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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.

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.

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.

This proposed RTT technique maybe implementing for SLAM, reliable autonomous navigation, optimal path planning, efficient robotics vision, consistent multi-robotic system, etc.

An efficient perception of the complex environment is the foremost requirement in mobile robotics. At present, the utilization of glass as a glass wall and automated transparent door in the modern building has become a highlight feature for interior decoration, which has resulted in the wrong perception of the environment by various range sensors. The perception generated by multi-data sensor fusion (MDSF) of sonar and laser is fairly consistent to detect glass but is still affected by the issues such as sensor inaccuracies, sensor reliability, scan mismatching due to glass, sensor model, probabilistic approaches for sensor fusion, sensor registration, etc. The paper aims to discuss these issues.

This paper presents a modified framework – Advanced Laser and Sonar Framework (ALSF) – to fuse the sensory information of a laser scanner and sonar to reduce the uncertainty caused by glass in an environment by selecting the optimal range information corresponding to a selected threshold value. In the proposed approach, the conventional sonar sensor model is also modified to reduce the wrong perception in sonar as an outcome of the diverse range measurement. The laser scan matching algorithm is also modified by taking out the small cluster of laser point (w.r.t. range information) to get efficient perception.

The probability of the occupied cells w.r.t. the modified sonar sensor model becomes consistent corresponding to diverse sonar range measurement. The scan matching technique is also modified to reduce the uncertainty caused by glass and high computational load for the efficient and fast pose estimation of the laser sensor/mobile robot to generate robust mapping. These stated modifications are linked with the proposed ALSF technique to reduce the uncertainty caused by glass, inconsistent probabilities and high load computation during the generation of occupancy grid mapping with MDSF. Various real-world experiments are performed with the implementation of the proposed approach on a mobile robot fitted with laser and sonar, and the obtained results are qualitatively and quantitatively compared with conventional approaches.

The proposed ASIF approach generates efficient perception of the complex environment contains glass and can be implemented for various robotics applications.

A new approach to rejecting interference between sonar systems is presented in this paper. The approach is based on identifying a transmitter by sending a double pulse with known separation. Using simple delayed pulse subtraction, a sonar receiver can test echo pulses quickly and simply for acceptance. This approach is implemented on a sonar system that is capable of accurately tracking objects at measurement rates exceeding 10 Hz. Two sonar sensors, each consisting of a transmitter and two receivers, are independently controlled to track objects from bearing and range measurements. Matched filtering is used to optimally estimate echo arrival times from which range and bearing angle are derived. Bearing accuracy is typically better than 0.1 degrees but degrades if there is air turbulence and temperature gradients. Bearing errors are shown experimentally to have significant autocorrelation at times of the order of seconds. The ability to reliably reject interference is demonstrated experimentally by both sensors simultaneously tracking the same plane object.

An autonomous mobile robot requires efficient perception of the environment to perform various tasks in a challenging environment. The precise sensory information from the range sensors is required to accomplish prerequisites, such as SLAM, path planning and localization. But the accuracy and precision of the sensors become unreliable in harsh environmental conditions because of the effect of rain, dust, humidity, fog and smoke. The purpose of this paper is to generate robust mapping of the environment in harsh environmental conditions.

This paper presents a novel technique, averaging data with short range selection (ADWSRS), to reduce the effect of harsh environmental (rain, wind, humidity, etc.) conditions on sensory information (range) to generate reliable grid mapping. The sensory information on laser and sonar sensors in terms of probability values (occupied/unoccupied cell) in generating grid maps are fused after passing through two newly designed pre-processing filters: laser averaging filter and short range selection filter. This proposed approach relies on various aspects such as averaging laser data analogous to current pose of the sensor, selection of short range with respect to threshold value to remove the effect of specular reflection/crosstalk of sonar and a newly designed apparatus in which dirt cover (glass cover) and air blower are coupled to remove the influence of dirt, rain and humidity.

This proposed approach is tested in different environmental conditions, and to verify the consistency of the proposed approach, qualitative and quantitative analyses are carried out, which shows 42.5 per cent improvement in the probability value of occupied cells in the generated grid map.

The proposed ADWSRS approach reduced the effect of harsh environmental conditions such as fog, rain and smoke to generate efficient mapping of the environment, which may be implemented in diverse applications such as autonomous navigation, localization, path planning and mapping.

While it is true that some wordprocessors have the capability to produce their own indices, Sonar Bookends offers the advantage that users do not have to mark words and phrases in the source document in order to produce an index. Sonar Bookends can generate a simple index using words as they appear in the document to be indexed. Indexes can also be built from a word/phrase list supplied by the user in the form of an ASCII file. In addition, proper noun indexes can be built by Sonar Bookends, based on rules of capitalization and punctuation. The package can mix and match any of these types of indices to produce a custom‐designed index.

Designing a compact navigation sonar system for remote controlled underwater vehicles can be a real challenge.

