Visualizing underwater environments using multifrequency sonar

This article introduces seabed visualization by describing three case studies that use a high-speed, multifrequency, continuous scan sonar called the Seabed Visualization System. The case studies involve: modeling a harbour wall in Holland, permitting a virtual inspection of the harbour environment; visualizing a sunken military vessel, the SS Richard Montgomery; and visualizing underwater pipelines in Easington, England     

Smartphone-based pedestrian tracking in indoor corridor environments

As the use of smartphones spreads rapidly, user localization becomes an important issue for providing diverse location-based services (LBS). While tracking users in outdoor environments is easily done with GPS, the solution for indoor tracking is not trivial. One common technique for indoor user tracking is to employ inertial sensors, but such a system needs to be capable of handling noisy sensors that would normally lead to cumulative locating errors. To reduce such error, additional infrastructure has often been deployed to adjust for these cumulative location errors. As well, previous work has used highly accurate sensors or sensors that are strapped to the body. This paper presents a stand-alone pedestrian tracking system, using only a magnetometer and an accelerometer in a smartphone in indoor corridor environments that are normally laid out in a perpendicular design. Our system provides reasonably accurate pedestrian locations without additional infrastructure or sensors. The experiment results show that the location error is less than approximately 7 m, which is considered adequate for indoor LBS applications.     

An exploration strategy for non-stationary opponents

The success or failure of any learning algorithm is partially due to the exploration strategy it exerts. However, most exploration strategies assume that the environment is stationary and non-strategic. In this work we shed light on how to design exploration strategies in non-stationary and adversarial environments. Our proposed adversarial drift exploration (DE) is able to efficiently explore the state space while keeping track of regions of the environment that have changed. This proposed exploration is general enough to be applied in single agent non-stationary environments as well as in multiagent settings where the opponent changes its strategy in time. We use a two agent strategic interaction setting to test this new type of exploration, where the opponent switches between different behavioral patterns to emulate a non-deterministic, stochastic and adversarial environment. The agent’s objective is to learn a model of the opponent’s strategy to act optimally. Our contribution is twofold. First, we present DE as a strategy for switch detection. Second, we propose a new algorithm called R-max# for learning and planning against non-stationary opponent. To handle such opponents, R-max# reasons and acts in terms of two objectives: (1) to maximize utilities in the short term while learning and (2) eventually explore opponent behavioral changes. We provide theoretical results showing that R-max# is guaranteed to detect the opponent’s switch and learn a new model in terms of finite sample complexity. R-max# makes efficient use of exploration experiences, which results in rapid adaptation and efficient DE, to deal with the non-stationary nature of the opponent. We show experimentally how using DE outperforms the state of the art algorithms that were explicitly designed for modeling opponents (in terms average rewards) in two complimentary domains.     

An active sensing strategy for contact location without tactile sensors using robot geometry and kinematics

In this study, we develop new techniques to sense contact locations and control robots in contact situations in order to enable articulated robotic systems to perform manipulations and grasping actions. Active sensing approaches are investigated by utilizing robot kinematics and geometry to improve upon existing sensing methods for contact. Compliant motion control is used so that a robot can actively search for and localize the desired contact location. Robot control in a contact situation is improved by the precise estimation of the contact location. From this viewpoint, we investigate a new control strategy to accommodate the proposed sensing techniques in contact situations. The proposed estimation algorithm and the control strategy both work complementarily. Then, we verify the proposed algorithm through experiments using 7-DOF hardware and a simulation environment. The two major contributions of the proposed active sensing strategy are the estimation algorithm for contact location without any tactile sensors, and the control strategy complementing the proposed estimation algorithm.     

An efficient cooperative exploration strategy for wireless sensor network

Wireless sensor networks (WSNs) are used in several applications such as healthcare devices, aerospace systems, automobile industry, security monitoring. However, WSNs have several challenges to improve the efficiency, robustness, failure tolerance and reliability of these sensors. Thus, cooperation between sensors is an important deal that increases sensor trust. Cooperative WSNs can be used to optimize the exploration of an unknown area in a distributed way. In this paper, the distributed Markovian model strategy that is used due to their past state-dependent reasoning. Moreover, the exploration strategy depends totally on the wireless communication protocol. Hence, in this paper, we propose an efficient cooperative strategy based on cognitive radio and software-defined radio which are promising technologies that increase spectral utilization and optimize the use of radio resources. We implement a distributed exploration strategy (DES) in mobile robots, and several experiments have been performed to localize targets while avoiding obstacles. Experiments were performed with several exploration robots. A comparison with another exploration strategy shows that DES improves the robots exploration.     

