An homogeneous space geometry for simultaneous localisation and mapping

Simultaneous Localisation and Mapping (SLAM) is the archetypal chicken and egg problem: Localisation of a robot with respect to a map requires an estimate of the map, while mapping an environment from data acquired by a robot requires an estimate of the robot localisation. The nonlinearity and co-dependence of the SLAM problem has made it an ongoing research problem for more than thirty years. The present paper details recent advances in understanding the SLAM problem, specifically the existence of an underlying geometry and symmetry structure that provides significant insight into the difficulties that have plagued many SLAM algorithms. To demonstrate the power of the geometric insight we derive a constant gain observer for the SLAM problem that; that does not depend on linearisation, has globally asymptotically stable error dynamics, is very robust, and operates in dynamic environments (estimating the landmark velocities as states in the observer).     

基于平方位置误差下限的优化功率分配方案

在无线传感网络中,节点定位是基于位置的应用基本要求。然而,现多数文献仅关注定位精度,而忽略了能量消耗对定位精度的影响。为此,针对基于接收信号强度RSSI(Received Signal Strength Indicator)定位方案,提出基于平方位置误差下限SPEB(Squared Position Error Bound)的优化功率分配SPEB-OPA(SPEB-Based Optimal Power Allocation)方案,目的在于最小化能量消耗。在SPEB-OPA算法中,将SPEB作为评定定位精度的参数,并推导出SPEB表达式,然后建立优化功率分配的目标函数,并考虑到锚节点位置存在误差。仿真结果表明,提出的SPEB-OPA方案极大地减少了功率消耗。当误差门限T=8时,SPEB-OPA方案的功率消耗比统一功率分配UPA(Uniform Power allocation)方案减少至50%。     

基于用户反馈的WiFi位置指纹数据库更新算法

为使Wi Fi位置指纹数据库,不在定位区域Wi Fi环境发生改变时,出现位置指纹过时现象,设计一种更新算法。该算法使用用户的反馈数据,考虑了AP信号不稳定情况下AP新增与撤销的检测问题;消除了手机热点等临时AP对传统更新算法的影响;最终通过检测AP位置移动等引起的RSS改变,加速位置指纹数据库的更新。实验结果表明,在实际环境中该算法在指纹数据库的更新效率上优于传统更新算法。     

Vibration data-driven machine learning architecture for structural health monitoring of steel frame structures

A vibration data-based machine learning architecture is designed for structural health monitoring (SHM) of a steel plane frame structure. This architecture uses a Bag-of-Features algorithm that extracts the speeded-up robust features (SURF) from the time-frequency scalogram images of the registered vibration data. The discriminative image features are then quantised to a visual vocabulary using K-means clustering. Finally, a support vector machine (SVM) is trained to distinguish the undamaged and multiple damage cases of the frame structure based on the discriminative features. The potential of the machine learning architecture is tested for an unseen dataset that was not used in training as well as with some datasets from entirely new damages close to existing (i.e., trained) damage classes. The results are then compared with those obtained using three other combinations of features and learning algorithms—(i) histogram of oriented gradients (HOG) feature with SVM, (ii) SURF feature with k-nearest neighbours (KNN) and (iii) HOG feature with KNN. In order to examine the robustness of the approach, the study is further extended by considering environmental variabilities along with the localisation and quantification of damage. The experimental results show that the machine learning architecture can effectively classify the undamaged and different joint damage classes with high testing accuracy that indicates its SHM potential for such frame structures.     

WSNs环境下基于高斯混合容积卡尔曼滤波的移动机器人定位算法

针对移动机器人的定位问题,提出一种面向无线传感器网络WSNs( Wireless Sensor Networks)环境下,结合高斯混合容积卡尔曼滤波( GM￣CKF)优化的定位算法。将WSNs对移动机器人的观测、机器人自身对环境特征的观测以及机器人自身运动控制量进行数据融合,并利用带有门限判别和选择性高斯分割的GM￣CKF算法,对机器人的预估位置实施预测修正,降低计算求解的空间维数,提高定位精度。仿真实验结果表明,所提出的方法比传统机器人自定位法定位精度有所提高,算法精度较标准的CKF算法提高了39.11％,比EKF算法提高了65.81％。     

Multisensor Fusion: An Autonomous Mobile Robot

A conventional autonomous mobile robot is introduced. The main idea is the integration of many conventional and sophisticated sensor fusion techniques, introduced by several authors in recent years. We show the actual possibility of integrating all these techniques together, rather than analyzing implementation details. The topics of multisensor fusion, observation integration and sensor coordination are widely used throuhout the article. The final goal is to demonstrate the validity of both mathematical and artificial intelligence techniques in guaranteeing vehicle survival in a dynamic environment, while the robot carries out a specific task. We review conventional techniques for the management of uncertainty while we describe an implementation of a mobile robot which combines on-line heterogeneous sensors in its navigation and localisation tasks.     

Analysis of the mechanical performance of a cardiovascular stent design based on micromechanical modelling

Stents are very commonly used in the treatment of coronary heart disease. They are permanent vascular support structures that offer a preferred alternative to bypass surgery in certain situations. The purpose of this work is to examine the mechanical behaviour of a stainless steel balloon expandable stent design using computational micromechanics in the context of the finite element method. Deployment and cardiac pulsing loading conditions are considered. Classical phenomenological plasticity theory (J₂ flow theory) and physically based crystal plasticity theory are used to describe the stent material behaviour. Parametric studies are carried out using both constitutive theories with a view to determining important stent deployment characteristics such as recoil and foreshortening. Comparisons of the results obtained using both theories illustrate differences, with the crystal plasticity theory models showing closer agreement to published performance data. The implications of this for stent design are discussed.     

