Autonomous Control System Research Articles

Study On The Application Of Thermoelectric Coolers Inside Unmanned Surface Vehicles

The development of unmanned surface vehicles for military and commercial needs is increasing as the development of autonomous control systems. The farther the operation range of unmanned surface vehicles makes the propulsion motor generated heat and decreased the performance of the vehicle. This study aims to analyze the application of a thermoelectric cooler to decrease the temperature of the electric motors as a propulsion system on the unmanned surface vehicle. The research was carried out by prototyping the thermoelectric cooler 12V and tested at the prototype of unmanned surface vehicles with a length overall 1.5 m. The results showed the application of a thermoelectric cooler potentially effective to decrease the electric motor temperature by as much as 26.3 ?C by neglect the heat loss due to convection. The results of this study contributed to the development of reliable unmanned surface vehicles.

Autonomous Guidance and Flight Control on a Partial-Authority Black Hawk Helicopter

An autonomous guidance and flight control system was integrated and flight tested on a Black Hawk helicopter as part of an effort to provide all weather capability for U.S. Army fleet rotorcraft. The guidance and flight-control system components were previously flight tested on a full-authority helicopter, and were adapted to fly on the partial-authority test helicopter of this paper. The main autonomy guidance components consisted of the Risk Minimizing Obstacle Field Navigation algorithm, Safe Landing Area Determination algorithm, Mission Manager, and Integrated Cueing Environment. These components provided reactive obstacle avoidance guidance, landing site selection, pilot/autonomy interaction, and pilot situational awareness for degraded visual environments. In addition to the autonomy components, the Autonomous Partial-Authority Flight Control System provided a fully stabilized path-following capability to the guidance components. This paper describes how these components were adapted to a partial-authority helicopter that is typical of the current U.S. Army fleet. To test the system, a laser detection and ranging unit was used as a surrogate ranging device in lieu of an all-weather sensor system that was concurrently under development. Flight test results are presented from the fully integrated system navigating through terrain, selecting landing sites, and autonomously landing, while simultaneously keeping the pilot situationally aware of the autonomy’s intent.

A Novel Fault Detection, Identification and Prediction Approach for Autonomous Vehicle Controllers Using SVM

Faults that develop in vehicle sensors have the potential to propagate unchecked throughout control systems if undetected. Automatic fault diagnosis and health monitoring algorithms will become necessary as automotive applications become more autonomous. The current fault diagnosis systems are not effective for complex systems such as autonomous cars where the case of simultaneous faults in different sensors is highly possible. Therefore, this paper proposes a novel fault detection, isolation and identification architecture for multi-fault in multi-sensor systems with an efficient computational burden for real-time implementation. Support Vector Machine techniques are used to detect and identify faults in sensors for autonomous vehicle control systems. In addition, to identify degrading performance in a sensor and predict the time at which a fault will occur, a novel predictive algorithm is proposed. The effectiveness and accuracy of the architecture in detecting and identifying multiple faults as well as the accuracy of the proposed predictive fault detection algorithm are verified through a MATLAB/IPG CarMaker co-simulation platform. The results present detection and identification accuracies of 94.94% and 97.01%, respectively, as well as a prediction accuracy of 75.35%.

Magnifier: A Compositional Analysis Approach for Autonomous Traffic Control

Autonomous traffic control systems are large-scale systems with critical goals. To satisfy expected properties, these systems adapt themselves to possible changes in their environment and in the system itself. The adaptation may result in further changes propagated throughout the system. For each change and its consequent adaptation, assuring the satisfaction of properties of the system at runtime is important. A prominent approach to assure the correct behavior of these systems is verification at runtime, which has strict time and memory limitations. To tackle these limitations, we propose Magnifier, an iterative, incremental, and compositional verification approach that operates on an actor-based model where actors are grouped in components, and components are augmented with a coordinator. The Magnifier idea is zooming on the area (component) affected by a change and verifying the correctness of properties of interest of the system after adapting the component to the change. Magnifier checks if the change is propagating, and if that is the case, then it zooms out to perform adaptation on a larger area to contain the change. The process is iterative and incremental, and considers areas affected by the change one by one. In Magnifier, we use the Coordinated Adaptive Actor model (CoodAA) for traffic control systems. We present a formal semantics for CoodAA as a network of Timed Input-Output Automata (TIOAs), and prove the correctness of our compositional reasoning. We implement our approach in Ptolemy II. The results of our experiments indicate that the proposed approach improves the verification time and the memory consumption compared to the non-compositional approach.

