Implementation of a Long Short-Term Memory Neural Network-Based Algorithm for Dynamic Obstacle Avoidance.

Autonomous mobile robots are essential to the industry, and human-robot interactions are becoming more common nowadays. These interactions require that the robots navigate scenarios with static and dynamic obstacles in a safely manner, avoiding collisions. This paper presents a physical implementation of a method for dynamic obstacle avoidance using a long short-term memory (LSTM) neural network that obtains information from the mobile robot's LiDAR for it to be capable of navigating through scenarios with static and dynamic obstacles while avoiding collisions and reaching its goal. The model is implemented using a TurtleBot3 mobile robot within an OptiTrack motion capture (MoCap) system for obtaining its position at any given time. The user operates the robot through these scenarios, recording its LiDAR readings, target point, position inside the MoCap system, and its linear and angular velocities, all of which serve as the input for the LSTM network. The model is trained on data from multiple user-operated trajectories across five different scenarios, outputting the linear and angular velocities for the mobile robot. Physical experiments prove that the model is successful in allowing the mobile robot to reach the target point in each scenario while avoiding the dynamic obstacle, with a validation accuracy of 98.02%.

Model-Free High-Order Sliding Mode Controller for Station-Keeping of an Autonomous Underwater Vehicle in Manipulation Task: Simulations and Experimental Validation.

The use of autonomous underwater vehicles (AUVs) has expanded in recent years to include inspection, maintenance, and repair missions. For these tasks, the vehicle must maintain its position while inspections or manipulations are performed. Some station-keeping controllers for AUVs can be found in the literature that exhibits robust performance against external disturbances. However, they are either model-based or require an observer to deal with the disturbances. Moreover, most of them have been evaluated only by numerical simulations. In this paper, the feasibility of a model-free high-order sliding mode controller for the station-keeping problem is validated. The proposed controller was evaluated through numerical simulations and experiments in a semi-Olympic swimming pool, introducing external disturbances that remained unknown to the controller. Results have shown robust performance in terms of the root mean square error (RMSE) of the vehicle position. The simulation resulted in the outstanding station-keeping of the BlueROV2 vehicle, as the tracking errors were kept to zero throughout the simulation, even in the presence of strong ocean currents. The experimental results demonstrated the robustness of the controller, which was able to maintain the RMSE in the range of 1-4 cm for the depth of the vehicle, outperforming related work, even when the disturbance was large enough to produce thruster saturation.

Experimental Validation of a Model-Free High-Order Sliding Mode Controller with Finite-Time Convergence for Trajectory Tracking of Autonomous Underwater Vehicles.

Several control strategies have been proposed for the trajectory tracking problem of Autonomous Underwater Vehicles (AUV). Most of them are model-based, hence, detailed knowledge of the parameters of the robot is needed. Few works consider a finite-time convergence in their controllers, which offers strong robustness and fast convergence compared with asymptotic or exponential solutions. Those finite-time controllers do not permit the users to predefine the convergence time, which can be useful for a more efficient use of the robot's energy. This paper presents the experimental validation of a model-free high-order Sliding Mode Controller (SMC) with finite-time convergence in a predefined time. The convergence time is introduced by the simple change of a time-base parameter. The aim is to validate the controller so it can be implemented for cooperative missions where the communication is limited or null. Results showed that the proposed controller can drive the robot to the desired depth and heading trajectories in the predefined time for all the cases, reducing the error by up to 75% and 41% when compared with a PID and the same SMC with asymptotic convergence. The energy consumption was reduced 35% and 50% when compared with those same controllers.

Finite-Time Controller for Coordinated Navigation of Unmanned Underwater Vehicles in a Collaborative Manipulation Task.

Unmanned underwater vehicles perform inspection and maintenance tasks in complex and changing environments. Some of these tasks require synchronous navigation of multiple vehicles, which is challenging. This paper proposes a synchronous navigation scheme for two BlueROV2 underwater vehicles for a coordinated multi-vehicle task. In the proposed scheme, the vehicles perform the collaborative task of grasping, transporting, and releasing an object. In this scheme, no vehicle-to-vehicle communication is required. A model-free second-order sliding mode controller with finite-time convergence is used to accomplish this task. The controller's convergence time is user-defined and does not depend on the physical or hydrodynamic parameters of the vehicle, unlike the other finite-time controllers found in the literature. Simulation experiments were conducted to verify the controller's performance, including high ocean currents as external disturbances. Comparisons were made with two state-of-the-art controllers with finite-time convergence. The results showed that the proposed controller achieved the best results, as the user-defined convergence time was achieved for both vehicles and the collaborative task was completed, no ripples, deviations, or oscillations were observed, and no chattering occurred. The results proved the robustness of the controller in the presence of high ocean currents without the need to readjust the parameters.

Changes in the Brain Activity and Visual Performance of Patients with Strabismus and Amblyopia after a Compete Cycle of Light Therapy.