Sensor fusion-based exploration in home environments using information,driving and localization gains

Exploration is one of the most important functions for a mobile service robot because a map is required to carry out various tasks. A suitable strategy is needed to efficiently explore an environment and to build an accurate map. This study proposed the use of several gains (information, driving, localization) that, if considered during exploration, can simultaneously improve the efficiency of the exploration process and quality of the resulting map. Considering the information and driving gains reduces behavior that leads a robot to explore a previously visited place, and thus the exploration distance is reduced. In addition, the robot can select a favorable path for localization by considering the localization gain during exploration, and the robot can estimate its pose more robustly than other methods that do not consider localizability during exploration. This proposed exploration method was verified by various experiments, which verified that a robot can build an accurate map fully autonomously and efficiently in various home environments using the proposed method.     

An exploration of students' strategy use in inquiry-based computer-supported collaborative learning

Abstract The aim of this study is to investigate students' use of cognitive learning strategies in inquiry-based computer-supported collaborative learning (CSCL). A process-oriented interview framework on cognitive activity, self-regulation and motivation, and a coding category for analysing cognitive learning strategies and cognitive self-regulation was developed. The students of an intervention group (n=18) participating in inquiry-based CSCL and a comparison group (n=8) were interviewed six to eight times during the 3 years of the study. The results derived from the mixed-method analysis of altogether 161 interviews were compared between the two groups. The results indicate that the students who participated in the inquiry-based CSCL activities reported deeper-level cognitive strategies such as monitoring, creating representations and sharing information collaboratively. The students of the comparison group reported more surface-level strategies such as memorization. However, the findings concerning the utility of CSCL inquiry on cognitive learning strategies were not uniformly positive. It was found that the students of the comparison group reported significantly more strategies under the category of content evaluation. Nevertheless, the results suggest that computer-supported inquiry-based learning can enhance the use of cognitive strategies that support learning.     

Motion planning in uncertain environments with vision-like sensors

In this work we present a methodology for intelligent path planning in an uncertain environment using vision-like sensors, i.e., sensors that allow the sensing of the environment non-locally. Examples would include a mobile robot exploring an unknown terrain or a micro-UAV navigating in a cluttered urban environment. We show that the problem of path planning in an uncertain environment, under certain assumptions, can be posed as the adaptive optimal control of an uncertain Markov decision process, characterized by a known, control-dependent system, and an unknown, control-independent environment. The strategy for path planning then reduces to computing the control policy based on the current estimate of the environment, also known as the “certainty-equivalence” principle in the adaptive control literature. Our methodology allows the inclusion of vision-like sensors into the problem formulation, which, as empirical evidence suggests, accelerates the convergence of the planning algorithms. Further we show that the path planning and estimation problems, as formulated in this paper, possess special structure which can be exploited to significantly reduce the computational burden of the associated algorithms. We apply this methodology to the problem of path planning of a mobile rover in a completely unknown terrain.     

Efficient adaptive response surface method using intelligent space exploration strategy

This article presents a novel intelligent space exploration strategy (ISES), which is then integrated with the adaptive response surface method (ARSM) for higher global optimization efficiency. ISES consists of two novel elements for space reduction and sequential sampling: i) Significant design space (SDS) identification algorithm, which is developed to identify the promising design space and balance local exploitation and global exploration during the search, and ii) An iterative maximin sequential Latin hypercube design (LHD) sampling scheme and tailored termination criteria. Moreover, an adaptive penalty method is developed for handling expensive constraints. The new global optimization strategy, notated as ARSM-ISES, is then tested with numerical benchmark problems on optimization efficiency, global convergence, robustness, and algorithm execution overhead. Comparative results show that ARSM-ISES not only outperforms the original ARSM and IARSM, in general it also converges to better optima with fewer function evaluations and less algorithm execution time as compared to state-of-the-art metamodel-based design optimization algorithms including MPS, EGO, and MSEGO. For high dimensional (HD) problems, ARSM-ISES shows promises as it performs better on chosen test problems than TR-MPS, which is especially designed for solving HD problems. ARSM-ISES is then applied to the optimal design of a lifting surface of hypersonic flight vehicles. Finally, main features and limitations of the proposed algorithm are discussed.     

