Sonar Readings Research Articles

Monte Carlo Localization for an Autonomous Underwater Vehicle with a Low-Cost Sonar

This paper proposes a Monto Carlo based localization (MCL) algorithm for autonomous underwater vehicle (AUV) with a low-cost mechanical scanning imaging sonar (MSIS). As MSIS has a slow-sampling characteristic, its scan is distorted by the vehicle motion during the scan interval and the sonar readings are sparse. Our contribution is introducing this two-stage approach to overcome the shortages of MSIS to achieve accurate localization: 1) the scan formation module is devised to eliminate the motion induced distortion of sonar scan; 2) MCL is applied to estimate the AUV pose accurately by the Dead Reckoning (DR) result and the formed sonar scan. Results of simulation verify that the proposed algorithm performs well in terms of effectiveness and accuracy.

A computational model of mapping in echolocating bats

Echolocating bats can navigate to salient places relying solely on their sonar system. Currently, much about the mechanism underlying sonar-based navigation in bats remains unknown, and no computational models of this ability have been proposed. In this paper, we propose a computational model of sonar-based navigation in bats. In particular, we advance a model explaining how bats could assemble a cognitive map from their environment using only their sonar readings. The model consists of two loops. The first loop performs low-level obstacle avoidance. This gives rise to stable and environment-derived flight corridors (i.e. preferred pathways for bats flying through the environment). The second high-level loop runs on top of the low-level loop and performs mapping. Mapping is done by combining local view information extracted from echo signals with local self-motion information to recognize previously visited places and memorize their spatial relationships. Using this model, we simulate a bat exploring unstructured environments while constructing a cognitive map using a biologically plausible algorithm. The model we propose allows the simulated bat to construct a global map of its flight paths through the environment without the bat ever reconstructing the three-dimensional layout of the local environment from any of its received echo signals. Indeed, neither the obstacle avoidance strategy that guides the bat through space nor the mapping algorithm requires the three-dimensional geometric structure of the environment to be accessible to the bat.

Modeling frazil ice growth in the St. Lawrence River

The paper presents a complete energy balance model of suspended frazil ice formation in the tidal water column of the St. Lawrence River. The model estimates of suspended frazil ice concentration are compared to in situ observations of an analogue of suspended frazil ice crystals, with good results. A time series of observed acoustic backscattering from suspended frazil serves as the analogue of the suspended frazil concentration. The model of frazil ice growth is used to estimate the rate of increase in mass of the suspended frazil ice by balancing the net rate of energy exchange with the atmosphere with the observed changes in water temperature and in anchor ice thickness. The positive results are achieved despite a number of difficulties with analysis. These difficulties include the bias resulting from the inability of the sonar to detect across the complete size range of suspended ice, the unknown impacts of advection, the unknown impact of anchor ice build-up on temperature and sonar readings, and the lack of knowledge regarding the accuracy of sonar estimation of anchor ice thickness. These results highlight the promise of models, suitably applied, and sonar to quantitatively estimate suspended frazil ice concentration.

PCA-based method to fuse behaviors from place characterization for robot navigation

SUMMARYThis paper presents a method to calculate the fusing rule among three reactive behaviors, Wall Following, Corridor Following and Door Crossing, from place characterization for robot navigation. The technique is supported by a local grid of the closest area to the robot, which is built from sonar readings. The contour of this grid is extracted, represented by itsFFTand, finally, it is reduced to a short feature vector with a principal component analysis (PCA). This feature vector is used to decide the fusing rule among the three behaviors. The algorithm is very fast in terms of its time performance, being then valid to be used in robot navigation, since the robot would rapidly react to new situations. It has also been successfully tested in simulated and real environments, with a Pioneer robot equipped with eight frontal sonar sensors, both in manually driven tasks and autonomous navigation tasks, proving its feasibility and effectiveness.

Enhanced maximum likelihood grid map with reprocessing incorrect sonar measurements

In this paper, we address the problem of building a grid map as accurately as possible using inexpensive and error-prone sonar sensors. In this research area, incorrect sonar measurements, which fail to detect the nearest obstacle in their beamwidth, generally have been dealt with in the same manner as correct measurements or have been excluded from the mapping. In the former case, the map quality may be severely degraded. In the latter case, the resulting map may have insufficient information after the incorrect measurements are removed because only correct measurements are frequently insufficient to cover the whole environment. We propose an efficient grid-mapping approach that incorporates incorrect measurements in a specialized manner to build a better map; we call this the enhanced maximum likelihood (eML) approach. The eML approach fuses the correct and incorrect measurements into a map based on sub-maps generated from each set of measurements. We also propose the maximal sound pressure (mSP) method to detect incorrect sonar readings using the sound pressure of the waves from sonar sensors. In several indoor experiments, integrating the eML approach with the mSP method achieved the best results in terms of map quality among various mapping approaches. We call this the maximum likelihood based on sub-maps (MLS) approach. The MLS map created using only two sonar sensors exhibited similar accuracy to the reference map, which was an accurate representation of the environment.

