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Accurately discovering hazards and issuing appropriate warnings to drivers in advance or performing autonomous control is the core of the Collision Avoidance (CA) system used to solve traffic safety problems. More comprehensive environmental awareness, diversified communication technologies, and autonomous control can make the CA system more accurate and effective, thereby improving driving safety. In addition, the assistance of Artificial Intelligence (AI) technology can make the CA system adapt to the environment and facilitate fast and accurate decisions. Considering the current lack of a thorough survey of driving safety with sensing, vehicular communications, and AI-based collision avoidance, in this paper, we survey existing researches for state-of-the-art data-driven CA techniques. Firstly, we discuss the major steps of CA and key research issues. For each step, we review the existing enabling techniques and research methods for CA in detail, including sensing and vehicular communication for safe driving, as well as CA algorithm design. Particularly, we present a comparison between the most common AI algorithms for different functions in the CA system. Testbeds and projects for CA are summarized next. Finally, several open challenges and future research directions are also outlined.

Building autonomous switches is an effective approach for rewiring metabolic flux during microbial synthesis of chemicals. However, current autonomous switches largely rely on metabolite-responsive biosensors or quorum-sensing circuits. In this study, a stationary phase promoter (SPP) and a protein degradation tag (PDT) were combined with the CRISPR interference (CRISPRi) system to construct an autonomous repression system that could shut down multiple-gene expression depending on the cellular physiological state. With this autonomous CRISPRi system to regulate one target gene, a fermenter-scale titer of shikimic acid reached 21 g/L, which was the highest titer ever reported by Escherichia coli in a minimal medium without any chemical inducers. With three target genes repressed, 26 g/L glutaric acid could be achieved with decreased byproduct accumulation. These results highlight the applicability of the autonomous CRISPRi system for microbial production of value-added chemicals.

Zusammenfassung Forward Model Learning, also das Erlernen vorwärtsgerichteter Modelle aus Daten, findet Anwendung in der vorhersagebasierten Regelung. Dazu werden Ein- und Ausgaben des Systems beobachtet, um ein Transitionsmodell zu erstellen und Vorhersagen über zukünftige Zeitschritte zu ermöglichen. Insbesondere komplexe Zustandsräume erfordern den Einsatz von spezialisierten Such- und Modellbildungsverfahren. In dieser Arbeit stellen wir Abstraktionsheuristiken für hochdimensionale Zustandsräume vor, welche es ermöglichen, die Modellkomplexität zu reduzieren und in vielen Fällen ein interpretierbares Ergebnis herbeizuführen. In zwei Fallstudien zeigen wir die Wirksamkeit des vorgestellten Verfahrens anhand von Methoden der Künstlichen Intelligenz in Spielen und in Motion Control Szenarien. Deren Übertragung ermöglicht vielversprechende Anwendungen in der Automatisierungstechnik.

For decades sucker rod pump artificially lifted wells have used devices called pump off controllers (POC) to match the pumping unit's runtime to the available reservoir production by idling the well for a set time where variable frequencies drives are not available. In doing this the POC allows the well to enter a set period of downtime when the downhole pump fillage is incomplete to avoid premature failures, and then brings the well back online to operate before production is lost. Although this method has been successful for several years, autonomous control algorithms can be utilized to reduce failures or increase production in cases where the downtime is not already optimized. Optimizing the idle time for a sucker rod pump artificially lifted well involves understanding the amount of time required to fill the near wellbore storage area before generating a fluid column above the pump intake that will begin to hinder inflow from the reservoir into the wellbore. By varying the idle time and observing the impact on production and cycles the program hunts for the optimal idle time. By constantly hunting for the optimal idle time the optimization process can adjust the idle time when operating conditions change. This gives the advantage of always meeting the current well bore and reservoir conditions without having to have a user make these changes and determine what the downtime for the well is. Autonomously modulating the idle time for a well, if done properly will either reduces incomplete fillage pump strokes, in cases where the idle time is too short, or will increase the wells production in cases where the idle time is too long. Overall this will result in the optimization of wells by reducing failures and/or increasing production, generating a huge value to the end user by automating the entire process of downtime optimization.

Funder: Engineering and Physical Sciences Research Council; doi: http://dx.doi.org/10.13039/501100000266, Electrified railways are becoming a popular transport medium and these consume a large amount of electrical energy. Environmental concerns demand reduction in energy use and peak power demand of railway systems. Furthermore, high transmission losses in DC railway systems make local storage of energy an increasingly attractive option. An optimisation framework based on genetic algorithms is developed to optimise a DC electric rail network in terms of a comprehensive set of decision variables including storage size, charge/discharge power limits, timetable and train driving style/trajectory to maximise benefits of energy storage in reducing railway peak power and energy consumption. Experimental results for the considered real-world networks show a reduction of energy consumption in the range 15%–30% depending on the train driving style, and reduced power peaks.