Разработка цифрового датчика пальцевой фотоплетизмограммы

Background and Objectives: Due to the development of methods for analyzing signals of autonomous blood circulation control, cardiovascular system disorders can be diagnosed today in the early stages. It is promising to use specialized devices for personalized diagnosis of the cardiovascular system and monitoring its state. Research on autonomous blood circulation control systems is a complex problem both from the point of view of physiology and radiophysics. Its solution requires the development of methods and specialized devices for the analysis and registration of signals from the cardiovascular system. Therefore the object of research is the development of a photoplethysmogram sensor with a digital communication channel with a band of 0.05-30 Hz, recording the signals from the autonomous blood circulation monitoring system. Materials and Methods: To compare the level of noise and nonlinear distortions in the center of the frequency range of interest to us (at a frequency of 0.1 Hz), the power spectra of the signals were analyzed, and the coherence function was also calculated. Results: a prototype of a device for recording and analyzing a photoplethysmogram signal was developed and implemented, which makes it possible to register the signals from the circuits of autonomous blood circulation regulation. A comparative analysis of the developed device with a serial analog sensor was carried out, which demonstrated the advantages of the developed device. Conclusion: The developed broadband digital sensor can be used in wearable devices to diagnose the functional state of the cardiovascular system based on the analysis of synchronization between the circuits of autonomous regulation of blood circulation.

An Intelligent Task Scheduling Mechanism for Autonomous Vehicles via Deep Learning

With the rapid development of the Internet of Things (IoT) and artificial intelligence, autonomous vehicles have received much attention in recent years. Safe driving is one of the essential concerns of self-driving cars. The main problem in providing better safe driving requires an efficient inference system for real-time task management and autonomous control. Due to limited battery life and computing power, reducing execution time and resource consumption can be a daunting process. This paper addressed these challenges and developed an intelligent task management system for IoT-based autonomous vehicles. For each task processing, a supervised resource predictor is invoked for optimal hardware cluster selection. Tasks are executed based on the earliest hyper period first (EHF) scheduler to achieve optimal task error rate and schedule length performance. The single-layer feedforward neural network (SLFN) and lightweight learning approaches are designed to distribute each task to the appropriate processor based on their emergency and CPU utilization. We developed this intelligent task management module in python and experimentally tested it on multicore SoCs (Odroid Xu4 and NVIDIA Jetson embedded platforms). Connected Autonomous Vehicles (CAV) and Internet of Medical Things (IoMT) benchmarks are used for training and testing purposes. The proposed modules are validated by observing the task miss rate, resource utilization, and energy consumption metrics compared with state-of-art heuristics. SLFN-EHF task scheduler achieved better results in an average of 98% accuracy, and in an average of 20–27% reduced in execution time and 32–45% in task miss rate metric than conventional methods.

A LiDAR Sensor-Based Spray Boom Height Detection Method and the Corresponding Experimental Validation.

Sprayer boom height (Hb) variations affect the deposition and distribution of droplets. An Hb control system is used to adjust Hb to maintain an optimum distance between the boom and the crop canopy, and an Hb detection sensor is a key component of the Hb control system. This study presents a new, low-cost light detection and ranging (LiDAR) sensor for Hb detection developed based on the principle of single-point ranging. To examine the detection performance of the LiDAR sensor, a step height detection experiment, a field ground detection experiment, and a wheat stubble (WS) height detection experiment as well as a comparison with an ultrasonic sensor were performed. The results showed that the LiDAR sensor could be used to detect Hb. When used to detect the WS height (HWS), the LiDAR sensor primarily detected the WS roots and the inside of the WS canopy. HWS and movement speed of the LiDAR sensor (VLiDAR) has a greater impact on the detection performance of the LiDAR sensor for the WS canopy than that for the WS roots. The detection error of the LiDAR sensor for the WS roots is less than 5.00%, and the detection error of the LiDAR sensor for the WS canopy is greater than 8.00%. The detection value from the LiDAR sensor to the WS root multiplied by 1.05 can be used as a reference basis for adjusting Hb, and after the WS canopy height is added to the basis, the value can be used as an index for adjusting Hb in WS field spraying. The results of this study will promote research on the boom height detection method and autonomous Hb control system.