Efficient exploration of unknown indoor environments using a team of mobile robots

Whenever multiple robots have to solve a common task, they need to coordinate their actions to carry out the task efficiently and to avoid interferences between individual robots. This is especially the case when considering the problem of exploring an unknown environment with a team of mobile robots. To achieve efficient terrain coverage with the sensors of the robots, one first needs to identify unknown areas in the environment. Second, one has to assign target locations to the individual robots so that they gather new and relevant information about the environment with their sensors. This assignment should lead to a distribution of the robots over the environment in a way that they avoid redundant work and do not interfere with each other by, for example, blocking their paths. In this paper, we address the problem of efficiently coordinating a large team of mobile robots. To better distribute the robots over the environment and to avoid redundant work, we take into account the type of place a potential target is located in (e.g., a corridor or a room). This knowledge allows us to improve the distribution of robots over the environment compared to approaches lacking this capability. To autonomously determine the type of a place, we apply a classifier learned using the AdaBoost algorithm. The resulting classifier takes laser range data as input and is able to classify the current location with high accuracy. We additionally use a hidden Markov model to consider the spatial dependencies between nearby locations. Our approach to incorporate the information about the type of places in the assignment process has been implemented and tested in different environments. The experiments illustrate that our system effectively distributes the robots over the environment and allows them to accomplish their mission faster compared to approaches that ignore the place labels.     

An information-based exploration strategy for environment mapping with mobile robots

The availability of efficient mapping systems to produce accurate representations of initially unknown environments is recognized as one of the main requirements for autonomous mobile robots. In this paper, we present an efficient mapping system that has been implemented on a mobile robot equipped with a laser range scanner. The system builds geometrical point-based maps of environments employing an information-based exploration strategy that determines the best observation positions by taking into account both the distance travelled and the information gathered. Our exploration strategy, being based on solid mathematical foundations, differs from many ad hoc exploration strategies proposed in literature. We present: (a) the theoretical aspects of the criterion for determining the best observation positions for a robot building a map, (b) the implementation of a mapping system that uses the proposed criterion, and (c) the experimental validation of our approach.     

Mobile robot localization using sonar

This correspondence describes a method by which range data from a sonar rangefinder can be used to determine the two-dimensional position and orientation of a mobile robot inside a room. The plan of the room is modeled as a list of segments indicating the positions of walls. The algorithm works by correlating straight segments in the range data against the room model, then eliminating implausible configurations using the sonar barrier test, which exploits physical constraints on sonar data. The approach is extremely tolerant of noise and clutter. Transient objects such as furniture and people need not be included in the room model, and very noisy, low-resolution sensors can be used. The algorithm's performance is demonstrated using a Polaroid Ultrasonic Rangefinder.     

A vertical and floor line-based monocular SLAM system for corridor environments

In this paper, we propose a vertical and floor line-based monocular simultaneous localization and mapping (SLAM) system which utilizes vertical lines, floor lines, and vanishing points as sensory input to perform robust SLAM in corridor environments. By combining three map feature types, our design can help a robot to perform accurate pose estimation, repeatable loop closure, and to construct a more expressive environmental map. As a primitive element of a geometric structure, a line segment has one additional dimension compared to a point feature, thereby allowing the use of line segments to easily represent a geometric structure using a smaller number of features. This system presents map features on a 2D ground space: the vertical line as a projection point, the floor line as the original line, and the vanishing point as a directional vector. Although the vertical line, floor line, and vanishing point use different parameterization and initialization methods, their measurement models are integrated into a unified extended Kalman filter (EKF) framework. Experimental results show that our system can be deployed in a structured indoor environment as a suitable SLAM solution.     

Image-based interactive exploration of real-world environments

Interactive scene walkthroughs have long been an important computer graphics application area. More recently, researchers have developed techniques for constructing photorealistic 3D architectural models from real-world images. We present an image-based rendering system that brings us a step closer to a compelling sense of being there. Whereas many previous systems have used still photography and 3D scene modeling, we avoid explicit 3D reconstruction because it tends to be brittle. Our system is not the first to propose interactive video-based tours. We believe, however, that our system is the first to deliver fully interactive, photorealistic image-based tours on a personal computer at or above broadcast video resolutions and frame rates. Moreover, to our knowledge, no other tour provides the same rich set of interactions or visually complex environments.     