Virtual force field algorithm for a behaviour-based autonomous robot in unknown environments

The present paper describes a real-time motion-planning approach which lies in the integration of three techniques: fuzzy logic (FL), virtual force field (VFF), and boundary following (BF). The FL algorithm is used for velocity control based on sonar readings. The path-planning algorithm is based on the VFF and BF methods. The proposed navigation system differs from previous works in terms of using different algorithms for planning robot motion. Other improvements concern functional and computational aspects of the design and integration of the modules. The robot shows robust performance in complex situations and local minimum scenarios. Simulation results show the effectiveness of the developed system in various environments with long walls, U-shaped, maze-like, and other types of clutter.

Development of the binocular-vision-enhanced mobile robot navigation

Active map learning for autonomous robots exploring in unknown environments is an essential and vital issue for robot navigation. However, many factors limit a robot's ability to learn accurate map models in practice. To enhance its learning capability, this paper proposed a binocular vision system, developed using IP web cameras and MATLAB image processing toolbox. It integrates knowledge from computer vision and robot navigation. Through the binocular vision system, binocular images are captured and analysed so that feature objects or shapes are identified. Together with sonar readings, this information is used in collision avoidance and map building. It is shown that the capability of active map learning is effectively realised by adopting this binocular vision system.

Simultaneous localization and mapping: swarm robot mutual localization and sonar arc bidirectional carving mapping

This work primarily aims to study robot swarm global mapping in a static indoor environment. Due to the prerequisite estimation of the robots' own poses, it is upgraded to a simultaneous localization and mapping (SLAM) problem. Five techniques are proposed to solve the SLAM problem, including the extended Kalman filter (EKF)-based mutual localization, sonar arc bidirectional carving mapping, grid-oriented correlation, working robot group substitution, and termination rule. The EKF mutual localization algorithm updates the pose estimates of not only the current robot, but also the landmark-functioned robots. The arc-carving mapping algorithm is to increase the azimuth resolution of sonar readings by using their freespace regions to shrink the possible regions. It is further improved in both accuracy and efficiency by the creative ideas of bidirectional carving, grid-orientedly correlated-arc carving, working robot group substitution, and termination rule. Software simulation and hardware experiment have verified the feasibility of the proposed SLAM philosophy when implemented in a typical medium-cluttered office by a team of three robots. Besides the combined effect, individual algorithm components have also been investigated.

Sonar-Based Simultaneous Localization and Mapping Using a Neuro-Evolutionary Optimization

This paper addresses a solution of simultaneous localization and mapping (SLAM) for sonar readings based on neuro-evolutionary optimization algorithm. In the past two decades, numerous studies have attempted to solve the SLAM problem using laser scanners and vision sensors. However, relatively little research has been carried out on a sonar-based SLAM algorithm, because the bearing accuracy and resolution of sonars are not enough to find consistent features for SLAM. The proposed algorithm in this paper solves the sonar-based SLAM as a global optimization problem using the cost function that represents the quality of a robot's trajectory in the world coordinate frame. In our algorithm, a neural network helps to estimate the robot's pose error accurately using sonar inputs at each position and the pose difference between two consecutive robot poses, and evolutionary programming is used to find the most suitable neural network. By way of learning and evolution, our algorithm does not need a prior assumption on the motion and sensor models, and therefore shows a robust performance regardless of the actual noise type. Our neural network-based SLAM algorithm is applied to a robot that has sonar sensors. The various experimental results demonstrate that the neural network-based SLAM guarantees a consistent environmental map under sonar readings that in general are known to have poor bearing accuracy and resolution.

Effective Maximum Likelihood Grid Map With Conflict Evaluation Filter Using Sonar Sensors

In this paper, we address the problem of building a grid map using cheap sonar sensors, i.e., the problem of using erroneous sensors when seeking to model an environment as accurately as possible. We rely on the inconsistency of information among sonar measurements and the sound pressure of the waves from the sonar sensors to develop a new method of detecting incorrect sonar readings, which is called the conflict evaluation with sound pressure (CEsp). To fuse the correct measurements into a map, we start with the maximum likelihood (ML) approach due to its ability to manage the angular uncertainty of sonar sensors. However, since this approach suffers from heavy computational complexity, we convert it to a light logic problem called the maximum approximated likelihood (MAL) approach. Integrating the MAL approach with the CEsp method results in the conflict evaluated maximum approximated likelihood (CEMAL) approach. The CEMAL approach generates a very accurate map that is close to the map that would be built by accurate laser sensors and does not require adjustment of parameters for various environments.