The subject of research is the navigation subsystem of autonomous control system to determine the location and position of agricultural machinery during the movement. The purpose of the work is to develop and research model and algorithms to determine the location and position of mobile agricultural machinery using a physical model. The following tasks are solved in the article: development of agricultural machinery physical model to collect information from sensors during movement, further development and research of applicability of algorithms for location and position determination. The following methods are used: methods of mathematical statistics, methods of information systems theory and data processing, methods of random signals filtration. The following results were obtained: during research, the agricultural machinery physical model to collect information from sensors during movement was created. The model includes a GPS receiver, an accelerometer, gyroscope and infrared encoders, to count the rotation of the wheels, as well as its own four wheelbase of agricultural machinery. The modernized GPS coordinate filtration algorithm using a geochex algorithm is proposed, which according to several successively obtained GPS coordinates calculates the hash received coordinates; if the coordinates have the same hash, it can be argued that the vehicle is in the segment of the area that corresponds to this hash. To determine the physical model position during the movement data from the accelerometer and the gyroscope was processed using Savitzky-Golay and Madgwick filters. With the use of wheels’ rotation data, the odometric algorithms for movement and location determining of the agricultural machinery physical model in motion were implemented. Conclusions: to improve the accuracy of estimating the location and position agricultural machinery, algorithms complexation of indicators from different navigation systems should be used to reduce the total error. Research results can be applied in the development of new and modifications of existing navigation subsystems of agricultural machinery autonomous control systems., Предметом исследования является навигационная подсистема системы автоматизированного управления для определения местонахождения и положения сельскохозяйственной техники во время движения. Цель работы – разработка и исследование модели и алгоритмов для определения местонахождения и положения мобильной сельскохозяйственной техники с использованием физической модели. В статье решаются следующие задачи: разработка физической модели сельскохозяйственной техники для сбора информации от датчиков во время движения, дальнейшая разработка и исследование применимости алгоритмов определения местоположения и положения. Используются следующие методы: методы математической статистики, методы теории информационных систем и обработки данных, методы фильтрации случайных сигналов. Получены следующие результаты: в ходе проведенных исследований была создана физическая модель сельскохозяйственного транспортного средства для сбора информации с датчиков во время движения. В состав модели входят GPS-приемник, акселерометр, гироскоп и инфракрасные энкодеры, для подсчета оборотов колес, а также сама четырехколесная база транспортной техники сельскохозяйственного назначения. Был предложен модернизированный алгоритм фильтрации GPS-координат с использованием алгоритма хеширования геохекс, который по нескольким последовательно полученным GPS-координатами выполняет расчет хеша полученных координат; если координаты имеют общий хэш, то можно утверждать, что транспортное средство находится в сегменте площади, которой соответствует данный хэш. Для определения положения физической модели во время движения данные от акселерометра и гироскопа было обработаны с помощью фильтров Савицкого-Голея и Маджвика. С применением данных об обращении колес был реализован одометрический алгоритм определения перемещения и местонахождения физической модели сельскохозяйственного транспортного средства во время движения. Выводы: для повышения точности оценки местонахождение и положение сельскохозяйственных транспортных средств следует использовать алгоритмы комплексирования данных от различных навигационных систем для уменьшения суммарной погрешности. Результаты исследования могут быть применены при разработке новых и модификации существующих навигационных подсистем автономных систем управления сельскохозяйственной техники., Предметом дослідження є навігаційна підсистема системи автономного управління для визначення місцезнаходження та положення сільськогосподарської техніки під час руху. Мета роботи – розробка та дослідження моделі і алгоритмів для визначення місцезнаходження та положення мобільної сільськогосподарської техніки з використанням фізичної моделі. В статті вирішуються наступні завдання: розробка фізичної моделі сільськогосподарської техніки для збору інформації від датчиків під час руху, подальша розробка та дослідження застосовності алгоритмів визначення місцезнаходження та положення. Використовуються такі методи: методи математичної статистики, методи теорії інформаційних систем та обробки даних, методи фільтрації випадкових сигналів. Отримано наступні результати: під час проведених досліджень було створено фізичну модель сільськогосподарського транспортного засобу для збору інформації з датчиків під час руху. До складу моделі входять GPS-приймач, акселерометр, гіроскоп та інфрачервоні енкодери, для підрахунку обертів коліс, а також сама чотирьохколісна база транспортної техніки сільськогосподарського призначення. Було запропоновано модернізований алгоритм фільтрації GPS-координат з використанням алгоритму хешування геохекс, який за декількома послідовно отриманими GPS-координатами виконує розрахунок хешу отриманих координат; якщо координати мають спільний хеш, то можна стверджувати, що транспортний засіб знаходиться в сегменті площі, якій відповідає даний хеш. Для визначення положення фізичної моделі під час руху дані від акселерометру та гіроскопу було оброблено за допомогою фільтрів Савіцького-Голея та Маджвіка. Із застосуванням даних про обернення коліс було реалізовано одометричні алгоритми визначення переміщення та місцезнаходження фізичної моделі сільськогосподарського транспортного засобу під час руху. Висновки: для підвищення точності оцінки місцезнаходження і положення сільськогосподарських транспортних засобів слід використовувати алгоритми комплексування показників від різних навігаційних систем для зменшення сумарної похибки. Результати дослідження можуть бути застосовані при розробці нових, та модифікації існуючих навігаційних підсистем автономних систем управління сільськогосподарської техніки.

