A model based design framework for safety verification of a semi-autonomous inspection drone

In this paper, we present a model based design approach to the development of a semi-autonomous control system for an inspection drone. The system is tasked with maintaining a set distance from the target being inspected and a constant relative pose, allowing the operator to manoeuvre the drone around the target with ease. It is essential that the robustness of the autonomous behaviour be thoroughly verified prior to actual implementation, as this will involve the flight of a large multi-rotor drone in close proximity to a solid structure. By utilising the Robotic Operating System to communicate between the autonomous controller and the drone, the same Simulink model can be used for numerical coverage testing, high fidelity simulation, offboard execution and final executable deploymen

Collision Avoidance of Two Autonomous Quadcopters

Traffic collision avoidance systems (TCAS) are used in order to avoid incidences of mid-air collisions between aircraft. We present a game-theoretic approach of a TCAS designed for autonomous unmanned aerial vehicles (UAVs). A variant of the canonical example of game-theoretic learning, fictitious play, is used as a coordination mechanism between the UAVs, that should choose between the alternative altitudes to fly and avoid collision. We present the implementation results of the proposed coordination mechanism in two quad-copters flying in opposite directions

Distributed selection of flight formation in UAV missions

Recent advances in sensor, processor and airframe technologies allow coordination of large groups of autonomous unmanned aerial vehicles (UAV) today. Reconfiguration of the formation is sometimes necessary in order to accomplish a mission’s objectives. A centralised solution to optimal reconfiguration may often be either impossible or intractable due to sensor, communication, physical, computational restrictions. Thus a distributed approach may be more appropriate to accommodate real-world scenarios. In this article we propose a novel distributed control method, which is divided into two modules: a leaderfollower module, which allows UAVs to keep a pre-specified formation, and a decision making module that allows UAVs to choose among various available formations in an optimum sense. UAVs choose the best formation to accomplish each part of the mission and retain this formation till the next way-point. The simulation presented uses a 5-leg mission and Parrot AR-drones are used as test-beds to demonstrate the usefulness of the proposed distributed controller

HFNet-SLAM: an accurate and real-time monocular SLAM system with deep features

Image tracking and retrieval strategies are of vital importance in visual Simultaneous Localization and Mapping (SLAM) systems. For most state-of-the-art systems, hand-crafted features and bag-of-words (BoW) algorithms are the common solutions. Recent research reports the vulnerability of these traditional algorithms in complex environments. To replace these methods, this work proposes HFNet-SLAM, an accurate and real-time monocular SLAM system built on the ORB-SLAM3 framework incorporated with deep convolutional neural networks (CNNs). This work provides a pipeline of feature extraction, keypoint matching, and loop detection fully based on features from CNNs. The performance of this system has been validated on public datasets against other state-of-the-art algorithms. The results reveal that the HFNet-SLAM achieves the lowest errors among systems available in the literature. Notably, the HFNet-SLAM obtains an average accuracy of 2.8 cm in EuRoC dataset in pure visual configuration. Besides, it doubles the accuracy in medium and large environments in TUM-VI dataset compared with ORB-SLAM3. Furthermore, with the optimisation of TensorRT technology, the entire system can run in real-time at 50 FPS

Don’t Worry, We’ll Get There: Developing Robot Personalities to Maintain User Interaction After Robot Error

Human robot interaction (HRI) often considers the human impact of a robot serving to assist a human in achieving their goal or a shared task. There are many circumstances though during HRI in which a robot may make errors that are inconvenient or even detrimental to human partners. Using the ROBOtic GUidance and Interaction DEvelopment (ROBO-GUIDE) model on the Pioneer LX platform as a case study, and insights from social psychology, we examine key factors for a robot that has made such a mistake, ensuring preservation of individuals’ perceived competence of the robot, and individuals’ trust towards the robot. We outline an experimental approach to test these proposals

Deep parameter optimisation for face detection using the Viola-Jones algorithm in OpenCV

OpenCV is a commonly used computer vision library containing a wide variety of algorithms for the AI community. This paper uses deep parameter optimisation to investigate improvements to face detection using the Viola-Jones algorithm in OpenCV, allowing a trade-off between execution time and classification accuracy. Our results show that execution time can be decreased by 48 % if a 1.80 % classification inaccuracy is permitted (compared to 1.04 % classification inaccuracy of the original, unmodified algorithm). Further execution time savings are possible depending on the degree of inaccuracy deemed acceptable by the user

Stem cell factor (SCF) and c-kit in the ovine fetal testis in normal and nutrition perturbed pregnancies

Stem cell factor (SCF) and c-kit in the ovine fetal testis in normal and nutrition perturbed pregnancie

A modular digital twinning framework for safety assurance of collaborative robotics

Digital twins offer a unique opportunity to design, test, deploy, monitor, and control real-world robotic processes. In this paper we present a novel, modular digital twinning framework developed for the investigation of safety within collaborative robotic manufacturing processes. The modular architecture supports scalable representations of user-defined cyber-physical environments, and tools for safety analysis and control. This versatile research tool facilitates the creation of mixed environments of Digital Models, Digital Shadows, and Digital Twins, whilst standardising communication and physical system representation across different hardware platforms. The framework is demonstrated as applied to an industrial case-study focused on the safety assurance of a collaborative robotic manufacturing process. We describe the creation of a digital twin scenario, consisting of individual digital twins of entities in the manufacturing case study, and the application of a synthesised safety controller from our wider work. We show how the framework is able to provide adequate evidence to virtually assess safety claims made against the safety controller using a supporting validation module and testing strategy. The implementation, evidence and safety investigation is presented and discussed, raising exciting possibilities for the use of digital twins in robotic safety assurance

Ovarian Damage During Chemotherapy in Autoimmune Diseases: Broad Health Implications beyond Fertility

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94402/1/Marder_2012_Ovarian_damage_chemotherapy_autoimmune.pdf165

CMV infection of liver transplant recipients: comparison of antigenemia and molecular biology assays

BACKGROUND: CMV is a major clinical problem in transplant recipients. Thus, it is important to use sensitive and specific diagnostic techniques to rapidly and accurately detect CMV infection and identify patients at risk of developing CMV disease. In the present study, CMV infection after liver transplantation was monitored retrospectively by two molecular biology assays - a quantitative PCR assay and a qualitative NASBA assay. The results were compared with those obtained by prospective pp65 antigenemia determinations. MATERIALS AND METHODS: 87 consecutive samples from 10 liver transplanted patients were tested for CMV by pp65 antigenemia, and CMV monitor and NASBA pp67 mRNA assay. RESULTS: CMV infection was detected in all patients by antigenemia and CMV monitor, whereas NASBA assay identified only 8/10 patients with viremia. Furthermore, CMV infection was never detected earlier by molecular biology assays than by antigenemia. Only 5/10 patients with CMV infection developed CMV disease. Using a cut off value of 8 cells/50,000, antigenemia was found to be the assay that better identified patients at risk of developing CMV disease. However, the kinetics of the onset of infection detected by NASBA and CMV monitor seemed to have better identified patients at risk of developing CMV disease. Furthermore, before onset of disease, CMV pp67 mRNA was found to have similar or better negative and positive predictive values for the development of CMV disease. CONCLUSIONS: The present data, suggests that the concomitant use of antigenemia and pp67 mRNA assay gives the best identification of patients at risk of developing CMV disease