Sonar Sensors Research Articles

A SURVEY: SOLAR POWERED IOT BASED SMART GARDEN/AGRICULTURE

In current digital world, a person anticipates automation which can make the work simple and easy. In this busy world a person has the problem of remembering many trivial things one such is watering their plants in the garden regularly as per the requirement of plants for their better and healthy growth. The main idea is to adapt current technology instead of our old system so that the latest Automated system provides sufficient water for farming, home gardening, etc could be achieved. In the current scenario people expect their daily activities to be automated which make their chore convenient and efficient, IoT is one such platform that focuses on human comfort. IoT mainly targets to combine everything in our world under one system or framework. It can be used to manage things and can also keep us updated constantly with current situation. Internet of Things comprises of various devices which can connect to internet and exchange information with one another and sends that information to the user. The proposed system uses Ultra sonic sensor for examining the water level of tank and soil moisture sensor to detect the water content of soil for automated supply of water using Solar power.

Separated Sonar Localization System for Indoor Robot Navigation

This article addresses the task of mobile robot localization for indoor navigation. In this article, we propose a novel indoor localization system based on separated sonar sensors, which can be deployed in large-scale indoor environments conveniently. In our approach, the separated sonar receivers deploy on the top ceiling, and the mobile robot equipped with the separated sonar transmitters navigates in the indoor environment. The distance measurements between the receivers and the transmitters can be obtained in real time from the control board of receivers with the infrared synchronization. The positions of the mobile robot can be computed without accumulative error. And the proposed localization method can achieve high precision in the indoor localization tasks at a very low cost. We also present a calibration method based on the simultaneous localization and mapping to initialize the positions of our system. To evaluate the feasibility and the dynamic accuracy of the proposed system, we construct our localization system in the Virtual Robot Experimentation Platform simulation platform and deploy this system in a real-world environment. Both the simulation and real-world experiments have demonstrated that our system can achieve centimeter-level accuracy. The localization accuracy of the proposed system is sufficient for robot indoor navigation.

Study on Control System of Integrated Unmanned Surface Vehicle and Underwater Vehicle.

In this paper, in order to overcome certain limitations of previously commercialized platforms, a new integrated unmanned surface vehicle (USV) and unmanned underwater vehicle (UUV) platform connected via underwater cable capable of acquiring real-time underwater data and long-time operation are studied. A catamaran-type USV was designed to overcome the limitations of an ocean environment and to play the role as the hub of power supply and communication for the integrated platform. Meanwhile, the UUV was designed as torpedo-shaped to minimize hydrodynamic resistance and its hardware design was focused on processing and sending the underwater camera and sonar data. The underwater cable driven by a winch system was installed to supply power from the USV to the UUV and to transmit acquired data form underwater sonar sensor or camera. Different from other previously studied cooperation system of USVs and autonomous underwater vehicles (AUVs), the merit of the proposed system is real-time motion coordination control between the USV and UUV while transmitting large amount of data using the tether cable. The main focus of the study is coordination of the UUV with respect to the global positioning system (GPS) attached at USV and verification of its performance throughout field tests. Waypoint tracking control algorithm was designed and implemented on USV and relative heading, distance control for USV–UUV coordination was implemented to UUV. To ensure the integrity of the coordination control of the integrated platform, a study on accurate measurement system of the relative position between the USV and the UUV by using the GPS and the ultrashort baseline (USBL) device was performed. Individual tests were conducted to verify the performance of USBL and AHRS, which provide the position and heading data of UUV among the sensors mounted on the actual platform, and the effectiveness of the obtained sensor data is presented. Using the accurate measurement system, a number of field tests were conducted to verify the performance of the integrated platform.