Despite recent advances in robotic technology, sewer pipe inspection is still limited to conventional approaches that use cable-tethered robots. Such commercially available tethered robots lack autonomy, and their operation must be manually controlled via their tethered cables. Consequently, they can only travel to a certain distance in pipe, cannot access small-diameter pipes, and their deployment incurs high costs for highly skilled operators. In this paper, we introduce a miniaturised mobile robot for pipe inspection. We present an autonomous control strategy for this robot that is effective, stable, and requires only low-computational resources. The robots used here can access pipes as small as 75 mm in diameter. Due to their small size, low carrying capacity, and limited battery supply, our robots can only carry simple sensors, a small processor, and miniature wheel-legs for locomotion. Yet, our control method is able to compensate for these limitations. We demonstrate fully autonomous robot mobility in a sewer pipe network, without any visual aid or power-hungry image processing. The control algorithm allows the robot to correctly recognise each local network configuration, and to make appropriate decisions accordingly. The control strategy was tested using the physical micro robot in a laboratory pipe network. In both simulation and experiment, the robot autonomously and exhaustively explored an unknown pipe network without missing any pipe section while avoiding obstacles. This is a significant advance towards fully autonomous inspection robot systems for sewer pipe networks.

At present, the UAV swarm positioning solution has the problems of poor positioning accuracy and instability. Therefore, it is necessary to design a sliding mode formation controller to realize formation. This study analyzes the self-service control strategy of the UAV swarm and establishes the behavior-based formation control strategy as the main research point of this article. This paper combines the Internet of Things and artificial intelligence algorithms to build an autonomous control model for the UAV swarm and designs the UAV formation control law from the disturbed and undisturbed conditions respectively. With reference to the basic architecture of the Internet of Things, this study imitates ZigBee’s self-organizing network to propose an adaptive networking scheme based on the Internet of Things by using the AP+STA working mode of the Internet of Things module in the node device. The results of the experiment show that the positioning accuracy of the UAV is high, which can meet the needs of cluster flight. Based on the Z-axis coordinates of the UAV, the accuracy of the laser distance measurement and the barometer value is significantly improved, the root mean square error is reduced, and the positioning result is significantly better than the data of direct traditional positioning.

Any phenomenon in nature is potential to be an inspiration for us to propose new ideas. Lateral line is a typical example which has attracted more interest in recent years. With the aid of lateral line, fish is capable of acquiring fluid information around, which is of great significance for them to survive, communicate and hunt underwater. In this paper, we briefly introduce the morphology and mechanism of the lateral line first. Then we focus on the development of artificial lateral line which typically consists of an array of sensors and can be installed on underwater robots. A series of sensors inspired by the lateral line with different sensing principles have been summarized. And then the applications of artificial lateral line systems in hydrodynamic environment sensing and vortices detection, dipole oscillation source detection, and autonomous control of underwater robots have been reviewed. In addition, the existing problems and future foci in this field have been further discussed in detail. The current works and future foci have demonstrated that artificial lateral line has great potentials of applications and contributes to the development of underwater robots.

Microgels are soft colloids that show responsive behavior and are easy to functionalize for applications. They are considered key components for future smart colloidal material systems. However, so far microgel systems have almost exclusively been studied in classical responsive switching settings using external triggers, while internally organized, autonomous control mechanisms as found in supramolecular chemistry and DNA nanotechnology relying on fuel‐driven out‐of‐equilibrium concepts have not been implemented into microgel systems. Here, we introduce chemically fueled transient volume phase transitions (VPTs) for poly(methacrylic acid) (PMAA) microgels, where the collapsed hydrophobic state can be programmed using the fuel concentration in a cyclic reaction network. We discuss details of the system behavior as a function of pH and fuel amount, unravel kinetically trapped regions and showcase transient encapsulation and time‐programmed release as a first application., Moving past classical passive responsive behavior, transient volume phase transitions of poly(methacrylic acid) microgels can be achieved through the use of a chemical fuel. The deswelling creates a transient hydrophobic environment with a lifetime tunable via pH and fuel concentration with possible applications for encapsulation/release.

