pilot. In the case of automatic control, ROV state feedback is provided by acoustic and inertial sensors and this state information, along with a controller strategy, is used to perform several tasks such as station-keeping and auto-immersion/heading among others. In this paper, the modelling design and control of the Kaxan ROV is presented: i) The complete six degrees of freedom, non linear hydrodynamic model with its parameters, ii) the Kaxan hardware/software architecture, iii) numerical simulations in Matlab/Simulink platform of a model-free second order sliding mode control along with ocean currents as disturbances and thruster dynamics, iv) a virtual environment to visualize the motion of the Kaxan ROV and v) experimental results of a one degree of freedom underwater system

A Linear Criterion to sort Color Components in Images

The color and its representation play a basic role in Image Analysis process. Several methods can be beneficial whenever they have a correct representation of wave-length variations used to represent scenes with a camera. A wide variety of spaces and color representations is founded in specialized literature. Each one is useful in concrete circumstances and others may offer redundant color information (for instance, all RGB components are high correlated). This work deals with the task of identifying and sorting which component from several color representations offers the majority of information about the scene. This approach is based on analyzing linear dependences among each color component, by the implementation of a new sorting algorithm based on entropy. The proposal is tested in several outdoor/indoor scenes with different light conditions. Repeatability and stability are tested in order to guarantee its use in several image analysis applications. Finally, the results of this work have been used to enhance an external algorithm to compensate the camera random vibrations.El color y su representación juegan un papel fundamental en el proceso de análisis de imagen. Varios métodos pueden ser beneficiosos siempre que tengan una representación correcta de las variaciones de longitud de onda usadas para representar la escena. Una amplia variedad de espacios y representaciones de color se basa en la literatura especializada. Cada uno de ellos es útil en circunstancias concretas y puede ofrecer información de color redundante (por ejemplo, todos los componentes RGB están altamente correlacionados). En este trabajo se identifica y clasifica cuál componente ofrece la mayor cantidad de información acerca de la escena, a partir de varias representaciones de color. Este enfoque se basa en el análisis de las dependencias lineales entre cada canal y la implementación de un nuevo algoritmo para clasificar los componentes en base a la entropía. La propuesta se pone a prueba en varias escenas al aire libre y en interiores con diferentes condiciones de luz. La repetitividad y la estabilidad son probadas para garantizar su uso en aplicaciones de análisis de imágenes. Finalmente, los resultados de este trabajo son usados para mejorar un algoritmo externo para la compensación de vibraciones

Improvement of Ultrasonic Pulse Generator for Automatic Pipeline Inspection

This paper presents the improvement of an ultrasonic pulse generator for a pipeline inspection gauge (PIG), which uses 64 transducers for inspecting distances up to 100 km with an axial resolution fixed at 3 mm and variable speeds between 0 and 2 m/s. An ultrasonic pulse generator is composed of a high-voltage (HV) MOSFETs, driver logic and an HV power supply. We used a DC-HV DC converter device as the HV power supply because it reduces the size of the ultrasound system considerably. However, pipeline geometry and inspection effects such as hammer and shock cause a variable pulse repetition frequency (PRF), producing voltage drops, poor quality of the HV pulse generated, failures in the dimensioning of defects and damage to devices by over-voltage. Our improvement is to implement a control scheme to maintain the high quality of the HV regardless of the variable PRF. To achieve this, we characterized three transfer functions of the DC-HV DC converter, varying the connected load to 10%, 45% and 80%. For the characterization, we used the least squares technique, considering an autoregressive exogenous (ARX) model. Later, we compared three control schemes: (1) proportional-integral-derivative (PID) tuned by simultaneous optimization of several responses (SOSR), (2) PID tuned by a neural network (NN) and (3) PI tuned by the analytical design method (ADM). The metrics used to compare the control schemes were the recovery time, the maximum over-voltage and the excess energy when the shock and hammer effects happen to occur. Finally, to verify the improvement of the HV pulser, we compared the ultrasonic pulses generated for various frequencies and amplitudes using the pulse generator with and without the control scheme