A novel hybrid particle swarm optimization for multi-UAV cooperate path planning

The path planning of unmanned aerial vehicle (UAV) in three-dimensional (3D) environment is an important part of the entire UAV’s autonomous control system. In the constrained mission environment, planning optimal paths for multiple UAVs is a challenging problem. To solve this problem, the time stamp segmentation (TSS) model is adopted to simplify the handling of coordination cost of UAVs, and then a novel hybrid algorithm called HIPSO-MSOS is proposed by combining improved particle swarm optimization (IPSO) and modified symbiotic organisms search (MSOS). The exploration and exploitation abilities are combined efficiently, which brings good performance to the proposed algorithm. The cubic B-spline curve is used to smooth the generated path so that the planned path is flyable for UAV. To assess performance, the simulation is carried out in the virtual three-dimensional complex terrain environment. The experimental results show that the HIPSO-MSOS algorithm can successfully generate feasible and effective paths for each UAV, and its performance is superior to the other five algorithms, namely PSO, Firefly, DE, MSOS and HSGWO-MSOS algorithms in terms of accuracy, convergence speed, stability and robustness. Moreover, HIPSO-MSOS performs better than other tested methods in multi-objective optimization problems. Thus, the HIPSO-MSOS algorithm is a feasible and reliable alternative for some difficult and practical problems.

Elements of the mathematical support for the design of an autonomous tractor

The article discusses the issue of substantiating the requirements for the location of passive benchmarks as part of a local complex of precision farming in its minimum configuration. As such, we consider a mobile chassis (for example, a wheeled agricultural tractor) equipped with an autonomous control system and a set of two or three passive benchmarks. References are given to works that reveal the technologies that are in demand in the design of the chassis of such a tractor and a description of the test equipment used in the design and debugging of units and assemblies of robotic machines. Within the framework of the concept under consideration, it is important that the complex combines the capabilities of autonomous, remote and manual control and is based on a mobile chassis that meets the necessary ergonomic requirements. The considered simplest system can be built into a structure that uses satellite positioning technologies and supports the principle of organizing multi-agent systems. The object of the research is a local complex of precision farming in the minimum composition: an autonomous agricultural tractor and a set of passive benchmarks. The purpose of the study is to determine the basic requirements for the selection of sites for the installation of passive benchmarks. The basic technology is the use of binary maps of permeability as a variant of the discrete working plan of an autonomous robotic complex. The technology for building maps of cross-country ability has been worked out when creating models of rovers and special vehicles. The paper illustrates the actual method of solving the problem in its mathematical interpretation, provides the main results and practical recommendations. It is shown that the proposed solutions can be extended to the areas of the forest engineering and logging industry, to the solution of logistic problems of the Far North, the territories of the Arctic and Antarctic.

Automatic braking system using fuzzy logic method

This research is design an autonomous car control system that can reduce the rate of traffic accident. Car prototype is designed by using Fuzzy Logic Control. Fuzzy logic is as main component of artificial intelligence has significantly influence the design controlled system. Fuzzy logic system is integrated with the arduino Mega 2560 microcontroller as the central control car prototype. In this paper, the Fuzzy logic used Mamdani method and 28 rule base. The fuzzy system was made based on fuzzy mechanism which are fuzzification, inference and defuzzification. Fuzzy logic has a variable input and output variables. Input variables used in this research is the distance between car and obstacle; the speed of car prototype and the output variable is the brake angle. The test is done with 5 variations of distance that is 100 cm, 125 cm, 150 cm, 175 cm, and 200 cm and two variations of speed that are 54 cm/s, and 63 cm/s. The test results showed that the prototype of the car was able to stop with a range of 22 cm - 35 cm from obstacle. The highest deceleration value at the speed of 63 cm/s is - 49.22 cm/s2 and the accuracy of braking on a system that has been designed is between 99.91% -100%.

Autonomous Elbow Controller for Differential Drive In-Pipe Robots

The inspection of legacy nuclear facilities to aid in decommissioning is a world wide issue. One of the challenges is the characterisation of pipe networks within them. This paper presents an autonomous control system for the navigation of these unknown pipe networks, specifically focusing on elbows. The controller utilises three low-cost feeler sensors to navigate the FURO II robot around 150 mm short elbows. The controller is shown to allow the robot to safely navigate around an elbow on all 39 attempts comparing that with the brute force method which only completed five of the nine attempts and damaging the robot. This shows the advantages of the proposed controller. A new metric (Impulse) is also proposed to compare the extra force applied to the robot over the time it is slipping in the elbow due to the errors in the drive unit speeds. Using this metric, the controller is shown to decrease the Impulse applied to the robot by 213.97 Ns when compared to the brute force method.

Versatile and high temperature spectroscopic cell for operando fluorescence and transmission x-ray absorption spectroscopic studies of heterogeneous catalysts.