The purpose of this research is to implement the Artificial Neural Network (ANN) method in combat robots so it can be directed to shoot targets well. The robot control system uses remote control and autonomous. In the autonomous robot system, ANN back propagation method is applied, where the weight value variable depends on ultrasonic sensor, GPS and camera. The microcontroller system will process automatically depending on the sensor input. Output data is used to direct the robot to the target, tracking and shooting. Robot is used chain wheel systems and weapons that used pistol types. The riffle is mounted on the robot can be moved mechanically azimuth and the elevation towards the target then triggered mechanically by the riffle through the activation of data relays from the microcontroller. Thus, the backpropagation method can be applied to robots so it can be functioned autonomously.

Stable and accurate attitude estimation is the key to the autonomous control of unmanned aerial vehicle. The Attitude Heading Reference System using micro-electro-mechanical system inertial measurement unit and magnetic sensor as measurement sensors is an indispensable system for attitude estimation of the unmanned aerial vehicle. Aiming at the problem of low precision of the Attitude Heading Reference System caused by the nonlinear attitude model of the micro unmanned aerial vehicle, an attitude heading reference algorithm based on cubature Kalman filter is proposed. Aiming at the nonlocal sampling problem of cubature Kalman filter, the transformed cubature Kalman filter using orthogonal transformation of the sampling point is presented. Meanwhile, an adaptive estimation algorithm of motion acceleration using Kalman filter is proposed, which realizes the online estimation of motion acceleration. The car-based tests show that the algorithm proposed in this paper can accurately estimate the carrier’s motion attitude and motion acceleration without global positioning system. The accuracy of acceleration reaches 0.2 m/s2, and the accuracy of attitude reaches 1°.

Unmanned aerial vehicles (UAVs) are usually controlled by radio or by autonomous control algorithms. Compared with manned aerial vehicles, they have great advantages in performing dangerous tasks but, presently, no UAV system can cope with high-intensity air combat. In addition, the robustness of a single UAV cannot be guaranteed in air combat missions; on the other hand, a multi-UAV system not only ensures this robustness but also improves the mission success rate by using saturation attacks. Therefore, this paper presents a multi-UAV air combat system based on swarm intelligence. Considering the problem of multi-UAV cooperative arrival at the air battlefield and the accomplishment of combat tasks, the aerodynamic model of the aircrafts and the threat area on the path to the battlefield are modeled; the path planning is completed through an ant colony algorithm. Based on the control algorithm of single-UAV finite-state machine and the cooperation of multiple UAVs, an autonomous control algorithm for multi-UAV systems is proposed to improve the success rate of UAV clusters in air combat. The effectiveness of the proposed algorithm is tested with a simulation platform.

Haptic exploration is a process of using haptic feedback to interact and perceive an unknown object. It is an essential approach to understand the physical and geometrical properties of the object. While numerous research has been carried out for haptic exploration on static objects, haptic exploration on objects with dynamic movements has not been reported. It is due to the significant challenges to achieve robust force and velocity control when the object is nonstationary. In this work, a novel adaptive force and velocity control algorithm based on intrinsic contact sensing (ICS) for haptic surface exploration of dynamic objects is presented. A fuzzy-logic control framework making use of the information obtained from ICS has been developed. To validate the proposed control algorithm, extensive surface exploration experiments have been carried out on objects with different surface properties, geometries, stiffness, and concave or convex patterns. The validation results demonstrate the high accuracy and robustness of the proposed algorithm using different experimental platforms.

This article proposes stochastic exploration algorithms for mobile robot exploration problems. Navigation with uncertain conditions in the absence of initial parameters is a situation wherein precomputation and prediction are impossible for a robot. Therefore, stochastic optimization techniques were applied to find the optimal solution for the robot exploration problem. Driving to the unknown areas, the robot updates the frontier line of sensor visibility during the exploration mission. The points of the frontier line are assumed as the swarm population with their own positions and costs, which allows the computation of the next global waypoint. The calculation of global waypoints is carried out by a nature-inspired optimization algorithm that can place a waypoint in uncertainties. This study offers to apply three metaheuristic algorithms individually, such as Whale Optimization, Grey Wolf Optimizer, and Particle Swarm Optimization algorithms, for comparison and testing their performances in the mobile robotics. At first, the simulations based on the proposed exploration algorithms were implemented and evaluated in a created environment. The results were compared in a single and average cases. Then, the real-world experiments using Grey Wolf Optimizer exploration algorithm were conducted in the different types of environments using MATLAB-ROS integration tool. These results proved the effectiveness and applicability of the bio-inspired optimization algorithm in the mobile robotics.

The approach of self-organized and autonomous controlled systems offers great potential to meet new requirements for the economical production of customized products with small batch sizes based on a distributed, flexible management of dynamics and complexity within the production and intralogistics system. To support the practical application of self-organization for intralogistics systems, a catalogue of criteria for the evaluation of the self-organization of flexible logistics systems has been developed and validated, which enables the classification of logistics systems as well as the identification and evaluation of corresponding potentials that can be achieved by increasing the degree of self-organization.

