Odometry Correction of a Mobile Robot Using a Range-Finding Laser

Two methods for improving odometry using a pan-tilt range-finding laser is considered. The first method is a one-dimensional model that uses the laser with a sliding platform. The laser is used to determine how far the platform has moved along a rail. The second method is a two-dimensional model that mounts the laser to a mobile robot. In this model, the laser is used to improve the odometry of the robot. Our results show that the one-dimensional model proves our basic geometry is correct, while the two-dimensional model improves the odometry, but does not completely correct it

Uncertainty Minimization in Robotic 3D Mapping Systems Operating in Dynamic Large-Scale Environments

This dissertation research is motivated by the potential and promise of 3D sensing technologies in safety and security applications. With specific focus on unmanned robotic mapping to aid clean-up of hazardous environments, under-vehicle inspection, automatic runway/pavement inspection and modeling of urban environments, we develop modular, multi-sensor, multi-modality robotic 3D imaging prototypes using localization/navigation hardware, laser range scanners and video cameras. While deploying our multi-modality complementary approach to pose and structure recovery in dynamic real-world operating conditions, we observe several data fusion issues that state-of-the-art methodologies are not able to handle. Different bounds on the noise model of heterogeneous sensors, the dynamism of the operating conditions and the interaction of the sensing mechanisms with the environment introduce situations where sensors can intermittently degenerate to accuracy levels lower than their design specification. This observation necessitates the derivation of methods to integrate multi-sensor data considering sensor conflict, performance degradation and potential failure during operation. Our work in this dissertation contributes the derivation of a fault-diagnosis framework inspired by information complexity theory to the data fusion literature. We implement the framework as opportunistic sensing intelligence that is able to evolve a belief policy on the sensors within the multi-agent 3D mapping systems to survive and counter concerns of failure in challenging operating conditions. The implementation of the information-theoretic framework, in addition to eliminating failed/non-functional sensors and avoiding catastrophic fusion, is able to minimize uncertainty during autonomous operation by adaptively deciding to fuse or choose believable sensors. We demonstrate our framework through experiments in multi-sensor robot state localization in large scale dynamic environments and vision-based 3D inference. Our modular hardware and software design of robotic imaging prototypes along with the opportunistic sensing intelligence provides significant improvements towards autonomous accurate photo-realistic 3D mapping and remote visualization of scenes for the motivating applications

Design and analysis of Intelligent Navigational controller for Mobile Robot

Since last several years requirement graph for autonomous mobile robots according to its virtual application has always been an upward one. Smother and faster mobile robots navigation with multiple function are the necessity of the day. This research is based on navigation system as well as kinematics model analysis for autonomous mobile robot in known environments. To execute and attain introductory robotic behaviour inside environments(e.g. obstacle avoidance, wall or edge following and target seeking) robot uses method of perception, sensor integration and fusion. With the help of these sensors robot creates its collision free path and analyse an environmental map time to time. Mobile robot navigation in an unfamiliar environment can be successfully studied here using online sensor fusion and integration. Various AI algorithm are used to describe overall procedure of mobilerobot navigation and its path planning problem. To design suitable controller that create collision free path are achieved by the combined study of kinematics analysis of motion as well as an artificial intelligent technique. In fuzzy logic approach, a set of linguistic fuzzy rules are generated for navigation of mobile robot. An expert controller has been developed for the navigation in various condition of environment using these fuzzy rules. Further, type-2 fuzzy is employed to simplify and clarify the developed control algorithm more accurately due to fuzzy logic limitations. In addition, recurrent neural network (RNN) methodology has been analysed for robot navigation. Which helps the model at the time of learning stage. The robustness of controller has been checked on Webots simulation platform. Simulation results and performance of the controller using Webots platform show that, the mobile robot is capable for avoiding obstacles and reaching the termination point in efficient manner

The dynamics of complex systems. Studies and applications in computer science and biology

Our research has focused on the study of complex dynamics and on their use in both information security and bioinformatics. Our first work has been on chaotic discrete dynamical systems, and links have been established between these dynamics on the one hand, and either random or complex behaviors. Applications on information security are on the pseudorandom numbers generation, hash functions, informationhiding, and on security aspects on wireless sensor networks. On the bioinformatics level, we have applied our studies of complex systems to theevolution of genomes and to protein folding

Algorithms for Positioning with Nonlinear Measurement Models and Heavy-tailed and Asymmetric Distributed Additive Noise

