Design and Control of Autonomous Underwater Robots: A Survey

During the 1990s, numerous worldwide research and development activities have occurred in underwater robotics, especially in the area of autonomous underwater vehicles (AUVs). As the ocean attracts great attention on environmental issues and resources as well as scientific and military tasks, the need for and use of underwater robotic systems has become more apparent. Great efforts have been made in developing AUVs to overcome challenging scientific and engineering problems caused by the unstructured and hazardous ocean environment. In the 1990s, about 30 new AUVs have been built worldwide. With the development of new materials, advanced computing and sensory technology, as well as theoretical advancements, R&D activities in the AUV community have increased. However, this is just the beginning for more advanced, yet practical and reliable AUVs. This paper surveys some key areas in current state-of-the-art underwater robotic technologies. It is by no means a complete survey but provides key references for future development. The new millennium will bring advancements in technology that will enable the development of more practical, reliable AUVs.     

一种基于视觉的移动机器人定位系统

具有自主的全局定位能力是自主式稳定机器人传感器系统的一项重要功能,为了实现这个目的,国内外均在不断地研究发展各种定位传感器系统,这里介绍了一种采用光学蝗全方位位置传感器系统,该传感器系统由主动式路标、视觉传感器、图象采集与数据处理系统组成,其视觉传感器和数据处理系统可安装在移动机器人上,然后可通过观测路标物「视角定位的方法,计算出机器人在世界坐标系中的位置和方向,实验证明,该系统可以只的在线定位,     

Navigation Using an Environmental Magnetic Field for Outdoor Autonomous Mobile Robots

GPS and laser range finders are generally utilized in current robot navigation. However, information from the magnetic field and electronic compass is not, since it is dynamically changing at every position. In this paper, the relationship between the intensity of a magnetic field in the environment and its position is taken into account by utilizing a three-axis magnetic sensor to scan the magnetic field in the environment to build a database. The mobile robot navigates by performing trajectory tracking based on the database. The experimental results show that by applying the proposed method, the mobile robot is able to navigate in an outdoor environment with reliable accuracy.     

Sonar Map Construction for Autonomous Mobile Robots Using a Data Association Filter

This paper presents a method for building a probability grid map for autonomous mobile robots with ultrasonic sensors using a data association filter (DAF). The method is based on evaluating the possibility that the acquired sonar data are all reflected by the same object. The DAF is able to associate data points with each other. Data affected by specular reflection are not likely to be associated with the same object, so they are excluded from the data cluster by the DAF, thereby improving the reliability of the data used for the probability grid map. Since the corrupted data are not used to update the probability map, it is possible to build a good quality grid map even in a specular environment. The DAF was applied to the Bayesian and the Orientation probability models, which are typical models used to build grid maps, to verify its effectiveness. Experimental results were also obtained using a mobile robot in a real-world environment.     

Path Planning for Autonomous Mobile Robots Using the Boundary Integral Equation Method

Path planning is a fundamental problem in mobile robotics. The objective of path planning is to find a sequence of states that carries a robot from some start state to the goal while avoiding obstacles. This paper focuses on point-to-point path planning, ie., developing a path between given points in a known environment with obstacles. A novel environment representation method is introduced which utilizes the Boundary Integral Equation (BIE) method to map the environment into continuous Harmonic potential fields. Different mappings and mechanisms for utilizing these mappings for path planning are developed, and a collection of path planning algorithms are described which exploit the properties of Harmonic fields and the computational efficiency of the BIE method.     

Semantics as a Foundation for Psychology: A Case Study of Wason's Selection Task

We review the various explanations that have been offered toaccount for subjects' behaviour in Wason's famous selection task. Weargue that one element that is lacking is a good understanding ofsubjects' semantics for the key expressions involved, and anunderstanding of how this semantics is affected by the demands the taskputs upon the subject's cognitive system. We make novel proposals inthese terms for explaining the major content effects of deonticmaterials. Throughout we illustrate with excerpts from tutorialdialogues which motivate the kinds of analysis proposed. Our long termgoal is an integration of the various insights about conditionalreasoning on offer from different cognitive science methodologies. Thepurpose of this paper is to try to draw the attention of logicians andsemanticists to this area, since we believe that empirical investigationof the cognitive processes involved could benefit from semanticanalyses.     

A Geometric Semantics for Dialogue Game Protocols for Autonomous Agent Interactions

Formal dialogue games studied by philosophers since the time of Aristotle have recently found application in Artificial Intelligence as the basis for protocols for interactions between autonomous software agents. For instance, game protocols have been proposed for agent dialogues involving team formation, persuasion, negotiation and deliberation. There is yet, however, no formal, mathematical theory of dialogue game protocols with which to compare two protocols or to study their formal properties. In this paper,¹ we present preliminary work towards such a theory, in which we develop a geometric semantics for these protocols and, with it, define a notion of equivalence between two protocols. We then demonstrate an algebraic property of protocol equivalence, and use this to show the non-equivalence of two similar generic protocols. We also explore the relationship between finite and infinite dialogues, motivated by the Ehrenfeucht-Fraïssé games of model theory. Our results have implications for the design and evaluation of agent dialogue-game protocols.     