In this study, an experimental 1:43-scale car was developed to verify a vehicle control strategy for autonomous race cars. The power slide driving technique was employed as a maneuver to overcome sharp corners at high speed. To develop a proper control strategy for autonomous race cars, the experimental scale car trajectories when driven by a human driver were analyzed. The side slip angle and the turn rate during sharp cornering were used as reference values for the developed autonomous control method that has a hierarchical architecture. The slip-based control method was employed as a high-level controller to stabilize the vehicle state when turning sharp corners. The performance of the developed control strategy was experimentally verified using a real-time environmental 1:43-scale car.

A nonlinear control technique for autonomous control of a tri-rotor unmanned aerial vehicle is presented in this paper. First, a comprehensive mathematical model is developed using the Newton–Euler approach for a tri-rotor, which is found to be highly nonlinear and coupled. Then, the equivalent input affine model is extracted by applying a suitable transformation. Finally, the sliding mode control for trajectory tracking is chosen which is immune to matched external disturbances, parametric uncertainties, and modeling errors. The proposed controller performance has been verified for appropriate inputs under wind disturbances using MATLAB, and the simulation results are presented.

Smart Building/Environment management is an interesting topic that, although widely investigated in the literature, has not seen wide adoption in real case studies. Current Smart Building solutions provide facilities for: i) automatically managing HVAC (Heating, Ventilation and Air Conditioning) systems; ii) energy management, iii) building automation. Current solutions are tightly coupled with the underlying IT infrastructure of the building and cannot be rearranged according to the events, catastrophic or otherwise, that may modify the building structure (e.g., a disruption to sections of a building). In this work, we present a novel approach for Smart Buildings (we refer to it as "Software Defined Building 2.0") to customize and reprogram IT infrastructure powering buildings, this way enabling cooperation among Cyber-Physical Systems (CPSs) operating within. We analyze an architecture enabling this approach, evaluating overhead management and its impact on systems’ performance.

With the rapid development of Unmanned Aerial Vehicle (UAV) technology, one of the emerging fields is to utilize multi-UAV as a team under autonomous control in a complex environment. Among the challenges in fully achieving autonomous control, cooperative task assignment stands out as the key function. In this paper, we analyze the importance and difficulties of multi-UAV cooperative task assignment in characterizing scenarios and obtaining high-quality solutions. Furthermore, we present three promising directions for future research: cooperative task assignment in a dynamic complex environment, in an unmanned-manned aircraft system and in a UAV swarm. Our goal is to provide a brief review of multi-UAV cooperative task assignment for readers to further explore.

The present work is focused on the development of a Virtual Environment as a test system for new advanced control algorithms for an Unmanned Aerial Vehicles. The virtualized environment allows us to visualize the behavior of the UAV by including the mathematical model of it. The mathematical structure of the kinematic and dynamic models is represented in a matrix form in order to be used in different control algorithms proposals. For the dynamic model, the constants are obtained experimentally, using a DJI Matrice 600 Pro UAV. All of this is conducted with the purpose of using the virtualized environment in educational processes in which, due to the excessive cost of the materials, it is not possible to acquire physical equipment; moreover, is it desired to avoid damaging them. Finally, the stability and robustness of the proposed controllers are determined to ensure analytically the compliance with the control criteria and its correct operation.

The alignment stability and controllability deteriorate considerably when the air spring vibration isolation system for the high power density main engine (HPDME-ASVIS) is in a special running state of high power and high output torque. The coupling characteristics among the alignment components are obtained by analyzing the mechanical characteristics of HPDME-ASVIS. A control response model is established, which can predict the alignment state and the vertical displacement of air springs after k times of inflation/deflation. By solving the linear programming problem, the alignment controllability judgment models are constructed for two conditions, which are under hard constraints and soft constraints, respectively. Furthermore, alignment reconfigurability is described and an autonomous control method is established. The experimental results confirm that high output torque will cause large and serious coupling of horizontal offset, self-rotation angle, and vertical deformation of air springs. Meanwhile, hey cannot reach the given control precision range. The traditional control method under hard constraints will result in a state of oscillation and may bring many reliability problems to HPDME-ASVIS. The alignment reconfigurability analysis can judge the alignment control influence degree based on different torque accurately. The autonomous control can use the control parameters autonomous adjustment and the spatial configuration to realize the alignment control reconfigurability for two different alignment control fault states. The stability of the alignment control system in HPDME-ASVIS can be improved observably, which is of great significance to the engineering application of the high power density main engine (HPDME) in the field of marine propulsion technology.