Determining the unknown position of a user equipment using measurements obtained from transmitters with known locations generally results in a nonlinear measurement function. The measurement errors can have a heavy-tailed and/ or skewed distribution, and the likelihood function can be multimodal.A positioning problem with a nonlinear measurement function is often solved by a nonlinear least squares (NLS) method or, when filtering is desired, by an extended Kalman filter (EKF). However, these methods are unable to capture multiple peaks of the likelihood function and do not address heavy-tailedness or skewness. Approximating the likelihood by a Gaussian mixture (GM) and using a GM filter (GMF) solves the problem. The drawback is that the approximation requires a large number of components in the GM for a precise approximation, which makes it unsuitable for real-time positioning on small mobile devices.This thesis studies a generalised version of Gaussian mixtures, which is called GGM, to capture multiple peaks. It relaxes the GM’s restriction to non-negative component weights. The analysis shows that the GGM allows a significant reduction of the number of required Gaussian components when applied for approximating the measurement likelihood of a transmitter with an isotropic antenna, compared with the GM. Therefore, the GGM facilitates real-time positioning in small mobile devices. In tests for a cellular telephone network and for an ultra-wideband network the GGM and its filter provide significantly better positioning accuracy than the NLS and the EKF.For positioning with nonlinear measurement models, and heavytailed and skewed distributed measurement errors, an Expectation Maximisation (EM) algorithm is studied. The EM algorithm is compared with a standard NLS algorithm in simulations and tests with realistic emulated data from a long term evolution network. The EM algorithm is more robust to measurement outliers. If the errors in training and positioning data are similar distributed, then the EM algorithm yields significantly better position estimates than the NLS method. The improvement in accuracy and precision comes at the cost of moderately higher computational demand and higher vulnerability to changing patterns in the error distribution (of training and positioning data). This vulnerability is caused by the fact that the skew-t distribution (used in EM) has 4 parameters while the normal distribution (used in NLS) has only 2. Hence the skew-t yields a closer fit than the normal distribution of the pattern in the training data. However, on the downside if patterns in training and positioning data vary than the skew-t fit is not necessarily a better fit than the normal fit, which weakens the EM algorithm’s positioning accuracy and precision. This concept of reduced generalisability due to overfitting is a basic rule of machine learning.This thesis additionally shows how parameters of heavy-tailed and skewed error distributions can be fitted to training data. It furthermore gives an overview on other parametric methods for solving the positioning method, how training data is handled and summarised for them, how positioning is done by them, and how they compare with nonparametric methods. These methods are analysed by extensive tests in a wireless area network, which shows the strength and weaknesses of each method

Technology 2003: Conference Proceedings from the Fourth National Technology Transfer Conference and Exposition, Volume 1

Proceedings from symposia of the Technology 2003 Conference and Exposition, December 7-9, I993, Anaheim, CA. Volume 1 features the Plenary Session and the Plenary Workshop, plus papers presented in Advanced Manufacturing, Biotechnology/Medical Technology, Environmental Technology, Materials Science, and Power and Energy

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

Smart chemical sensing microsystem : towards a nose-on-a-chip

The electronic nose is a rudimentary replica of the human olfactory system. However there has been considerable commercial interest in the use of electronic nose systems in application areas such as environmental, medical, security and food industry. In many ways the existing electronic nose systems are considerable inferior when compared to their biological counterparts, lacking in terms of discrimination capability, processing time and environmental adaptation. Here, the aim is to extract biological principles from the mammalian olfactory systems to create a new architecture in order to aid the implementation of a nose-on-a-chip system. The primary feature identified in this study was the nasal chromatography phenomena which may provide significant improvement by producing discriminatory spatio-temporal signals for electronic nose systems. In this project, two different but complimentary groups of systems have been designed and fabricated to investigate the feasibility of generating spatio-temporal signals. The first group of systems include the fast-nose (channel 10 cm x 500 μm2), proto-nose I (channel 1.2 m x 500 μm2) and II (channel 2.4 m x 500 μm2) systems that were build using discrete components. The fast-nose system was used to characterise the discrete sensors prior to use. The proto-nose systems, in many ways, resembles gas chromatography systems. Each proto-nose system consists of two microchannels (with and without coating) and 40 polymer-composite sensors of 10 different materials placed along it. The second group of systems include the hybrid-nose and the aVLSI-nose microsensor arrays assembled with microchannel packages of various lengths (5 cm, 32 cm, 7lcm, 240 cm) to form nose-on-a-chip systems. The hybrid-nose sensor array consists of 80 microsensors built on a 10 mm x 10 mm silicon substrate while the aVLSI-nose sensor array consists of 70 microsensors built on a 10 mm x 5 mm silicon substrate using standard CMOS process with smart integrated circuitries. The microchannel packages were fabricated using the Perfactory microstereolithography system. The most advanced microchannel package contains a 2.4 m x 500 J.lm2 microchannel with an external size of only 36 mm x 27 mm x 7 mm. The nose-on-a-chip system achieved miniaturisation and eliminates the need for any external processing circuitries while achieving the same capability of producing spatio-temporal signals. Using a custom-designed vapour test station and data acquisition electronics, these systems were evaluated with simple analytes and complex odours. The experimental results were in-line with the simulation results. On the coated proto-nose II system, a 25 s temporal delay was observed on the toluene vapour pulse compared to ethanol vapour pulse; this is significant compared to the uncoated system where no delay difference was obtained. Further testing with 8 analyte mixtures substantiated that spatio-temporal signals can be extracted from both the coated proto-nose and nose-on-a-chip (hybrid-nose sensor array with 2.4 m long microchannel) systems. This clearly demonstrates that these systems were capable of imitating certain characteristics of the biological olfactory system. Using only the temporal data, classification was performed with principal components analysis. The results reinforced that these additional temporal signals were useful to improve discrimination analysis which is not possible with any existing sensor-based electronic nose system. In addition, fast responding polymer-composite sensors were achieved exhibiting response times of less than 100 ms. Other biological characteristics relating to stereolfaction (two nostrils sniffing at different rates), sniffing rate (flow velocity) and duration (pulse width) were also investigated. The results converge with the biological observations that stereolfaction and sniffing at higher rate and duration improve discrimination. Last but not least, the characterisation of the smart circuitries on the aVLSI-nose show that it is possible to achieve better performance through the use of smart processing circuitries incorporating a novel DC-offset cancellation technique to amplify small sensor response with large baseline voltage. The results and theories presented in this study should provide useful contribution for designing a higher-performance electronic nose incorporating biological principles