An ADS-Based Sparse Optimization Method for Sonar Imaging Sensor Arrays

The Mills Cross sonar sensor array, achieved by the virtual element technology, is one way to build a low-complexity and low-cost imaging system while not decreasing the imaging quality. This type of sensor array is widely investigated and applied in sensor imaging. However, the Mills Cross array still holds some redundancy in sensor spatial sampling, and it means that this sensor array may be further thinned. For this reason, the Almost Different Sets (ADS) method is proposed to further thin the Mills Cross array. First, the original Mills Cross array is divided into several transversal linear arrays and one longitudinal linear array. Secondly, the Peak Side Lobe Level (PSLL) of each virtual linear array is estimated in advance. After the ADS parameters are matched according to the thinned ratio of the expectant array, all linear arrays are thinned in order. In the end, the element locations in the thinned linear array are used to determine which elements are kept or discarded from the original Mills array. Simulations demonstrate that the ADS method can be used to thin the Mills array and to further decrease the complexity of the imaging system while retaining beam performance.

Design and Build a Counter System Number of Library Visitors Hang Tuah University, Surabaya

Currently developing from science and technology, especially technology and knowledge in the field of electronics has developed very rapidly. For that we need to learn about the development of science, if not we will be increasingly left behind. Counter people entering the library, the function of this tool is counting everyone who enters the library or who is outside the library. To support the quality and quality of libraries, the system provided for services must be fast and automatic. For example, the system processes data on the number of visitors that have been used. As a follow-up to developing administrative facilities at the Hang Tuah University library specialized institutions, it requires adequate visitor data processing. To find out how many visitors are active in the library each visit, without having to look in the visitor's absence book. Then it will be easy to determine better management of the library management. then the need for tools that can count visitors who enter the library automatically. Overall this system will be composed by the arduino uno microcontroller system, with an ultrasonic sensor as a visitor detection device. From the results of each component's experiment, the HC-SR04 ultra sonic sensor system was obtained to run up to a distance of 50 cm. The bell can ring properly every time a sensor moves a moving object. The experimental system as a whole is produced from a system that can run well at the ideal distance of the ultrasonic reading sensor between 3 - 50 cm. By using 2 ultrasonic sensors which are placed on two sides (in and out), the microcontroller-based system can work well when reading incoming and outgoing visitors which are then displayed to the screen. With this system, it is possible to develop a library in terms of visitor data collection.q

Passive Underwater Target Tracking: Conditionally Minimax Nonlinear Filtering with Bearing-Doppler Observations.

The paper presents an application of the Conditionally-Minimax Nonlinear Filtering (CMNF) algorithm to the online estimation of underwater vehicle movement given a combination of sonar and Doppler discrete-time noisy sensor observations. The proposed filter postulates recurrent “prediction–correction” form with some predefined basic prediction and correction terms, and then they are optimally fused. The CMNF estimates have the following advantageous features. First, the obtained estimates are unbiased. Second, the theoretical covariance matrix of CMNF errors meets the real values. Third, the CMNF algorithm gives a possibility to choose the preliminary observation transform, basic prediction, and correction functions in any specific case of the observation system to improve the estimate accuracy significantly. All the features of conditionally-minimax estimates are demonstrated by the regression example of random position estimate given the noisy bearing observations. The contribution of the paper is the numerical study of the CMNF algorithm applied to the underwater target tracking given bearing-only and bearing-Doppler observations.

A decision-theoretic approach to acquire environmental information for improved subsea search performance

This study addresses subsea search applications where an autonomous underwater vehicle (AUV) is tasked with finding the target density in a given search region within finite time. We assume that AUV is equipped with a side-scan sonar sensor that detects the targets at the sampled location. We consider that sensor performance is dependent on local environmental conditions (e.g., clutter density, sediment type) that vary throughout the search region, and we presume that environmental conditions are unknown or partially known. Due to uncertain and varying environmental conditions, resulting search performance is also uncertain and it varies by location. This paper specifically considers the cases where environmental information can be acquired either by a separate vehicle or by the same vehicle that performs the search task. Our main contribution is to formally derive a decision-theoretic cost function to compute the locations where the environmental information should be acquired so that the performance of the search task can be improved. For the cases where computing the optimal locations to sample the environment is computationally expensive, we offer an approximation approach that yields provable near-optimal paths. We show that our decision-theoretic cost function outperforms the information-maximization approach, which is often employed in similar applications.