zetce- Otonom sistemler son zamanlarda ozellikle coklu hava araci operasyonlari icin yeni firsatlar sunmaktadir. Pilotlar tarafindan yerine getirilmesi gereken bircok gorev, otonom olarak da gerceklestirilebilir. Bununla birlikte, kol ucusu yapan hava araclarinin otonomisi karmasik kontrol sistemlerini icermektedir. Bu calismanin temel amaci, otonom kol ucusu icin gerekli yapmaktir, Bunun icin bir lider ve bir takipci ucagi goz onune alinmistir. Takipci ucagin lider ucagi takip etmesini saglayan tamamen otonom bir kontrol sistemi tasarlanmistir. Lider ucagm neden oldugu girdap etkisi de dikkate alinarak yorungenin izlenmesi yeni bir geometrik algoritma gelistirilerek saglanmistir. Tasarlanan sistem kol ucusunu otomatik olarak kontrol etmektedir. Anahtar Kelimeler - kol ucusu; PID kontrol; rota takibi; girdap modeli. Abstract - Recently swarm technology has provided new opportunities especially for aerial vehicle operations. Many missions have to be accomplished by pilots could be completed autonomously. However, autonomy of cooperated aerial vehicles consists of important complexities in control systems. The main purpose of this study is to design required autopilots for autonomous formation flight. For that, a leader and a follower aircraft are considered. A fully autonomous control system that provides the follower aircraft to track the leader aircraft is designed. Trajectory tracking is accomplished by a new geometric algorithm under effect of vortex caused by leader aircraft. The designed system controls formation flight autonomously.

Commonly, surveillance activities on lake waters is mostly carried out by using a surface vehicle as special-designed vehicle, especially to conduct water quality measurements, underwater surveys, and bathymetry mapping. However, conventional survey and monitoring still involves humans on the site. If a survey is conducted during strong wind conditions, it could jeopardize surveyor’s safety. Therefore, a vehicle must have several criteria, e.g., it must be pretty spacious and comfortable to carry surveyors, free from engine vibrations, stabilized and easy to maneuver, and the surveyor's safety can be guaranteed. This paper discusses preliminary research aiming to develop an Autonomous Raft Vehicle (ARV), a type of autonomous unmanned surface vehicle. The ARV is equipped with autonomous control based on multi-way-points with an A* algorithm. Thus, a user only requires giving a command once initially during path planning. A* algorithm over multi-way-point could improve ARV navigation when there are obstacles along the predetermined trajectory. Hence the predetermined trajectory will be maintained throughout the mission. It is a significant contribution to this paper.

Autonomous robotic suturing has the potential to improve surgery outcomes by leveraging accuracy, repeatability, and consistency compared to manual operations. However, achieving full autonomy in complex surgical environments is not practical and human supervision is required to guarantee safety. In this paper, we develop a confidence-based supervised autonomous suturing method to perform robotic suturing tasks via both Smart Tissue Autonomous Robot (STAR) and surgeon collaboratively with the highest possible degree of autonomy. Via the proposed method, STAR performs autonomous suturing when highly confident and otherwise asks the operator for possible assistance in suture positioning adjustments. We evaluate the accuracy of our proposed control method via robotic suturing tests on synthetic vaginal cuff tissues and compare them to the results of vaginal cuff closures performed by an experienced surgeon. Our test results indicate that by using the proposed confidence-based method, STAR can predict the success of pure autonomous suture placement with an accuracy of 94.74%. Moreover, via an additional 25% human intervention, STAR can achieve a 98.1% suture placement accuracy compared to an 85.4% accuracy of completely autonomous robotic suturing. Finally, our experiment results indicate that STAR using the proposed method achieves 1.6 times better consistency in suture spacing and 1.8 times better consistency in suture bite sizes than the manual results.

This paper presents a cost effective UV robot for applications of disinfecting hospital and factory spaces from Covid-19 and other communicable diseases. This is based on a disinfection method using a remotely controlled Ultra-Violet (UV) disinfection robot. The robot is equipped with 3 UVC lamps in a 360 degree beam coverage setting, on a mobile base which is equipped with motors, controllers and sensors that allow the lamps to move around and disinfect surfaces with ease. The robot can automatically detect human and or other types of non-living presence over which machine learning models are built that allows for some degree of autonomous control. Its main control unit is operated via WiFi using the mobile as a transceiver device allowing for remote safe control of UVC lamp disinfection.