A Real-Time Image Recognition System for Tiny Autonomous Mobile Robots

Intelligent sensors for mobile robots play an important role in many technical applications. In this paper a real-time image recognition system for a tiny autonomous mobile robot is presented, capable of detecting objects in real-time at a frame rate of up to 60 frames/s. The image recognition module has very low power consumption of less than 250 mW and fits into a package of only 35 × 35 mm including a CMOS camera and a low power, high performance signal processor. We propose an object recognition algorithm that is optimized for deeply embedded systems used in energy and performance constrained devices. The algorithm is based on a combination of edge and color detection and uses a fixed model for each object to be recognized. Results of the ball recognition application show that its relative polar coordinates are found within 11 ms.Stefan Mahlknecht received a Masters degree in electrical engineering and a doctoral degree from Vienna University of technology. His doctoral thesis dealt with the topic of energy selfsufficient wireless sensor networks. He spend one year at the University of Illinois in Urbana Champain, where he focused on communication networks and protocols. Since 2001 he is a member of the departements staff. His research interests are devoted to the topic of wireless sensor networks, communication protocols, embedded systems and robotics.Roland Oberhammer is a master thesis student at the Institute of Computer Technology at the Vienna University of Technology. His master thesis dealt with the topic of real-time ball recognition on resource limited embedded platforms. His research interests are in the field of embedded systems and robotics.Gregor Novak received a Masters degree in mechanical engineering and a doctoral degree in technical sciences both from the Vienna University of Technology in Austria in the years 1997 and 2002, respectively as well as a Masters degree in engineering management in 2001 from Oakland University in USA. Presently he is the coordinator of the Vienna University of Technologys Center of Excellence for Autonomous Systems. His research focuses on autonomous mobile cooperating robots.     

一种基于遗传算法的移动机器人自定位方法

针对移动机器人定位研究中的位姿跟踪、全局定位和"绑架"三类问题,提出一种基于遗传算法的移动机器人自定位方法.设计基于位置相似度的种群适应度计算方法,利用实值编码方式实现种群的交叉、变异,有效提高算法的实时性.针对机器人定位过程中的"绑架"现象,在常规遗传算法的基础上引人种群发散算子,减小种群匮乏效应.在此基础上,利用机器人运动模型更新种群状态实现机器人的连续定位.在实际室内环境进行机器人定位实验,证实本文算法的有效性.     

一种快速鲁棒的越野环境下自主移动机器人障碍检测算法

为了既能正确地从视差图中检测出障碍,又能鲁棒地抵抗噪声的干扰,本文提出一种快速鲁棒的越野环境下自主移动机器人障碍检测算法.首先综合利用高度约束、连续性约束和坡度约束给出了障碍点的完整定义,然后借用区域增长的思想综合多种约束进行障碍检测,最后使用冗余检测方法与多分辨率策略对算法进行优化,并对冗余检测方法的原理进行证明.实...     

Semantics for Knowledge and Change of Awareness

We examine various logics that combine knowledge, awareness, and change of awareness. An agent can become aware of propositional propositions but also of other agents or of herself. The dual operation to becoming aware, forgetting, can also be modelled. Our proposals are based on a novel notion of structural similarity that we call awareness bisimulation, the obvious notion of modal similarity for structures encoding knowledge and awareness.     

知识获取模型的设计与探讨

介绍了MAS的组成和BDI Agent的结构,在此基础上提出了基于动态信任的MAS知识获取模型,并给出了基于模态逻辑的知识获取形式化框架,最后用形式化框架对知识获取模型进行了描述.     

KAL:一个可学习的知识获取环境

可学习的知识获取环境KAL帮助软件工程师从领域专家那儿获取专家知识,形成一个具有继承性的概念层次结构.该环境为知识获取提供视图(viewing)、图示(graphing)、概念聚类和概念发现等工具.     

Perception and Planning Architecture for Autonomous Ground Vehicles

By combining smart sensors and traversability grids with a JAUS-based component and messaging architecture, DARPA Grand Challenge finalist team CIMAR quickly developed a robust autonomous ground vehicle (AGV), a custom-built off-road vehicle. Key components included six smart sensors for detecting environmental conditions and reporting a priori data, a smart arbiter for fusing data from multiple smart sensors, and a reactive driver for providing real-time navigation planning and obstacle avoidance     

Experimental Study of Iterated Kalman Filters for Simultaneous Localization and Mapping of Autonomous Mobile Robots

In this paper, we investigate the role of iteration in Kalman filters family for improvement of the estimation accuracy of states in simultaneous localization and mapping (SLAM). The linearized error propagation existing in Kalman filters family can result in large errors and inconsistency in the SLAM problem. One approach to alleviate this situation is the use of iteration in extended Kalman filter (EKF) and sigma point Kalman filter (SPKF) based SLAM. The main contribution is to present that the iterated versions of Kalman filters can increase consistency and robustness of these filters against linear error propagation. Experimental results are presented to validate this improvement of state estimate convergence through repetitive linearization of the nonlinear observation model in EKF-SLAM and SPKF-SLAM algorithms.     