Asisten Navigasi untuk Orang dengan Gangguan Penglihatan Menggunakan Ultra Sonic (Sonar Vision) Untuk Yayasan Mitra Netra

Generally, people with visual impairments in the low vision category use sticks to find ways to deal with obstacles in front of them. However, this detection can only be felt as long as the stick is used. The purpose of this community service activity is to made a sonar vision set using a sonar sensor that can be attached to the wrist and belt. This device is designed to detect obstacles up to a distance of 3 meters. This activity was carried out at the Mitra Netra Foundation. The approach used is the creation, provision and education of the use of the Sonar Vision module for people with visual impairments. The results of this activity show that 100 low vision sufferers have benefited from this system, where 84% of them feel that sonar vision helps them in navigation.

A hybrid legged-wheeled obstacle avoidance strategy for service operations

Hybrid legged-wheeled robots are gaining interest in various service applications, like surveillance or inspection in hospitals. The autonomy of these robots is not only related to their power consumption, it mostly refers to their capability to safely move in complex partially structured environments. This paper proposes to investigate the combination of different moving strategies and sensors to enhance the adaptability and autonomy of a hybrid hexapod robot in specific environments shared with humans. Namely, this paper proposes a locomotion strategy that combines leg motions and Mecanum omniwheels with multiple sensory feedbacks to achieve safe obstacle avoidance during a service operation. Several experimental tests are carried out by using Cassino Hexapod III in combination with sonar, IMU and Lidar sensors at IRCCS Neuromed site in Pozzilli. Experimental results show the effectiveness of the proposed operation strategy with Cassino Hexapod III to avoid multiple obstacles.

Navigation assistant for vision impaired people using ultra sonic (sonar vision) and global positioning system (GPS)

People with low vision usually use stick to find their way or to face an obstacle in front of them. However, this detection only can be felt as long as the length of the stick from its handle is as far as the reach of the hand. Seeing this limitation, a set of sonar vision was made using sonar sensor, which equipped as a wristwatch and a buckle of a belt. The sonar vision was designed to detect an obstacle for up to 3 meters away. A hundred low vision people already got the benefit of this system, which 84% of them felt that the sonar vision helped them in the navigation. The new design based on the feedback is presented in this paper, which is equipped with GPS for positioning information.

Android Based Voice Activated Wheel Chair for Disabled

The aim of the project is to develop a wheel chair which can be controlled by voice of the person. It is based on the speech recognition model. The project is focused on controlling the wheel chair by human voice. The system is intended to control a wheel seat by utilizing the voice of individual. The structure of this framework will be particularly valuable to the crippled individual and furthermore to the older individuals. It is a booming technology which interfaces human with machine. Smart phone device is the interface. This will allow the challenging people to move freely without the assistant of others. They will get a moral support to live independently .The hardware used are Arduino kit, Microcontroller, Wheelchair and DC motors. DC motor helps for the movement of wheel chair. Ultra Sonic Sensor senses the obstacles between wheelchair and its way.

Android Based Voice Activated Wheel Chair for Disabled

The aim of the project is to develop a wheel chair which can be controlled by voice of the person. It is based on the speech recognition model. The project is focused on controlling the wheel chair by human voice. The system is intended to control a wheel seat by utilizing the voice of individual. The structure of this framework will be particularly valuable to the crippled individual and furthermore to the older individuals. It is a booming technology which interfaces human with machine. Smart phone device is the interface. This will allow the challenging people to move freely without the assistant of others. They will get a moral support to live independently .The hardware used are Arduino kit, Microcontroller, Wheelchair and DC motors. DC motor helps for the movement of wheel chair. Ultra Sonic Sensor senses the obstacles between wheelchair and its way.

An Optimized Spatial Sampling Strategy for Wide-View Planar Array 3-D Sonar Sensors.

Array-based imaging sonar sensors often have the advantage that the sampling directions in which sensor data are processed can be arbitrarily chosen. The associated computational cost of the imaging algorithm scales linearly with the number of sampled directions, though, which may limit the use of such sensors for real-time applications such as robotics. This is especially true in the case of wide-field sensors, which generally require a larger number of sampled directions (e.g., to cover the full frontal hemisphere). For this reason, it is necessary to optimize the set of sampling directions and reduce its number to a minimum. In this article, we propose a spatial sampling strategy for array-based sonar sensors, which considers the point-spread function of the array sensor to obtain such a minimal set. We apply this strategy to optimize the set of sampling directions for a specific planar array sonar sensor and compare the efficiency of the resulting sampling grid with two commonly used sampling strategies.