The use of computer controlled swarms of UAVs for crop spraying enables non-uniform coverage of high precision and time efficiency. For this purpose an algorithmic control method for autonomous UAV swarm spraying, based on multi-agent area coverage method Heat Equation Driven Area Coverage (HEDAC), is proposed. Motion control relies on suitable spraying model which enables of multi-agent spraying simulations for arbitrary agent’s trajectories. The HEDAC control method was tested in comparison with conventional (Lawnmower) and state-of-the-art (SMC) path planning methods on three numerical tests: two based on simple geometries and algebraically defined spraying goal densities, and one based on a real-world crop disease map. Additionally, the effects of spraying tool design (number of nozzles and their spraying density) on spraying accuracy were analyzed, with results consistently illustrating the direct causation between tool precision and overall spraying error. The results of the testing have shown HEDAC control to be significantly faster than Lawnmower (approximately 35%–65% less time needed) and SMC (approximately 15%–50% less time needed) in achieving convergence, while producing spraying density of comparable accuracy. Moreover, HEDAC spraying typically mitigates over-spraying by approximately 3%–8% when compared with conventional path planning. In additional tests, it is shown that an implementation of collision avoidance technique for HEDAC motion control provides collision-free UAV swarm spraying. The effect of HEDAC collision avoidance control on the spraying convergence rate and accuracy is practically insignificant. It may be concluded that in real-world application HEDAC controlled UAV spraying swarms are expected to significantly outperform UAVs operating with existing path planning methods.

The application of Industry 4.0 concepts to the digitization of machine tools is considered. Organizational and technological principles fundamental to new machine-tool properties, including smart and autonomous control, are outlined. The creation of digital images and digital twins of machine tools simulating their internal processes is discussed. The characteristics and behavior of machine tools over the whole life cycle are analyzed.

Microgrids serve as an integral part of future power distribution systems. Typically, microgrids are managed by centralised controllers. There are two major concerns about using a single centralised controller. The controller can become a performance and reliability bottleneck for the entire system, where its failure can bring the entire system down. Excessive communication delays can also degrade the system performance. As a solution, a true decentralised control architecture for microgrids is proposed, designed, developed, and tested here. Distributing the controls to local agents decreases the possibility of network congestion to occur. Decentralisation will also enhance the reliability of the system since the single point of failure is replaced by a distributed architecture. The proof-of-concept of true decentralisation of microgrid control architecture is implemented using Hardware-in-the-Loop Platform. Device level and system level controller and interaction models are defined for a self-coordination. Also, microgrid energy management system (EMS) and control case scenarios are demonstrated. The experimental results show the robustness of the proposed architecture.

We propose a trust and self-confidence-based autonomy allocation strategy to automatically choose between manual and autonomous control of (semi)autonomous mobile robots in guidance and navigation tasks. We utilize a performance-centric, computational trust and self-confidence model and automated autonomy allocation strategy, developed in our earlier work (H. Saeidi and Y. Wang, “Trust and self-confidence based autonomy allocation for robotic systems,” in Proc. 54th IEEE Conf. Decis. Control , 2015, pp. 6052–6057.), based on objective and unbiased performance measures for the human and the robot. A set of robot simulations with a human-in-the-loop is conducted for a teleoperated unmanned aerial vehicle tracking task. The results demonstrate that our allocation strategy can capture human autonomy allocation pattern with an accuracy of 64.05%. We also show that the strategy can improve the overall robot performance by 11.76% and reduce operator's workload by 10.07% compared to a manual allocation. Moreover, compared to a performance maximization strategy, our strategy is 23.42% more likely to be accepted and generally preferred and trusted by the participants. Furthermore, we design a decision pattern correction algorithm based on nonlinear model predictive control to help a human operator gradually adapt to a modified allocation pattern for improved overall performance.

The passive suspension of a six-wheeled self-propelled chassis is analyzed in terms of the influence of its kinematic and geometric parameters on the configuration, cross-country ability, and static stability of low-speed autonomous planetary rovers. The results of the synthesis of a new suspension scheme for a four-wheeled chassis are presented.

The unmanned aerial vehicle (UAV) has been a research hotspot worldwide. The UAV system is developing to be more and more intelligent and autonomous. UAV path planning is an important part of UAV autonomous control and the important guarantee of UAV's safety. For the purpose of improving the collision avoidance and path planning algorithms, the artificial potential field, fuzzy logic algorithm and ant colony algorithm are simulated respectively in the static obstacle and dynamic obstacle environments, and compared based on the minimum avoidance distance and range ratio. Meanwhile, an improved algorithm of artificial potential field is proposed, and the improvement helps the UAV escape the local minimum by introducing the vertical guidance repulsion. The simulation results are rigorous and reliable, which lay a foundation for the further fusion of multiple algorithms and improving the path planning algorithms.

The paper presents examples of solutions that can be used in vision systems designed for mobile robots, which allow to recognize the robot’s environment and to plan routes to specific destinations. Implemented algorithm can identify fiducial markers such as ARTag, by means of which the target places or intermediate points can be marked. The mobile robot can create a route on the basis of recognized markers. The paper also shows an example of vision system enabling autonomous operation of the control panel by computing a depth map. The algorithms of the vision system are implemented on the basis of the OpenCV library.