Sound localisation ability of soldiers wearing infantry ACH and PASGT helmets

Helmets provide soldiers with ballistic and fragmentation protection but impair auditory spatial processing. Missed auditory information can be fatal for a soldier; therefore, helmet design requires compromise between protection and optimal acoustics. Twelve soldiers localised two sound signals presented from six azimuth angles and three levels of elevation presented at two intensity levels and with three background noises. Each participant completed the task while wearing no helmet and with two U.S. Army infantry helmets – the Personnel Armor System for Ground Troops (PASGT) helmet and the Advanced Combat Helmet (ACH). Results showed a significant effect of helmet type on the size of both azimuth and elevation error. The effects of level, background noise, azimuth and elevation were found to be significant. There was no effect of sound signal type. As hypothesised, localisation accuracy was greatest when soldiers did not wear helmet, followed by the ACH. Performance was worst with the PASGT helmet.     

A systematic approach to the problem of odour source localisation

Although chemical sensing is far simpler than vision or hearing, navigation in a chemical diffusion field is still not well understood. Biological studies have already demonstrated the use of various search methods (e.g., chemotaxis and biased random walk), but robotics research could provide new ways to investigate principles of olfactory-based search skills (Webb, 2000; Grasso, 2001). In previous studies on odour source localisation, we have tested three biologically inspired search strategies: chemotaxis, biased random walk, and a combination of these methods (Kadar and Virk, 1998; Lytridis et al., 2001). The main objective of the present paper is to demonstrate how simulation and robot experiments could be used conjointly to systematically study these search strategies. Specifically, simulation studies are used to calibrate and test our three strategies in concentric diffusion fields with various noise levels. An experiment with a mobile robot was also conducted to assess these strategies in a real diffusion field. The results of this experiment are similar to those of simulation studies showing that chemotaxis is a more efficient but less robust strategy than biased random walk. Overall, the combined strategy seems to be superior to chemotaxis and biased random walk in both simulation and robot experiment. Christodoulos Lytridis received a BEng degree in electronic and computer engineering, MSc degree in mobile robotics and PhD in Olfactory based navigation for mobile robots from the University of Portsmouth. His research interests include odor-based cooperative navigation, biologically inspired navigational methods, and odor sensors for mobile robots. He is an associate member of the Institution of Electrical Engineering. Endre E. Kadar is a senior lecturer in psychology at the University of Portsmouth. He began his academic career as a theoretical mathematician at the Institute of Hydraulic Planning, working in applied modelling on a variety of problems in engineering. Then he moved to the University of Connecticut to work as a teaching and research assistant and received his PhD in experimental psychology in 1996. His PhD research project has dealt with the problem of ecological foundation of perceptual control of locomotion. After moving to Portsmouth in 1996, his theoretical work provided the basis for a productive collaboration with engineers in robotics. Also, these novel field theoretical ideas have resulted in radically new approaches to exploratory learning and visual control of high speed driving. Application of these approaches has been supported by several ESRC grants. Gurvinder S. Virk graduated with first class honours in electrical and electronic engineering (University of Manchester) and received a PhD in control theory (Imperial College, London). He has followed an academic career working at Sheffield City Polytechnic, Universities of Southampton, Sheffield and Bradford, Portsmouth and is now professor of control and robotics at the University of Leeds. His main research interests include robotics, building and energy management systems, and application of advanced model-based control systems. He has held several grants from national and European sources, as well as industry (the total value of ￡11M). He has more then 200 publications, including four books. He coordinated the European thematic network CLAWAR and has been involved in developing robots for exploring volcanic environments, inspection of highway bridges and dams, and biomedical applications. He is chairman of the ISO TC184/SC2 Advisory Group on Standards for mobile service robots. He is Fellow of the Institution of Engineering and Technology, Fellow of the Chartered Institution of Building Services Engineers, Fellow of the Institution of Applied Mathematics and Its Applications. He is Charted Engineer and a Charted Mathematician. He has been awarded the Freedom of the City of London for his work in promoting Information Technology.     

Low-Power Log-Domain CMOS Filter Bank for 2-D Sound Source Localization

This paper proposes the design of a novel current-mode front-end for the extraction of localization spectral cues from two audio signals, together with test results. The front-end consists of two parallel filter banks, envelope extraction and comparison circuitry, together with an AGC loop. The extracted cues are intended to be further processed in order to determine the source azimuth and elevation. A current-mode log-domain implementation using subthreshold MOS operation is used for micropower operation while still achieving a good bandwidth and linearity. A current-mode solution is also preferred because of the ease of implementation of certain mathematical operations. The front-end splits the input signals into different frequency bands and computes monaural and interaural spectral cues from the resulting signal envelopes for each band. The front-end has been optimized to operate at a supply voltage of 1.8 V and most blocks have been designed using a differential architecture. To our knowledge, this is the first log-domain implementation of a front-end for 2-D localization cues extraction. The design has been carried out using a standard double-poly double-metal 0.8 m CMOS process with V _T = 0.8 V. The bandpass filters which form the main core of the chip exhibit a measured dynamic range of 62 dB corresponding to 1.9% THD, while the total power dissipation is 890 W.