This research assesses the brain activity and visual performance at baseline and after light therapy (LTH), of seventeen patients with strabismus and amblyopia (SA), and eleven healthy controls (HCs) from Querétaro, México. Quantitative electroencephalogram analysis (qEEG) was used to record the brain activity, and clinical metrics such as the visual acuity, angle of deviation, phoria state, stereopsis, and visual fields determined the visual performance. Results showed a constant higher alpha-wave frequency for HCs. Low voltages remained negative for HCs and positive for SA patients across stimulation. After LTH, high voltage increased in SA patients, and decreased in HCs. A second spectral peak, (theta-wave), was exclusively recorded in SA patients, at baseline and after LTH. Positive Spearman correlations for alpha-wave frequency, low and high voltages were only seen in SA patients. Synchronized brain activity was recorded in all SA patients stimulated with filters transmitting light in the blue but not in the red spectrum. Enhancement in the visual performance of SA patients was found, whereas deterioration of the phoria state and a decrease in the amount of stereopsis was seen in HCs. To conclude, only a suffering brain and a visual pathway which needs to be enabled can benefit from LTH.

Sensor Data Fusion for a Mobile Robot Using Neural Networks.

Mobile robots must be capable to obtain an accurate map of their surroundings to move within it. To detect different materials that might be undetectable to one sensor but not others it is necessary to construct at least a two-sensor fusion scheme. With this, it is possible to generate a 2D occupancy map in which glass obstacles are identified. An artificial neural network is used to fuse data from a tri-sensor (RealSense Stereo camera, 2D 360° LiDAR, and Ultrasonic Sensors) setup capable of detecting glass and other materials typically found in indoor environments that may or may not be visible to traditional 2D LiDAR sensors, hence the expression improved LiDAR. A preprocessing scheme is implemented to filter all the outliers, project a 3D pointcloud to a 2D plane and adjust distance data. With a Neural Network as a data fusion algorithm, we integrate all the information into a single, more accurate distance-to-obstacle reading to finally generate a 2D Occupancy Grid Map (OGM) that considers all sensors information. The Robotis Turtlebot3 Waffle Pi robot is used as the experimental platform to conduct experiments given the different fusion strategies. Test results show that with such a fusion algorithm, it is possible to detect glass and other obstacles with an estimated root-mean-square error (RMSE) of 3 cm with multiple fusion strategies.

Adaptable Bed for Curved-Layered Fused Deposition Modeling of Nonplanar Structures: A Proof of Concept.

Curved-layered fused deposition modeling demands a curved mandrel for each of the designs to fabricate. This results in more fabrication time and material used. To overcome this, an adaptable pin-base mandrel is presented. The fabrication of curved structures with such mandrel uses the extruder as a pushing tool, pushing each of the pins to the depths required for each surface. After the base surface is formed, the conventional process of curved fused deposition modeling (FDM) is used. Several lattice shell structures composed of nonplanar layers were fabricated on FDM mandrels and the adaptable pin-bed proposed here. The comparison of the manufacturing results showed that the adaptable base allows the successful fabrication of the samples. The solution exposed here represents a proof of concept to validate the idea. The adaptable base presented in this study is unique and it brings advantages to the fabrication of curved-layered structures.

Neuro-Sliding Control for Underwater ROV's Subject to Unknown Disturbances.

Proposed in this paper is a model-free and chattering-free second order sliding mode control (2nd-SMC) in combination with a backpropagation neural network (BP-NN) control scheme for underwater vehicles to deal with external disturbances (i.e., ocean currents) and parameter variations caused, for instance, by the continuous interchange of tools. The compound controller, here called the neuro-sliding control (NSC), takes advantage of the 2nd-SMC robustness and fast response to drive the position tracking error to zero. Simultaneously, the BP-NN contributes with its capability to estimate and to compensate online the hydrodynamic variations of the vehicle. When a change in the vehicle's hydrodynamics occurs, the 2nd-SMC may no longer be able to compensate for the variations since its feedback gains are tuned for a different condition; thus, in order to preserve the desired performance, it is necessary to re-tune the feedback gains, which a cumbersome and time consuming task. To solve this, a viable choice is to implement a BP-NN control scheme along with the 2nd-SMC that adds or removes energy from the system according to the current condition it is in, in order to keep, or even improve, its performance. The effectiveness of the proposed compound controller was supported by experiments carried out on a mini-ROV.

Neural Network Direct Control with Online Learning for Shape Memory Alloy Manipulators.

New actuators and materials are constantly incorporated into industrial processes, and additional challenges are posed by their complex behavior. Nonlinear hysteresis is commonly found in shape memory alloys, and the inclusion of a suitable hysteresis model in the control system allows the controller to achieve a better performance, although a major drawback is that each system responds in a unique way. In this work, a neural network direct control, with online learning, is developed for position control of shape memory alloy manipulators. Neural network weight coefficients are updated online by using the actuator position data while the controller is applied to the system, without previous training of the neural network weights, nor the inclusion of a hysteresis model. A real-time, low computational cost control system was implemented; experimental evaluation was performed on a 1-DOF manipulator system actuated by a shape memory alloy wire. Test results verified the effectiveness of the proposed control scheme to control the system angular position, compensating for the hysteretic behavior of the shape memory alloy actuator. Using a learning algorithm with a sine wave as reference signal, a maximum static error of 0.83° was achieved when validated against several set-points within the possible range.

Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning.

Cryptocurrencies are becoming increasingly relevant in the financial world and can be considered as an emerging market. The low barrier of entry and high data availability of the cryptocurrency market makes it an excellent subject of study, from which it is possible to derive insights into the behavior of markets through the application of sentiment analysis and machine learning techniques for the challenging task of stock market prediction. While there have been some previous studies, most of them have focused exclusively on the behavior of Bitcoin. In this paper, we propose the usage of common machine learning tools and available social media data for predicting the price movement of the Bitcoin, Ethereum, Ripple and Litecoin cryptocurrency market movements. We compare the utilization of neural networks (NN), support vector machines (SVM) and random forest (RF) while using elements from Twitter and market data as input features. The results show that it is possible to predict cryptocurrency markets using machine learning and sentiment analysis, where Twitter data by itself could be used to predict certain cryptocurrencies and that NN outperform the other models.

Neural Network-Based Self-Tuning PID Control for Underwater Vehicles.

For decades, PID (Proportional + Integral + Derivative)-like controllers have been successfully used in academia and industry for many kinds of plants. This is thanks to its simplicity and suitable performance in linear or linearized plants, and under certain conditions, in nonlinear ones. A number of PID controller gains tuning approaches have been proposed in the literature in the last decades; most of them off-line techniques. However, in those cases wherein plants are subject to continuous parametric changes or external disturbances, online gains tuning is a desirable choice. This is the case of modular underwater ROVs (Remotely Operated Vehicles) where parameters (weight, buoyancy, added mass, among others) change according to the tool it is fitted with. In practice, some amount of time is dedicated to tune the PID gains of a ROV. Once the best set of gains has been achieved the ROV is ready to work. However, when the vehicle changes its tool or it is subject to ocean currents, its performance deteriorates since the fixed set of gains is no longer valid for the new conditions. Thus, an online PID gains tuning algorithm should be implemented to overcome this problem. In this paper, an auto-tune PID-like controller based on Neural Networks (NN) is proposed. The NN plays the role of automatically estimating the suitable set of PID gains that achieves stability of the system. The NN adjusts online the controller gains that attain the smaller position tracking error. Simulation results are given considering an underactuated 6 DOF (degrees of freedom) underwater ROV. Real time experiments on an underactuated mini ROV are conducted to show the effectiveness of the proposed scheme.

Portable System for Monitoring the Microclimate in the Footwear-Foot Interface.

A new, continuously-monitoring portable device that monitors the diabetic foot has shown to help in reduction of diabetic foot complications. Persons affected by diabetic foot have shown to be particularly sensitive in the plantar surface; this sensitivity coupled with certain ambient conditions may cause dry skin. This dry skin leads to the formation of fissures that may eventually result in a foot ulceration and subsequent hospitalization. This new device monitors the micro-climate temperature and humidity areas between the insole and sole of the footwear. The monitoring system consists of an array of ten sensors that take readings of relative humidity within the range of 100% ± 2% and temperature within the range of -40 °C to 123.8 ± 0.3 °C. Continuous data is collected using embedded C software and the recorded data is processed in Matlab. This allows for the display of data; the implementation of the iterative Gauss-Newton algorithm method was used to display an exponential response curve. Therefore, the aim of our system is to obtain feedback data and provide the critical information to various footwear manufacturers. The footwear manufactures will utilize this critical information to design and manufacture diabetic footwear that reduce the risk of ulcers in diabetic feet.

A new adaptive self-tuning Fourier coefficients algorithm for periodic torque ripple minimization in permanent magnet synchronous motors (PMSM).

A New Adaptive Self-Tuning Fourier Coefficients Algorithm for Periodic Torque Ripple Minimization in Permanent Magnet Synchronous Motors (PMSM) Torque ripple occurs in Permanent Magnet Synchronous Motors (PMSMs) due to the non-sinusoidal flux density distribution around the air-gap and variable magnetic reluctance of the air-gap due to the stator slots distribution. These torque ripples change periodically with rotor position and are apparent as speed variations, which degrade the PMSM drive performance, particularly at low speeds, because of low inertial filtering. In this paper, a new self-tuning algorithm is developed for determining the Fourier Series Controller coefficients with the aim of reducing the torque ripple in a PMSM, thus allowing for a smoother operation. This algorithm adjusts the controller parameters based on the component's harmonic distortion in time domain of the compensation signal. Experimental evaluation is performed on a DSP-controlled PMSM evaluation platform. Test results obtained validate the effectiveness of the proposed self-tuning algorithm, with the Fourier series expansion scheme, in reducing the torque ripple.