Sonar Sensor Model Research Articles

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.

Degeneracy-Aware Imaging Sonar Simultaneous Localization and Mapping

High-frequency imaging sonar sensors have recently been applied to aid underwater vehicle localization, by providing frame-to-frame odometry measurements or loop closures over large timescales. Previous methods have often assumed a planar environment, thereby restricting the use of such algorithms mostly to seafloor mapping. We propose an algorithm to generate pose-to-pose constraints for pairs of sonar images, which may also be applied to larger sets of images, that makes no assumptions about the environmental geometry. The algorithm is sensitive to the inherent degeneracies of the imaging sonar sensor model, and may be tuned to tradeoff between providing more constraints on the sensor motion and not overfitting to noise in the measurements. For real-time localization, we fuse the resulting pair-wise sonar pose constraints with vehicle odometry in a pose graph optimization framework. We rigorously evaluate the proposed method and demonstrate improvement in accuracy over previously proposed formulations both in simulation and real-world experiments.

Multi-data sensor fusion framework to detect transparent object for the efficient mobile robot mapping

PurposeAn 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.Design/methodology/approachThis 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.FindingsThe 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.Originality/valueThe proposed ASIF approach generates efficient perception of the complex environment contains glass and can be implemented for various robotics applications.

Sonar Sensor Model for the Precision Measurement to Generate Robust Occupancy Grid Map

Precise measurement of the parameters such as position and angular coordinates, distance travelled, and velocity and acceleration of an autonomous mobile robot in occupancy grid map is an active area of research. The stated parameters rely on the probability values stored in the grid cells of the occupancy grid. Occupancy grid mapping is a proficient paradigm in the field of mobile robotics for modeling the environment by receiving the sensory information from range sensors. The objective of this study is to develop novel approach—adaptive sonar model (ASM)—for the generation of robust occupancy grid by reducing the effect of inconsistency in the grid cells probabilities of the occupancy as a consequence of long-range measurement. The results acquired with the implementation of the ASM techniques are compared with the conventional sonar sensor model which reveals 20% reduction in the uncertainty in the occupancy grid map generated due to long-range measurement.

Removal of Specular Reflection and Cross Talk in Sonar for Precise and Accurate Range Measurements

Sonar sensor based range measurement for mobile robot mapping/perception of the complex environment is considered as proficient and low cost technique. Sonar sensor are employ for various applications such as autonomous navigation of mobile robot, underwater navigation and mapping, perception of shape and size of objects, etc. Different configurations are used in the past to mount the sonar sensors on the front/back/side of the mobile robots for the accurate and precise perception of the environment. The fast, precise and consistent sonar occupancy grid mapping is the key requirement for the various mobile robot applications which results in the use of multiple sonar sensors in the form of convex shape sonar rings mounted on mobile robot. Sonar rings provide fast mapping of the environment but, at the cost of low reliability, less precision and inconsistency in the occupancy grid due to specular reflection, false triggering synchronization and cross talk. This paper presents a novel technique for the construction of concave shape sonar rings called C-type arrangement (CTA) of sonar sensors which is derived from the analytical and mathematical study of convex type sonar sensor rings, circle geometry, trigonometry and Sonar sensor model. CTA approach is designed with an objective of examine metrological characteristic of multi sonar rings such as uncertainty, precision and error in range measurement. CTA technique reduce the uncertainties in sonar occupancy grid mapping due to specular reflection and cross talk for the application such as autonomous navigation, path planning, mapping, localization, etc. The proposed technique is experimentally verified in various real world experiments and enhanced results are analyzed both quantitatively and qualitatively that validate the reliability of the CTA technique. The CTA technique enhance the reliability in sonar sensor range measurement for the various robotics task like space exploration, mining, search and rescue operation, underwater surveillance etc.

Atmosphere effects on sonar sensor model for UAS applications

The use of Unmanned Aerial System (UAS) applications is growing in both civilian and military fields. Fully autonomous UAS applications are useful to a variety of disciplines; for example, they can provide structural damage inspections in critical or dangerous areas (e.g. the L'Aquila earthquake in Italy, or Hurricane Ike in Galveston, Texas, USA). However, in some conditions, such as urban or low altitude operations, the Global Navigation Satellite System (GNSS) receiver antenna is prone to losing the line-of-sight from the satellite, making GNSS unable to deliver high quality positon information. This is quite dangerous for closed-loop control systems during the landing phase. In these cases, an ultrasonic sensor may be very useful for altitude control during the vehicle landing.

Development of sonar morphology-based posterior approach model for occupancy grid mapping

SUMMARYAn advanced sonar morphology-based posterior approach (SMP) to building an occupancy grid map for a mobile robot is proposed in this study. It is very important for a mobile robot to find its surrounding saliencies and to localize itself for indoor navigation. Ultrasonic sensors are of great practical value in building environmental maps and for autonomous operation. However, grid maps constructed by ultrasonic sensors cannot typically form a realistic representation of a given environment due to incorrect sonar measurements caused by specular reflection and wide beam width. The sonar sensor model proposed in this study, in which the negative effect of incorrect sonar measurements is minimized by geometric association with sonar footprints, is adopted to build a high-quality grid map. Experimental results and evaluations in home and corridor environments demonstrate the validity of the proposed methods.