Using mobile beacons to locate sensors in obstructed environments

Locating sensors in an indoor environment is a challenging problem due to the insufficient distance measurements caused by short ultrasound range and the incorrect distance measurements caused by multipath effect of ultrasound. In this paper, we propose a virtual ruler approach, in which a vehicle equipped with multiple ultrasound beacons travels around the area to measure distances between pairwise sensors. Virtual Ruler can not only obtain sufficient distances between pairwise sensors, but can also eliminate incorrect distances in the distance measurement phase of sensor localization. We propose to measure the distance between pairwise sensors from multiple perspectives using the virtual ruler and filter incorrect values through a statistical approach. By assigning measured distances with confidence values, the localization algorithm can intelligently localize each sensor based on high confidence distances, which greatly improves localization accuracy. Our performance evaluation shows that the proposed approach can achieve better localization results than previous approaches in an indoor environment.     

Three-dimensional tracking using qualitative bionic sonar

A sonar-driven robot, ROBAT^3D, has been implemented to track an object moving in three dimensions using qualitative interpretation of the sonar signals. ROBAT^3D is equipped with a sonar system consisting of five identical transducers configured in the form of a cross. The center transducer emits an acoustic pulse and the echoes are detected by pairs of receivers that flank the transmitter horizontally and vertically. The vertically-oriented receivers are used to control the pitch while those oriented horizontally control the yaw. ROBAT^3D is driven by four air jets that provide lateral forces that direct the heading toward the object. Two bits of information are extracted from each emission, one for pitch correction and the other for yaw correction. The correction is determined by which sensor in a pair first detects the echo. That sensor actuates an air valve supplying the contralateral jet to correct the heading. The range to the object can be determined from the echo time-of-flight. This simple nonlinear analog control system is very fast and can track an object that has an angular velocity upto π /2 rad /s. A trade-off exists between the size of a limit cycle oscillation and the bandwith of the system, determined by the thrust of the jets and the delay in the sonar sensing.     

Mobile robot map making using sonar

This article describes a method of producing high-resolution maps of an indoor environment with an autonomous mobile robot equipped with sonar range-finding sensors. This method is based on investigating obstacles in the near vicinity of a mobile robot. The mobile robot examines the straight line segments extracted from the sonar range data describing obstacles near the robot. The mobile robot then moves parallel to the straight line sonar segments, in close proximity to the obstacles, continually applying sonar barrier test. The sonar barrier test exploits the physical constraints of sonar data, and eliminates noisy data. This test determines whether or not a sonar line segment is a true obstacle edge or a false reflection. Low resolution sonar sensors can be used with the method described. The performance of the algorithm is demonstrated using a Denning Corp. Mobile Robot, equipped with a ring of Polaroid Corp. Ultrasonic Rangefinders.     

Scalable WIM: effective exploration in large-scale astrophysical environments

Navigating through large-scale virtual environments such as simulations of the astrophysical Universe is difficult. The huge spatial range of astronomical models and the dominance of empty space make it hard for users to travel across cosmological scales effectively, and the problem of wayfinding further impedes the user's ability to acquire reliable spatial knowledge of astronomical contexts. We introduce a new technique called the scalable world-in-miniature (WIM) map as a unifying interface to facilitate travel and wayfinding in a virtual environment spanning gigantic spatial scales: Power-law spatial scaling enables rapid and accurate transitions among widely separated regions; logarithmically mapped miniature spaces offer a global overview mode when the full context is too large; 3D landmarks represented in the WIM are enhanced by scale, positional, and directional cues to augment spatial context awareness; a series of navigation models are incorporated into the scalable WIM to improve the performance of travel tasks posed by the unique characteristics of virtual cosmic exploration. The scalable WIM user interface supports an improved physical navigation experience and assists pragmatic cognitive understanding of a visualization context that incorporates the features of large-scale astronomy.     

MATLAB辅助生物反应工程教学的探究与实践

生物反应工程涉及数学问题较多,现有教科书和枯燥的讲授方式使学生很难理解而缺乏学习兴趣.如何避免使用过多数学问题而达到好的教学效果,是生物反应工程教学的难题.本文介绍了用多媒体实现MATLAB辅助生物反应工程教学的方法,学生不需掌握过多与生物反应工程相关的数学知识,即可用MATLAB解决问题,激发了学生学习兴趣.研究实例还可作为相关课程教学的补充.四年教学实践证明:用MATLAB辅助生物反应工程教学,在培养学生主动学习和计算机应用能力方面效果显著.