B-Theta*: an Efficient Online Coverage Algorithm for Autonomous Cleaning Robots

We propose a novel approach to deal with the online complete-coverage task of cleaning robots in unknown workspaces with arbitrarily-shaped obstacles. Our approach is based on the boustrophedon motions, the boundary-following motions, and the Theta* algorithm known as B-Theta*. Under control of B-Theta*, the robot performs a single boustrophedon motion to cover an unvisited region. While performing the boustrophedon motion, if the robot encounters an obstacle with a boundary that has not yet been covered, it switches to the boundary mode to cover portions along the obstacle boundary, and then continues the boustrophedon motion until it detects an ending point. To move to an unvisited region, the robot detects backtracking points based on its accumulated knowledge, and applies an intelligent backtracking mechanism thanks to the proposed Theta* for multi-goals in order to reach the next starting point. Complete coverage is achieved when no starting point exists for a new boustrophedon motion. Computer simulations and experiments on real workspaces show that our proposed B-Theta* is efficient for the complete-coverage task of cleaning robots.     

Development of A Behavior‐Based Cooperative Search Strategy for Distributed Autonomous Mobile Robots Using Zigbee Wireless Sensor Network

To achieve efficient and objective search tasks in an unknown environment, a cooperative search strategy for distributed autonomous mobile robots is developed using a behavior‐based control framework with individual and group behaviors. The sensing information of each mobile robot activates the individual behaviors to facilitate autonomous search tasks to avoid obstacles. An 802.15.4 ZigBee wireless sensor network then activates the group behaviors that enable cooperative search among the mobile robots. An unknown environment is dynamically divided into several sub‐areas according to the locations and sensing data of the autonomous mobile robots. The group behaviors then enable the distributed autonomous mobile robots to scatter and move in the search environment. The developed cooperative search strategy successfully reduces the search time within the test environments by 22.67% (simulation results) and 31.15% (experimental results).     

Autonomous Unbiased Study Group Formation Algorithm for Rapid Knowledge Propagation

Knowledge propagation is a necessity, both in academics and in the industry. The focus of this work is on how to achieve rapid knowledge propagation using collaborative study groups. The practice of knowledge sharing in study groups finds relevance in conferences, workshops, and class rooms. Unfortunately, there appears to be only few researches on empirical best practices and techniques on study groups formation, especially for achieving rapid knowledge propagation. This work bridges this gap by presenting a workflow driven computational algorithm for autonomous and unbiased formation of study groups. The system workflow consists of a chronology of stages, each made of distinct steps. Two of the most important steps, subsumed within the algorithmic stage, are the algorithms that resolve the decisional problem of number of study groups to be formed, as well as the most effective permutation of the study group participants to form collaborative pairs. This work contributes a number of new algorithmic concepts, such as autonomous and unbiased matching, exhaustive multiplication technique, twisted round-robin transversal, equilibrium summation, among others. The concept of autonomous and unbiased matching is centered on the constitution of study groups and pairs purely based on the participants’ performances in an examination, rather than through any external process. As part of practical demonstration of this work, study group formation as well as unbiased pairing were fully demonstrated for a collaborative learning size of forty (40) participants, and partially for study groups of 50, 60 and 80 participants. The quantitative proof of this work was done through the technique called equilibrium summation, as well as the calculation of inter-study group Pearson Correlation Coefficients, which resulted in values higher than 0.9 in all cases. Real life experimentation was carried out while teaching Object-Oriented Programming to forty (40) undergraduates between February and May 2021. Empirical result showed that the performance of the learners was improved appreciably. This work will therefore be of immense benefit to the industry, academics and research community involved in collaborative learning.     

A Knowledge Discovery Framework for Civil Infrastructure: A Case Study of the Intelligent Workplace

Large databases are becoming increasingly common in civil infrastructure applications. Although it is relatively simple to specifically query these databases at a low level, more abstract questions like ‘How does the environment affect pavement cracking?’ are difficult to answer with traditional methods. Data mining techniques can provide a solution for learning abstract knowledge from civil infrastruc-ture databases. However, data mining needs to be performed within a systematic process to ensure correct and reproducible results. Many decisions must be made during this process, making it difficult for novice analysts to apply data mining techniques thoroughly. This paper presents an application of a knowledge discovery process to data collected for an ‘intelligent’ building. The knowledge discovery process is illustrated and explained through this case study. Additionally, we discuss the importance of this case study in the context of a research effort to develop an interactive guide for the knowledge discovery process.