잘못된 초음파 센서 데이터를 여과하기 위한 음압 비교법

In this paper, we address the problem of detecting incorrect sonar measurements. We use ideas from the inconsistency of information among sonar measurements together with the sound pressure of the wave from the sonar sensor to develop a new method, called the comparison of the sound pressure (CSP), to detect incorrect sonar readings. The inconsistency of information in cells can be a clue that indicates candidates for incorrect measurements, and the sound pressure of the wave from the sonar sensor determines incorrect readings among the candidates. From various experiments, the proposed method is confirmed that it is better than existing method at deciding the state of sonar measurements.

Autonomous parallel parking of a car-like mobile robot by a neuro-fuzzy sensor-based controller

In this paper, a neuro-fuzzy model has been developed for autonomous parallel parking of a car-like mobile robot. In our approach we have focused on the most difficult case of parallel parking which is the case when the parking space dimensions cannot be identified. The proposed model uses the data from three sonar sensors mounted in the front left corner of the car to decide on the turning angle. Fifth-order polynomial reference paths for three different size parking dimensions have been used to generate the training data. The fuzzy model has been identified by subtractive clustering algorithm and trained by ANFIS. The simulation results show that the model can successfully decide about the motion direction at each sampling time without knowing the parking space width, based on the direct sonar readings which serve as inputs. The results which are based on real dimensions of a typical car demonstrate the feasibility and effectiveness of the proposed controller in parallel parking.

From Sensors to Human Spatial Concepts: An Annotated Data Set

An annotated data set is presented meant to help researchers in developing, evaluating, and comparing various approaches in robotics for building space representations appropriate for communicating with humans. The data consist of omnidirectional images, laser range scans, sonar readings, and robot odometry. A set of base-level human spatial concepts is used to annotate the data.

A Probabilistic Framework for Sonar Scan Matching Localization

Scan matching is a popular localization technique based on comparing two sets of range readings gathered at consecutive robot poses. Scan matching algorithms implicitly assume that matching readings correspond to the same object in the environment. This is a reasonable assumption when using accurate sensors such as laser range finders and that is why they are extensively used to perform scan matching localization. However, when using other sensors such as ultrasonic range finders or visual sonar, this assumption is no longer valid because of their lower angular resolution and the sparsity of the readings. In this paper we present a sonar scan matching framework, the spIC, which is able to deal with the sparseness and low angular resolution of sonar sensors. To deal with sparseness, a process to group sonar readings gathered along short robot trajectories is presented. Probabilistic models of ultrasonic and odometric sensors are defined to cope with the low sonar angular resolution. Consequently, a probabilistic scan matching process is performed. Finally, the correction of the whole robot trajectory involved in the matching process is presented as a constrained optimization problem.

Map building through pseudo dense scan matching using visual sonar data

This paper presents a novel approach to the vision based grid map building and localization problem that works in a complex indoor environment with a single forward viewing camera. Most existing visual SLAM has been limited to the feature-based method and only a few researchers have proposed visual SLAM methods for building a grid map using a stereo vision system which has not been popular in practical application. In this paper, we estimate the planar depth by applying a simple visual sonar ranging technique to the single camera image and then associating sequential scans through our own pseudo dense adaptive scan matching algorithm reducing the processing time compared to the standard point-to-point correspondence based algorithm and finally produce a grid map. To this end, we construct a Pseudo Dense Scan (PDS) which is an odometry based temporal accumulation of the visual sonar readings emulating omni-directional sensing in order to overcome the sparseness of the visual sonar. Moreover, in order to obtain a much more refined map, we further correct the slight trajectory error incurred in the PDS construction step using Sequential Quadratic Programming (SQP) which is a well-known optimization scheme. Experimental results show that our method can obtain an accurate grid map using a single camera without the need for a high price range sensors or stereo camera.