A modular high-temperature cell consisting of a plug-flow microreactor with a fixed catalyst bed and long heating zone has been established for operando x-ray absorption/fluorescence spectroscopic and diffraction studies. The functionality of the cell is demonstrated for two important areas: emission control using 2 wt. % Pd/Al2O3 acting as a three-way catalyst and direct conversion of methane to olefins and aromatics on a 0.5% Fe/SiO2 catalyst. The performance has been determined by online infrared spectroscopy and mass spectrometry, respectively. In addition, the cell can be combined with optical spectroscopy, such as Raman spectroscopy. The catalyst, present as powdered/sieved samples, can be measured under reaction conditions at temperatures of up to 1050 °C. Another key aspect is a long isothermal heating zone with a small temperature gradient (<3 °C/mm at 1000 °C without reaction) including an inert zone for pre-heating of the reactant gas. Due to the small size of the microreactor and the heating system including a water cooling system, heating/cooling rates of up to 100 °C/min can be achieved. Moreover, due to the compact design and the autonomous control system, the high temperature operando setup fits to the space at the majority of synchrotron beamlines. In many cases, the concentration of the element of interest in the catalysts is low requiring x-ray absorption spectroscopy measurements in the fluorescence measurement mode. Hence, the microreactor was designed to fit such needs as well. More specifically, the case of Fe-containing catalysts was particularly considered by using iron-free materials for the reactor housing.

Adaptive Learning-Based Observer With Dynamic Inversion for the Autonomous Flight of an Unmanned Helicopter

This article presents the development of an autonomous flight control system for a small-scale unmanned helicopter based on an online adaptive learning-based observer and model predictive control (MPC). The adaptive learning-based observer provides an approximate dynamic inversion for solving the coupled dynamic problem. The discrete-time MPC (DMPC) uses a prediction process to obtain a stable model. The Laguerre function, a state observer, and a recursive learning algorithm were integrated into DMPC, the core of the flight control system. Exponential data weighting was adopted to increase the stability of the designed flight control system. By integrating these approaches, an adaptive learning-based flight control system, which consists of the pitch, roll, yaw, and heave controllers, is presented to evaluate the stability and performance of the proposed autonomous flight control system of an unmanned helicopter. Moreover, an adaptive controller based on neural approximation for yaw motion is used to improve the performance of yawing control of the helicopter. In order to validate the feasibility of the proposed method, a hardware-in-the-loop simulation was performed in real time. The system included an embedded system, a self-developed users interface, and the X-plane flight simulator. The simulation results showed that the flight control system was able to maintain the position and the attitude of the helicopter in hover mode under the influence of wind gust and disturbance. The proposed method had better performance than a robust control system, in both ideal and disturbed environments.

Exploring reliable edge‐cloud computing for service latency optimization in sustainable cyber‐physical systems

AbstractIn recent years, the advance in information technology has promoted a wide span of emerging cyber‐physical systems (CPS) applications such as autonomous automobile systems, healthcare monitoring, and process control systems. For these CPS applications, service latency management is extraordinarily important for the sake of providing high quality‐of‐experience to terminal users. Edge‐cloud computing, integrating both edge computing and cloud computing, is regarded as a promising computation paradigm to achieve low service latency for terminal users in CPS. However, existing latency‐aware edge‐cloud computing methods dedicated for CPS fail to jointly consider energy budgets and reliability requirements, which may greatly degrade the sustainability of CPS applications. In this article, we explore the problem of minimizing service latency of edge‐cloud computing coupled CPS under the constraints of energy budgets and reliability requirements. We propose a two‐stage approach composed of static and dynamic service latency optimization. At static stage, Monte‐Carlo simulation with integer‐linear‐programming technique is adopted to find the optimal computation offloading mapping and task backup number. At dynamic stage, a backup‐adaptive dynamic mechanism is developed to avoid redundant data transmissions and executions for achieving additional energy savings and service latency enhancement. Experimental results show that our solution is able to reduce system service latency by up to 18.3% compared with representative baseline solutions.

Design of Integrated Autonomous Driving Control System That Incorporates Chassis Controllers for Improving Path Tracking Performance and Vehicle Stability

This paper describes an integrated autonomous driving (AD) control system for an autonomous vehicle with four independent in-wheel motors (IWMs). The system consists of two parts: the AD controller and the chassis controller. These elements are functionally integrated to improve vehicle stability and path tracking performance. The vehicle is assumed to employ an IWM independently at each wheel. The AD controller implements longitudinal/lateral path tracking using proportional-integral(PI) control and adaptive model predictive control. The chassis controller is composed of two lateral control units: the active front steering (AFS) control and the torque vectoring (TV) control. Jointly, they find the yaw moment to maintain vehicle stability using sliding mode control; AFS is prioritized over TV to enhance safety margin and energy saving. Then, the command yaw moment is optimally distributed to each wheel by solving a constrained least-squares problem. Validation was performed using simulation in a double lane change scenario. The simulation results show that the integrated AD control system of this paper significantly improves the path tracking capability and vehicle stability in comparison with other control systems.