Simple control solutions for steering underactuated ships have been seldom studied in literature. This paper provides a control method for automatic steering of underactuated ships in presence of disturbances and in uncertain environmental conditions. A mathematical model of ship dynamics is developed and validated. Our control method involves introduction of a preparation phase consisting of three algorithms. Waypoints leading to every desired set-point are defined and a continuous Dubins path is generated between these waypoints. A second-order motion profile is assigned to the path to suggest the speed with the best timing for the voyage. The control objective is reformulated from a tracking problem to a regulating problem by changing coordinates of the reference signal. PD-controllers are employed in closed loop to reduce position and heading error. The controller parameters are set in the design phase and do not need additional tuning for different trajectories and ship parameters. Weather information together with the planned motion profile are used in a feedforward controller design to achieve better disturbance rejection and higher accuracy. Performance and robustness of the design are evaluated in simulation by Course-keeping and Course-changing experiments in extreme and uncertain environmental conditions. While the developed control method is simple compared to the methods in literature, it can still achieve quite satisfactory steering performance.

Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicles (UAV) have been a high potential topic in the aerospace industry during the last decades due to its multirotor and fixed-wing nature of the aircraft. Besides, having the ability to rapidly deploy from a tight airstrip and gathering Intelligence, Surveillance, and Reconnaissance (ISR) information is the best way to be one step ahead of the enemy. In this paper, we present the implementation and development of gasoline-electric hybrid propulsion VTOL Unmanned Aerial vehicle respectively. The Hybrid propulsion VTOL UAV offers image and real-time video transmission to the ground station with fully autonomous control to get the best view of the enemy from the sky. The gasoline-electric hybrid propulsion system provides long flight endurance with efficient power consumption. The fundamentals of the multirotor and the conventional fixed-wing aircraft present the theoretical background of the aircraft. The accomplished design consists of high-performance multirotor motors with an efficient gasoline engine. Furthermore, the control system architecture, avionics, and power distribution system presented with addressing cost-effective trending design techniques. The performance of the system has been improved using commercially off-the-shelf (COTS) hardware.

The advent of micro/nanorobotics promises to transform the physical, chemical, and biological domains by harnessing opportunities otherwise limited by size. Most notable is the biomedical field in which the ability to manipulate micro/nanoparticles has numerous applications in biophysics, drug delivery, tissue engineering, and microsurgery. Acoustics, the physics of vibrational waves through matter, offers a precise, accurate, and minimally invasive technique to manipulate microrobots or microparticles (stand-ins for microrobots). One example is through the use of flexural vibrations induced in resonant structures such as Chladni plates. In this research, we developed a platform for precise two-dimensional microparticle manipulation via acoustic forces arising from Chladni figures and resonating microscale membranes. The project included two distinct phases: (1) macroscale manipulation with a Chladni plate in air and (2) microscale manipulation using microscale membranes in liquid. In the first phase (macroscale in air), we reproduced previous studies in order to gain a better understanding of the underlying physics and to develop control algorithms based on statistical modeling techniques. In the second phase (microscale in liquid), we developed and tested a new setup using custom microfabricated structures. The macroscale statistical modeling techniques were integrated with microscale autonomous control systems. It is shown that control methods developed on the macroscale can be implemented and used on the microscale with good precision and accuracy.

In recent years, the field of artificial intelligence has shown significant progress in solving problems that seemed impossible just a few years ago: from the real-time autonomous control of super-pressure balloons in the stratosphere to reducing the energy required for cooling a data centre, and from training robots to acquire skills that generalize effectively to diverse real-world objects and situation to improving mobile phone battery use and screen brightness for millions of people. Underlying all these real-world success stories is a collection of methods known as deep reinforcement learning which has proven superior at finding near-optimal policies of behaviors. We envision that combining deep reinforcement learning with automatic target recognition, which extracts actionable military information from sensor data, will have a meaningful impact on the future of warfare.

We propose an autonomous flight control method for secure navigation of Unmanned Aerial Vehicles (UAVs). We focus on the relation between flight command given to UAV and the response. According to the flight commands sampled in world coordinate system, we acquire the corresponding 3D moving vectors, which represent moving distances and orientations in 3D space, by employing motion capture system. As the input/output relation implemented in UAV is a black box, our approach acquires the sequential input/output relation between flight command and 3D moving vector by using recurrent neural network. Given sequential flight commands, the model predicts the 3D moving vectors that correspond to the command sequence. We demonstrate that our proposed method yields the desirable command sequence for autonomous control.

As robots and autonomous systems become more adept at handling complex scenarios, their underlying mechanisms also become increasingly complex and opaque. This lack of transparency can give rise to unverifiable behaviours, limiting the use of robots in a number of applications including high-stakes scenarios, e.g. self-driving cars or first responders. In this paper and accompanying video, we present a system that learns from demonstrations to inspect areas in a remote environment and to explain robot behaviour. Using semi-supervised learning, the robot is able to inspect an offshore platform autonomously, whilst explaining its decision process both through both image-based and natural language-based interfaces.