Grid-Based Visual SLAM in Complex Environments

This paper presents a novel approach to the real-time SLAM problem that works in unstructured indoor environment with a single forward viewing camera. Most existing visual SLAM extract features from the environment, associate them in different images and produce a feature map as a result. However, we estimate the distances between the robot and the obstacles by applying a visual sonar ranging technique to the image and then associate this range data through the Iterative Closest Point (ICP) algorithm and finally produce a grid map. Moreover, we construct a pseudo-dense scan (PDS) which is essentially a temporal accumulation of data, emulating a dense omni-directional sensing of the visual sonar readings based on odometry readings in order to overcome the sparseness of the visual sonar and then associate this scan with the previous one. Moreover, we further correct the slight trajectory error incurred in the PDS construction step to obtain a much more refined map using Sequential Quadratic Programming (SQP) which is a well-known optimization scheme. Experimental results show that our method can obtain an accurate grid map using a single camera alone without the need for more expensive.

PROBABILISTIC FILTERING OF SONAR DATA

In this paper we propose an iterative probabilistic sonar filtering technique able to decide whether a sonar reading is good or not. This technique is applied to standard time-of-flight sonars, and may be used in real time applications. A non-iterative approach based on a voting schema is also discussed, reducing even more the computational cost. The technique does not depend on any specific mapping, localization or navigation technique. Thus, a large range of applications is possible. A quality metric to evaluate the goodness of sets of raw readings is also presented. Experimental results demonstrate the fast convergence of the algorithm, as well as its robustness and the quality of the filtered readings.

Extração automática de mapas de atributos baseada em técnica bayesiana para localização de robôs móveis

A solução do problema de determinação da postura (ou localização) é de fundamental importância para a incorporação de autonomia em robôs móveis. Entretanto, devido à natureza inexata do movimento, uma localização precisa não é possível usando unicamente odometria. Faz-se necessário extrair das leituras sensoriais do robô informações que permitam corrigir os desvios intrínsecos a cada ação executada. Neste contexto, os algoritmos de Monte Carlo estimam e atualizam a postura (com base em modelos a priori de sensores e atuadores) através de um conjunto de partículas que simbolizam possíveis posturas do robô no ambiente, associadas a uma crença que indica quão bem estas se aproximam de sua localização real. Complexa, no entanto, é a tarefa de obtenção do modelo sensorial, principalmente quando realizada por meio de técnicas não-automáticas. A idéia central aqui utilizada consiste no treinamento de redes neurais artificiais para extração automática de um mapa de atributos que simplifica o modelo sensorial, usando um método bayesiano (BaLL - Bayesian Landmark Learning) a partir de leituras sensoriais. Este trabalho descreve a implementação utilizando-se como base a plataforma ARIA para simulação de um robô móvel Magellan Pro cujos sensores externos são sonares. Os resultados mostram o funcionamento da técnica e a sua aplicabilidade para a obtenção automática de mapas de atributos.

On the use of Bayesian Networks to develop behaviours for mobile robots

Bayesian Networks are models which capture uncertainties in terms of probabilities that can be used to perform reasoning under uncertainty. This paper presents an attempt to use Bayesian Networks as a learning technique to manage task execution in mobile robotics. To learn the Bayesian Network structure from data, the K2 structural learning algorithm is used, combined with three different net evaluation metrics. The experiment led to a new hybrid multiclassifying system resulting from the combination of 1-NN with the Bayesian Network, that allows one to use the power of the Bayesian Network while avoiding the computational burden of the reasoning mechanism — the so-called evidence propagation process. As an application example we present an approach of the presented paradigm to implement a door-crossing behaviour in a mobile robot using only sonar readings, in an environment with smooth walls and doors. Both the performance of the learning mechanism and the experiments run in the real robot-environment system show that Bayesian Networks are valuable learning mechanisms, able to deal with the uncertainty and variability inherent to such systems.

Simultaneous landmark classification, localization and map building for an advanced sonar ring

SUMMARYAn autonomous mobile robot operating in an unknown indoor environment often needs to map the environment while localizing within the map. Feature-based world models including line and point features are widely used by researchers. This paper presents a novel delayed-classi-fication algorithm to categorize these features using a recently developed high-performance sonar ring within a simultaneous localization and map-building (SLAM) process. The sonar ring sensor accurately measures range and bearing to multiple targets at near real-time repetition rates of 11.5 Hz to 6 m range, and uses 24 simultaneously fired transmitters, 48 receivers and multiple echoes per receiver. The proposed algorithm is based on hypothesis generation and verification using the advanced sonar ring data and an extended Kalman filter (EKF) approach. It is capable of initiating new geometric features and classifying them within a short distance of travel of about 10 cm. For each new sonar reading not matching an existing feature, we initiate a pair of probational line and point features resulting from accurate range and bearing measurements. Later measurements are used to confirm or remove the probational features using EKF validation gates. The odometry error model of the filter allows for variations in effective wheel separation required by pneumatic robot tyres. The implementation of the novel classification and SLAM algorithm is discussed in this paper and experimental results using real sonar data are presented.