The purpose of this paper is to develop a novel MEMS vector hydrophone with the key features of smaller size, better consistency, higher sensitivity and directional reception, and to develop a highly effective and economical obstacle avoidance sonar system. Currently, the typical vector hydrophones are resonant vector hydrophones based on the accelerometer, which greatly increases the volume and constrains the detection sensitivity. Also, because the system is composed of a number of devices, its size is difficult to be reduced.

A novel double T-shape MEMS vector hydrophone is proposed with a fish’s lateral line organs as prototypes. The structure size and layout location of the piezoresistors were determined by simulation analysis, and the double T-shape microstructure was fabricated integrally by MEMS manufacturing technology, after which, the acoustic package of the microstructure was completed and the prototype was produced. Finally, the packaged hydrophone was calibrated in a standing wave field in the first-class national-defense underwater acoustic calibration station of China. Also, the design and test of an obstacle avoidance sonar system based on the vector hydrophone were completed.

The calibration data show that the double T-shape vector hydrophone has a flat frequency response curve, exhibits a sensitivity of −180 dB (1 kHz, 0 dB reference 1 V/uPa) and shows a good directivity pattern in the form of an “8” shape. The test results of the obstacle avoidance sonar system further verify the feasibility of detecting underwater acoustic signals.

The next work is to increase the sensitivity by optimizing the microstructure and to realize orientation by organizing array.

The hydrophone has the advantages of smaller size, lower cost and directional reception. It can be used to develop highly effective and economical obstacle avoidance sonar system, thus solving the problems of water transport efficiency and traffic safety. The hydrophone has broad application prospects and a huge market potential in the civilian fields.

The MEMS technology and innovative bionic microstructure enable the miniaturization and low cost of the hydrophone. The hydrophone is easy to form array and can narrow the array aperture greatly. So, the hydrophone can be widely used in civil sonar systems.

The purpose of this study is to develop a new positioning method for remotely operated vehicle (ROV) in the nuclear power plant. The ROV of the nuclear power plant is developed to inspect the reactor cavity pools, the component pools and spent-fuel storage pools. To enhance the operational safety, the ability of localizing the ROV is indispensable.

Therefore, the positioning method is proposed based on the MEMS inertial measurement unit and mechanical scanning sonar in this paper. Firstly, the ROV model and on board sensors are introduced in detail. Then the sensor-based Kalman filter is deduced for attitude estimation. After that, the positioning method is proposed that divided into static positioning and dynamic positioning. The improved iterative closest point-Kalman filter is deduced to estimate the global position by the whole circle scanning sonar data in static, and the relative positioning method is proposed by the small scale scanning sonar data in dynamic.

The performance of the proposed method is verified by comparing with the visual positioning system. Finally, the effectiveness of the proposed method is proved by the experiment in the reactor simulation pool of the Daya Bay Nuclear Power Plant.

The research content of this manuscript is aimed at the specific application needs of nuclear power plants and has high theoretical significance and application value.

The main purpose of this paper is to investigate two key elements of localization and mapping of Autonomous Underwater Vehicle (AUV), i.e. to overview various sensors and algorithms used for underwater localization and mapping, and to make suggestions for future research.

The authors first review various sensors and algorithms used for AUVs in the terms of basic working principle, characters, their advantages and disadvantages. The statistical analysis is carried out by studying 35 AUV platforms according to the application circumstances of sensors and algorithms.

As real‐world applications have different requirements and specifications, it is necessary to select the most appropriate one by balancing various factors such as accuracy, cost, size, etc. Although highly accurate localization and mapping in an underwater environment is very difficult, more and more accurate and robust navigation solutions will be achieved with the development of both sensors and algorithms.

This paper provides an overview of the state of art underwater localisation and mapping algorithms and systems. No experiments are conducted for verification.

The paper will give readers a clear guideline to find suitable underwater localisation and mapping algorithms and systems for their practical applications in hand.

There is a wide range of audiences who will benefit from reading this comprehensive survey of autonomous localisation and mapping of UAVs.

The paper will provide useful information and suggestions to research students, engineers and scientists who work in the field of autonomous underwater vehicles.

The purpose of this research is to provide the necessarily and resourceful information regarding range sensors to select the best fit sensor for robust autonomous navigation. Autonomous navigation is an emerging segment in the field of mobile robot in which the mobile robot navigates in the environment with high level of autonomy by lacking human interactions. Sensor-based perception is a prevailing aspect in the autonomous navigation of mobile robot along with localization and path planning. Various range sensors are used to get the efficient perception of the environment, but selecting the best-fit sensor to solve the navigation problem is still a vital assignment.

Autonomous navigation relies on the sensory information of various sensors, and each sensor relies on various operational parameters/characteristic for the reliable functioning. A simple strategy shown in this proposed study to select the best-fit sensor based on various parameters such as environment, 2 D/3D navigation, accuracy, speed, environmental conditions, etc. for the reliable autonomous navigation of a mobile robot.

This paper provides a comparative analysis for the diverse range sensors used in mobile robotics with respect to various aspects such as accuracy, computational load, 2D/3D navigation, environmental conditions, etc. to opt the best-fit sensors for achieving robust navigation of autonomous mobile robot.

This paper provides a straightforward platform for the researchers to select the best range sensor for the diverse robotics application.