Analytical Approach to Sampling Estimation of Underwater Tunnels Using Mechanical Profiling Sonars

Hydroelectric power plants often make use of tunnels to redirect the flow of water to the plant power house. Such tunnels are often flooded and can span considerable distances. Periodical inspections of such tunnels are highly desirable since a tunnel collapse will be catastrophic, disrupting the power plant operation. In many cases, the use of Unmanned Underwater Vehicles (UUVs) equipped with mechanical profiling sonars is a suitable and affordable way to gather data to generate 3D mapping of flooded tunnels. In this paper, we study the resolution of 3D tunnel maps generated by one or more mechanical profiling sonars working in tandem, considering synchronization and occlusion problems. The article derives the analytical equations to estimate the sampling of the underwater tunnels using mechanical profiling sonars (scanning sonars). Experiments in a simulated environment using up to four sensors simultaneously are presented. We also report experimental results obtained by a UUV inside a large power plant tunnel, together with a first map of this environment using a single sonar sensor.

Removal of sonar wave interference in multi-robot system for the efficient SLAM by randomized triggering time technique

Purpose Sonar sensor-based mobile robot mapping is an efficient and low cost technique for the application such as localization, autonomous navigation, SLAM and path planning. In multi-robots system, numbers of sonar sensors are used and the sound waves from sonar are interacting with the sound wave of other sonar causes wave interference. Because of wave interference, the generated sonar grid maps get distorted which resulted in decreasing the reliability of mobile robot’s navigation in the generated grid maps. This research study focus in removing the effect of wave interfaces in the sonar mapping to achieve robust navigation of mobile robot. Design/methodology/approach The wrong perception (occupancy grid map) of the environment due to cross talk/wave interference is eliminated by randomized the triggering time of sonar by varying the delay/sleep time of each sonar sensor. A software-based approach randomized triggering technique (RTT) is design in laboratory virtual instrument engineering workbench (LabVIEW) that randomized the triggering time of the sonar sensor to eliminate the effect of wave interference/cross talk when multiple sonar are placed in face-forward directions. Findings To check the reliability of the RTT technique, various real-world experiments are perform and it is experimentally obtained that 64.8% improvement in terms of probabilities in the generated occupancy grid map has been attained when compared with the conventional approaches. Originality/value This proposed RTT technique maybe implementing for SLAM, reliable autonomous navigation, optimal path planning, efficient robotics vision, consistent multi-robotic system, etc.

Remotely Operated Underwater Vehicle with Robotic Gripper

This paper has been endeavoring to enforce modern technique in the field of electronics. It is focused on a 6Degree of Freedom (DOF) and an under actuated system. For the underwater controlling and modeling of the vehicle, it has used an Internet Protocol (IP) camera for underwater surveillance. The most important task which the project will do is that it has an image processing feature that will enable the vehicle to find and detect lost or floating objects and picked it out through robotic arm. The Sonar sensor shows the depth of the vehicle and the distance from objects and the barometer shows the surrounding pressure, temperature and altitude of the vehicle. The real time controlling is carried out with the prototype and the results shows that the system is fully capable of accomplishing tasks such as wireless communication, object detection, object recovery and can detect major environmental changes such as pressure, temperature, obstacle avoidance and other real time information processing effectively and efficiently. This report has been so devised to be of special value to research organizations. It introduces a model for simulation and control purpose. The Remotely Operated Underwater Vehicles (ROVs) are remote control underwater robots, driven by an individual on the surface. The whole system model is discussed and finally the results are conveyed.

An Automated Obstacle Detector and Path Finder Robotic Car

This is to present you a simple and cost-efficient obstacle detecting mobile robot. Here a controlled rotating sonar sensor has been used to measure distance. With this robot the angular and distance values are being sampled with the system support and being simplified to get a correct way through the given algorithm. The system was implemented in C++ type Arduino coding Software. Inputting the data and processing it in Arduino; all were digitally maintained; the digital pins of Arduino were used. And the outputs were controlled by the Arduino which were pre-given. This simple, cost efficient and mostly accurate project can be used in farming as well as defense and security sector of any country.

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.

Study on the Classification Performance of Underwater Sonar Image Classification Based on Convolutional Neural Networks for Detecting a Submerged Human Body

Auto-detecting a submerged human body underwater is very challenging with the absolute necessity to a diver or a submersible. For the vision sensor, the water turbidity and limited light condition make it difficult to take clear images. For this reason, sonar sensors are mainly utilized in water. However, even though a sonar sensor can give a plausible underwater image within this limitation, the sonar image’s quality varies greatly depending on the background of the target. The readability of the sonar image is very different according to the target distance from the underwater floor or the incidence angle of the sonar sensor to the floor. The target background must be very considerable because it causes scattered and polarization noise in the sonar image. To successfully classify the sonar image with these noises, we adopted a Convolutional Neural Network (CNN) such as AlexNet and GoogleNet. In preparing the training data for this model, the data augmentation on scattering and polarization were implemented to improve the classification accuracy from the original sonar image. It could be practical to classify sonar images undersea even by training sonar images only from the simple testbed experiments. Experimental validation was performed using three different datasets of underwater sonar images from a submerged body of a dummy, resulting in a final average classification accuracy of 91.6% using GoogleNet.