Collected Papers (Neutrosophics and other topics), Volume XIV

This fourteenth volume of Collected Papers is an eclectic tome of 87 papers in Neutrosophics and other fields, such as mathematics, fuzzy sets, intuitionistic fuzzy sets, picture fuzzy sets, information fusion, robotics, statistics, or extenics, comprising 936 pages, published between 2008-2022 in different scientific journals or currently in press, by the author alone or in collaboration with the following 99 co-authors (alphabetically ordered) from 26 countries: Ahmed B. Al-Nafee, Adesina Abdul Akeem Agboola, Akbar Rezaei, Shariful Alam, Marina Alonso, Fran Andujar, Toshinori Asai, Assia Bakali, Azmat Hussain, Daniela Baran, Bijan Davvaz, Bilal Hadjadji, Carlos Díaz Bohorquez, Robert N. Boyd, M. Caldas, Cenap Özel, Pankaj Chauhan, Victor Christianto, Salvador Coll, Shyamal Dalapati, Irfan Deli, Balasubramanian Elavarasan, Fahad Alsharari, Yonfei Feng, Daniela Gîfu, Rafael Rojas Gualdrón, Haipeng Wang, Hemant Kumar Gianey, Noel Batista Hernández, Abdel-Nasser Hussein, Ibrahim M. Hezam, Ilanthenral Kandasamy, W.B. Vasantha Kandasamy, Muthusamy Karthika, Nour Eldeen M. Khalifa, Madad Khan, Kifayat Ullah, Valeri Kroumov, Tapan Kumar Roy, Deepesh Kunwar, Le Thi Nhung, Pedro López, Mai Mohamed, Manh Van Vu, Miguel A. Quiroz-Martínez, Marcel Migdalovici, Kritika Mishra, Mohamed Abdel-Basset, Mohamed Talea, Mohammad Hamidi, Mohammed Alshumrani, Mohamed Loey, Muhammad Akram, Muhammad Shabir, Mumtaz Ali, Nassim Abbas, Munazza Naz, Ngan Thi Roan, Nguyen Xuan Thao, Rishwanth Mani Parimala, Ion Pătrașcu, Surapati Pramanik, Quek Shio Gai, Qiang Guo, Rajab Ali Borzooei, Nimitha Rajesh, Jesús Estupiñan Ricardo, Juan Miguel Martínez Rubio, Saeed Mirvakili, Arsham Borumand Saeid, Saeid Jafari, Said Broumi, Ahmed A. Salama, Nirmala Sawan, Gheorghe Săvoiu, Ganeshsree Selvachandran, Seok-Zun Song, Shahzaib Ashraf, Jayant Singh, Rajesh Singh, Son Hoang Le, Tahir Mahmood, Kenta Takaya, Mirela Teodorescu, Ramalingam Udhayakumar, Maikel Y. Leyva Vázquez, V. Venkateswara Rao, Luige Vlădăreanu, Victor Vlădăreanu, Gabriela Vlădeanu, Michael Voskoglou, Yaser Saber, Yong Deng, You He, Youcef Chibani, Young Bae Jun, Wadei F. Al-Omeri, Hongbo Wang, Zayen Azzouz Omar