Robotics Horizon

The Rt Hon David Willets, minister for Universities and Science identified the importance of Robotics and Autonomous Systems as a general technology: 'Robots acting independently of human control - which can learn, adapt and take decisions - will revolutionise our economy and society over the next 20 years' (Willetts 2013). The current report has the focus on the societal aspect of this revolution and briefly sets out the landscape of current and future robotic systems applied in everyday human life and offers a brief overview of what robotics currently is and might be about in the future. The report includes contributions from across the UK robotics community (though completeness is not claimed).The emphasis is on the application of robots operating in the vicinity of human beings that can learn, adapt and take decisions. However, the underlying enabling technologies such as machine vision, machine learning and artificial intelligence are not discussed separately

Error reduction techniques for a MEMS accelerometer-based digital input device.

Tsang, Chi Chiu.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 66-69).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiStatement of Originality --- p.vTable of Contents --- p.viiList of Figures --- p.xNomenclature --- p.xiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Objectives --- p.3Chapter 1.3 --- Contributions --- p.3Chapter 1.4 --- Thesis Organization --- p.4Chapter 2 --- A Ubiquitous Digital Writing System --- p.5Chapter 2.1 --- Introduction --- p.5Chapter 2.2 --- MEMS Motion Sensing Technology --- p.6Chapter 2.2.1 --- Micro-Electro-Mechanical Systems (MEMS) --- p.6Chapter 2.2.2 --- Principle of a MEMS Accelerometer --- p.6Chapter 2.2.3 --- Principle of a MEMS Gyroscope --- p.7Chapter 2.3 --- Architecture of Ubiquitous Digital Writing System --- p.8Chapter 2.3.1 --- Micro Inertial Measurement Unit (μlMU) --- p.8Chapter 2.3.2 --- Data Transmission Module --- p.10Chapter 2.3.3 --- User Interface Software --- p.10Chapter 2.4 --- Summary --- p.12Chapter 3 --- Calibration of μ-Inertial Measurement Unit --- p.13Chapter 3.1 --- Introduction --- p.13Chapter 3.2 --- Sources of Error --- p.13Chapter 3.2.1 --- Deterministic Errors --- p.13Chapter 3.2.2 --- Stochastic Error --- p.14Chapter 3.3 --- Calibration of Accelerometers --- p.14Chapter 3.4 --- Coordinate Transformation with Gravity Compensation --- p.15Chapter 3.4.1 --- Coordinate Transformation --- p.16Chapter 3.4.2 --- Attitude Determination --- p.18Chapter 3.4.3 --- Gravity Compensation --- p.19Chapter 3.5 --- Summary --- p.20Chapter 4 --- Zero Velocity Compensation --- p.21Chapter 4.1 --- Introduction --- p.21Chapter 4.2 --- Algorithm Description --- p.21Chapter 4.2.1 --- Stroke Segmentation --- p.22Chapter 4.2.2 --- Zero Velocity Compensation (ZVC) --- p.22Chapter 4.3 --- Experimental Results and Discussion --- p.23Chapter 4.4 --- Summary --- p.24Chapter 5 --- Kalman Filtering --- p.28Chapter 5.1 --- Introduction --- p.28Chapter 5.2 --- Summary of Kalman filtering algorithm --- p.28Chapter 5.2.1 --- System Model --- p.28Chapter 5.2.2 --- Initialization --- p.29Chapter 5.2.3 --- Time Update --- p.32Chapter 5.2.4 --- Measurement Update --- p.33Chapter 5.2.5 --- Stroke Segmentation --- p.34Chapter 5.3 --- Summary --- p.34Chapter 6 --- Error Compensation from Position Feedback --- p.35Chapter 6.1 --- Introduction --- p.35Chapter 6.2 --- Global Positioning System (GPS) --- p.35Chapter 6.3 --- Zero z-axis Kalman Filtering --- p.36Chapter 6.3.1 --- Algorithm Implementation --- p.36Chapter 6.3.2 --- Experimental Results and Discussion --- p.40Chapter 6.4 --- Combined Electromagnetic Resonance (EMR) Position Detection Board and μlMU --- p.43Chapter 6.4.1 --- EMR Position Detection System --- p.43Chapter 6.4.2 --- A Combined Scheme --- p.44Chapter 6.4.3 --- Algorithm Implementation --- p.46Chapter 6.4.4 --- Synchronization --- p.50Chapter 6.4.5 --- Experimental Results and Discussion --- p.50Chapter 6.5 --- Summary --- p.54Chapter 7 --- Conclusion --- p.55Chapter 7.1 --- Future Work --- p.56Chapter 7.1.1 --- Improvement in the μlMU --- p.56Chapter 7.1.2 --- Combined Camera Optical Tracking and μlMU --- p.57Chapter 7.2 --- Concluding Remarks --- p.58Chapter A --- Derivation of Kalman Filtering Algorithm --- p.59Chapter A.1 --- Introduction --- p.59Chapter A.2 --- Derivation of a Priori State Estimation Equation --- p.60Chapter A.3 --- Derivation of a Posteriori State Estimation Equation --- p.60Chapter A.4 --- Derivation of a Priori Error Covariance Matrix --- p.61Chapter A.5 --- Derivation of the Optimal Kalman Gain --- p.62Chapter A.6 --- Derivation of a Posteriori Error Covariance Matrix --- p.63Chapter B --- Derivation of Process Noise Covariance Matrix --- p.64Bibliography --- p.66Publications --- p.6

Recent advances in video analytics for rail network surveillance for security, trespass and suicide prevention— a survey

Railway networks systems are by design open and accessible to people, but this presents challenges in the prevention of events such as terrorism, trespass, and suicide fatalities. With the rapid advancement of machine learning, numerous computer vision methods have been developed in closed-circuit television (CCTV) surveillance systems for the purposes of managing public spaces. These methods are built based on multiple types of sensors and are designed to automatically detect static objects and unexpected events, monitor people, and prevent potential dangers. This survey focuses on recently developed CCTV surveillance methods for rail networks, discusses the challenges they face, their advantages and disadvantages and a vision for future railway surveillance systems. State-of-the-art methods for object detection and behaviour recognition applied to rail network surveillance systems are introduced, and the ethics of handling personal data and the use of automated systems are also considered