Bioinspired sonar reflectors as guiding beacons for autonomous navigation

Sonar sensors are universally applied in autonomous vehicles such as robots and driverless cars as they are inexpensive, energy-efficient, and provide accurate range measurements; however, they have some limitations. Their measurements can lead to ambiguous estimates and echo clutter can hamper target detection. In nature, echolocating bats experience similar problems when searching for food, especially if their food source is close to vegetation, as is the case for gleaning bats and nectar-feeding bats. However, nature has come up with solutions to overcome clutter problems and acoustically guide bats. Several bat-pollinated plants have evolved specially shaped floral parts that act as sonar reflectors, making the plants acoustically conspicuous. Here we show that artificial sonar beacons inspired by floral shapes streamline the navigation efficacy of sonar-guided robot systems. We developed floral-inspired reflector forms and demonstrate their functionality in 2 proof-of-principle experiments. First we show that the reflectors are easily recognized among dense clutter, and second we show that it is possible to discern different reflector shapes and use this identification to guide a robot through an unfamiliar environment. Bioinspired sonar reflectors could have a wide range of applications that could significantly advance sonar-guided systems.

Survey on Deep Learning Techniques Used for Classification of Underwater Sonar Images

Object detection methods have been evolving as deep learning gives the major support for the classification of the dataset irrespective of the domain. In this paper, underwater sonar image target detection methods are reviewed using deep learning comparatively. Types of sonar-like side-scan sonar, forward-looking sonar, synthetic aperture sonar provide higher resolution images than optical images in low illuminated water. Recently research on underwater exploration using Automated underwater vehicles (AUV) mounted with sonar sensors are evolving to support long searches to find objects of interest. The object detection can help us classify mines, seafloor, ocean habitats, shipwrecks, and finding drowning victims. This paper specifies a survey on state-of-the-art deep learning techniques used for underwater object detection from Sonar images. The study shows that deep learning techniques using transfer learning in real datasets as well as in semisynthetic dataset helps improve the efficiency of object detection.

End‐to‐end cubic phase signal recovery method based on deep convolutional neural network

Cubic phase (CP) signal is widely used in radar and sonar signal processing and the CP signal received by the radar or sonar sensors may miss part of discrete samples and be corrupted by noise. In this study, a novel end-to-end CP signal recovery method is proposed to recover the CP signal that experiences data missing and noise corruption based on deep convolutional neural network (DCNN). Two key techniques are used in the structure design of the proposed network. The first is dilated convolution, which has a larger receptive field than the standard convolution operation and can extract information more efficiently in the CP signal recovery problem. The second is denseNet, which connects all layers directly and can alleviate the vanishing-gradient problem. Experiment results show that the proposed DCNN-based method achieves superior performance to the traditional compressed sensing-based method both from the perspective of recovery error and time consumption.

Building semantic grid maps for domestic robot navigation

This article proposes a semantic grid mapping method for domestic robot navigation. Occupancy grid maps are sufficient for mobile robots to complete point-to-point navigation tasks in 2-D small-scale environments. However, when used in the real domestic scene, grid maps are lack of semantic information for end users to specify navigation tasks conveniently. Semantic grid maps, enhancing the occupancy grid map with the semantics of objects and rooms, endowing the robots with the capacity of robust navigation skills and human-friendly operation modes, are thus proposed to overcome this limitation. In our method, an object semantic grid map is built with low-cost sonar and binocular stereovision sensors by correctly fusing the occupancy grid map and object point clouds. Topological spaces of each object are defined to make robots autonomously select navigation destinations. Based on the domestic common sense of the relationship between rooms and objects, topological segmentation is used to get room semantics. Our method is evaluated in a real homelike environment, and the results show that the generated map is at a satisfactory precision and feasible for a domestic mobile robot to complete navigation tasks commanded in natural language with a high success rate.