Development of advanced sonar sensor model using data reliability and map evaluation method for grid map building

This paper proposes a novel approach for building the occupancy grid map using sonar data. It is very important for a mobile robot to recognize and construct its surrounding environment for navigational purposes. However, the grid map constructed using ultrasonic sensors cannot represent the shape of the given environment realistically due to incorrect sonar measurements incurred by specular reflections. For building a high-quality grid map, we introduce the advanced sonar sensor model in which the negative effect of incorrect sonar measurements is minimized by associating geometrically with sonar data. In addition, the evaluating method of the grid map with respect to the reference map was developed. The experimental results demonstrate the validity of the proposed methods.

Side Scan Sonar Modeling for Maritime Robotic Operations

One of the key aspects in designing an Autonomous Underwater Vehicle (AUV) simulation framework is sensor modeling. This paper presents specifically the underwater sonar sensor modeling structure used in the proposed AUV simulation framework. This sensor model covers the mathematical aspects from the field of acoustics which mimics real world sensors. Simplified sonar signal models are widely used however rarely discussed in the literature. Based on this designed simulation framework, simple scenario using different sonar configuration is shown and discussed. This paper shows the formulation of a typical side-scan sonar with emphasis on the assumptions which leads to the simplification of the sonar model. The sonar sensor model is built based on a developed AUV test-bed which was done previously in the University of Adelaide.

초음파 데이터의 신뢰도 모델 기반 지도 작성

This paper proposes a novel approach to building an occupancy grid map using sonar data. It is very important for a mobile robot to recognize and construct its surrounding environments for navigation. However, the grid map constructed by ultrasonic sensors cannot represent a realistic shape of given environments due to incorrect sonar measurements caused by specular reflection. To overcome this problem, we propose an advanced sonar sensor model which consists of distance and shape factors used to determine the reliability of sensor data. Through this sensor model, a robot can build a high-quality grid map. The proposed method was verified by various experiments and showed that the robot could build an accurate map with sonar data in various indoor environments.

이동로봇의 자율주행을 위한 다중센서융합기반의 지도작성 및 위치추정

Presently, the exploration of an unknown environment is an important task for thee new generation of mobile service robots and mobile robots are navigated by means of a number of methods, using navigating systems such as the sonar-sensing system or the visual-sensing system. To fully utilize the strengths of both the sonar and visual sensing systems. This paper presents a technique for localization of a mobile robot using fusion data of multi-ultrasonic sensors and vision system. The mobile robot is designed for operating in a well-structured environment that can be represented by planes, edges, comers and cylinders in the view of structural features. In the case of ultrasonic sensors, these features have the range information in the form of the arc of a circle that is generally named as RCD(Region of Constant Depth). Localization is the continual provision of a knowledge of position which is deduced from it`s a priori position estimation. The environment of a robot is modeled into a two dimensional grid map. we defines a vision-based environment recognition, phisically-based sonar sensor model and employs an extended Kalman filter to estimate position of the robot. The performance and simplicity of the approach is demonstrated with the results produced by sets of experiments using a mobile robot.

A METHOD FOR EXTRACTING LINES AND THEIR UNCERTAINTY FROM ACOUSTIC UNDERWATER IMAGES FOR SLAM

The extraction of reliable features is a key issue for autonomous underwater vehicle navigation. Imaging sonars can produce acoustic images of the surroundings of the vehicle. Despite of the noise, the phantom echoes and reflections, we believe that they are a good source for features since they can work in turbid water, where other sensors like vision fail. Moreover, they can cover wide areas, increasing the number of features visible within a scan. This work presents an algorithm to extract linear features from underwater structured environments including as major contributions a sonar sensor model, an adapted implementation of the Hough transform and a novel procedure to estimate feature uncertainty.

Grid-based localization of a mobile robot using sonar sensors

This paper presents a technique for localization of a mobile robot using sonar sensors. Localization is the continual provision of knowledges of position that are deduced from its a priori position estimation. The environment of a robot is modeled by a two-dimensional grid map. We define a physically based sonar sensor model and employ an extended Kalman filter to estimate positions of the robot. Since the approach does not rely on an exact geometric model of an object, it is very simple and offers sufficient generality such that integration with concurrent mapping and localization can be achieved without major modifications. The performance and simplicity of the approach are demonstrated with the results produced by sets of experiments using a mobile robot equipped with sonar sensors.

Mobile robot relocation from echolocation constraints

This paper presents a method for relocation of a mobile robot using sonar data. The process of determining the pose of a mobile robot with respect to a global reference frame in situations where no a priori estimate of the robot's location is available is cast as a problem of searching for correspondences between measurements and an a priori map of the environment. A physically-based sonar sensor model is used to characterize the geometric constraints provided by echolocation measurements of different types of objects. Individual range returns are used as data features in a constraint-based search to determine the robot's position. A hypothesize and test technique is employed in which positions of the robot are calculated from all possible combinations of two range returns that satisfy the measurement model. The algorithm determines the positions which provide the best match between the range returns and the environment model. The performance of the approach is demonstrated using data from both a single scanning Polaroid sonar and from a ring of Polaroid sonar sensors.