Oil Plume Mapping: Adaptive Tracking and Adaptive Sampling From an Autonomous Underwater Vehicle

We have developed an adaptive sampling algorithm for an Explorer autonomous underwater vehicle (AUV) to conduct in-situ analysis of acoustic measurements to perform autonomous oil plume detection and tracking. The methodology of the tracking phase involves ongoing analysis of the detected plume, assessing target validity and proximity for AUV decision-making for plume mapping. We previously introduced the bumblebee flight path, a new biomimetic search pattern designed to maximize the spatial coverage in the oil plume detection phase. This paper focuses on a new tracking strategy as the key adaptive stage in our plume delineation. For initial development we used a 360-degree scanning sonar sensor model. Simulations were done with different plume models to assess the performance of the developed adaptive sampling algorithm. A convergence study demonstrated that the algorithm could successfully track the boundary of a non-regular shaped/patchy oil plume at up to a 0.05Hz sampling frequency. A sensitivity study identified the correlations between plume feature complexity and the anticipated range of acoustic measurement update delays. The decision-making architecture consists of three separate components which implements either proximity or boundary following control and contributes to the final decision on the next desired heading of the vehicle. A weight ratio, that determined the relative allocation of each component, was varied to study its impact on the tracking performance of the AUV. The novelty of our approach is in addressing the discontinuous and patchy nature of realistic oil plumes. Our sampling algorithm and its performance in simulations is a significant step beyond the practical limitations of existing gradient-following methods because it accounts for the oil patches and droplets which gradient-following algorithms do not.

High-Resolution Ultrasound Sensing for Robotics Using Dense Microphone Arrays

State-of-the-art autonomous vehicles use all kinds of sensors based on light, such as a camera or LIDAR (Laser Imaging Detection And Ranging). These sensors tend to fail when exposed to airborne particles. Ultrasonic sensors have the ability to work in these environments since they have longer wavelengths and are based on acoustics, making them able to pass through the mentioned distortions. However, they have a lower angular resolution compared to their optical counterparts. In this article a 3D in-air sonar sensor is simulated, consisting of a Uniform Rectangular Array similar to the newly developed micro Real Time Imaging Sonar ( $\mu $ RTIS) by CoSys-Lab. Different direction of arrival techniques will be compared for an 8 by 8 uniform rectangular microphone array in a simulation environment to investigate the influence of different parameters in a completely controlled environment. We will investigate the influence of the signal-to-noise ratio and number of snapshots to the angular and spatial resolution in the direction parallel and perpendicular to the direction of the emitted signal, respectively called the angular and range resolution. We will compare these results with real-life imaging results of the $\mu $ RTIS. The results presented in this article show that, despite the fact that in-air sonar applications are limited to only one snapshot, more advanced algorithms than Delay-And-Sum beamforming are viable options, which is confirmed with the real-life data captured by the $\mu $ RTIS.

Improvements to Terrain Aided Navigation Accuracy in Deep-Sea Space by High Precision Particle Filter initialization

AUV (autonomous underwater vehicles) are required to have long-term and high-precision positioning capability relative to seabed targets in most deep-sea exploration tasks. However, acoustic positioning error is positively correlated with its operating range and inertial navigation has inevitable accumulated time errors, neither of which provide precise AUV positions. TAN (terrain aided navigation) directly calculates the AUV position to the seabed terrain coordinate system by tracking the seabed topographic characteristics, which can guide the AUV to seabed target accurately. However, the initial TAN positioning error will increase with the AUV operation depth, which causes a large PF (particle filter) initialization error and particle coverage interval, and will affect the convergence and stability of TAN. To solve this problem, we first propose a TAP (terrain aided position) confidence interval model. We then use the confidence interval to constrain the initial particles to a smaller range. Finally, the validity of the algorithm is verified by playback simulation with ship-borne multi-beam sonar sensor measured data. The results show that the TAP confidence interval can reduce the coverage of the initial particle, and can improve the convergence speed and filtering accuracy of the TAN.