Application of robust H-infinity controller in transition flight modeling of autonomous VTOL convertible Quad Tiltrotor UAV

PurposeThis paper aims to provide a mathematical modeling and design of H-infinity controller for an autonomous vertical take-off and landing (VTOL) Quad Tiltrotor hybrid unmanned aerial vehicles (UAVs). The variation in the aerodynamics and model dynamics of these aerial vehicles due to its tilting rotors are the key issues and challenges, which attracts the attention of many researchers. They carry parametric uncertainties (such as non-linear friction force, backlash, etc.), which drives the designed controller based on the nominal model to instability or performance degradation. The controller needs to take these factors into consideration and still give good stability and performance. Hence, a robust H-infinity controller is proposed that can handle these uncertainties.Design/methodology/approachA unique VTOL Quad Tiltrotor hybrid UAV, which operates in three flight modes, is mathematically modeled using Newton–Euler equations of motion. The contribution of the model is its ability to combine high-speed level flight, VTOL and transition between these two phases. The transition involves the tilting of the proprotors from 90° to 0° and vice-versa in 15° intervals. A robust H-infinity control strategy is proposed, evaluated and analyzed through simulation to control the flight dynamics for different modes of operation.FindingsThe main contribution of this research is the mathematical modeling of three flight modes (vertical takeoff–forward, transition–cruise-back, transition-vertical landing) of operation by controlling the revolutions per minute and tilt angles, which are independent of each other. An autonomous flight control system using a robust H-infinity controller to stabilize the mode of transition is designed for the Quad Tiltrotor UAV in the presence of uncertainties, noise and disturbances using MATLAB/SIMULINK. This paper focused on improving the disturbance rejection properties of the proposed UAV by designing a robust H-infinity controller for position and orientation trajectory regulation in the presence of uncertainty. The simulation results show that the Tiltrotor achieves transition successfully with disturbances, noise and uncertainties being present.Originality/valueA novel VTOL Quad Tiltrotor UAV mathematical model is developed with a special tilting rotor mechanism, which combines both aircraft and helicopter flight modes with the transition taking place in between phases using robust H-infinity controller for attitude, altitude and trajectory regulation in the presence of uncertainty.

Autonomous Positioning and Control System of Rotor UAV Based on Machine Vision

In recent years, with the rapid development of electronic technology and image recognition technology, target recognition technology based on rotary wing UAV has become a hot research topic. This paper mainly studies the autonomous positioning and control system of rotary-wing UAV based on machine vision. This paper uses the weighted average method to convert the color images collected by the camera into grayscale images. The color image collected by the airborne camera is grayed out, and the binary image is obtained after threshold segmentation. The median filter technology is used to eliminate the noise, the edge information of the mark is detected, and the Harris corner points are finally extracted. After the clustering operation is completed, the traditional least squares method is used to fit a straight line, each connected component is matched, and each point is weighted according to its gradient. In this paper, the RANSAC algorithm is used to remove the mismatch points and obtain the SIFT characteristic information. At the same time, the PID control algorithm is used to obtain the deviation required for PID control. According to the rotational speed of the four motors of the deviation control system, the attitude control of the aircraft is realized. Finally, the positioning accuracy of the system is evaluated. Experimental results show that the detection time of SIFT feature points is about 100ms. The results show that machine vision improves the positioning accuracy of rotary-wing UAV and improves the accuracy of target recognition.

Development of an algorithm for the calibration of optical and thermal cameras into a unified machine vision system and its software implementation

The analysis of thermal images is an urgent task for many areas of modern technology and technology, since thermal images are used in many areas of life. Disjointed systems do not allow you to quickly form a general idea of the ongoing processes, since they do not allow an analysis of all parameters. The combination of data spaces obtained in different ranges can increase the productivity and speed of decision making by automated systems. Most often, images obtained with only a thermal imaging camera are a set of spots that characterize the gradient of temperature change in space. These images do not have clear boundaries, which is associated with the physics of thermal energy transfer processes in space. Combination of data obtained by thermal imaging and a camera recording data in the visible range, which are familiar to perception and more understandable to humans.Areas of application of combined images are: medicine, when making a diagnosis, identifying patients in a crowd (for example, a pandemic), searching for oncology or tissue death processes (as a result of hypothermia or burns); security systems and access control systems; chemistry, in the analysis of mixing processes and reactions; automated and autonomous control systems when building automatic driving systems; and etc.The development of machine vision systems is based on the analysis of information received by light-sensitive matrices in the form of two-dimensional signals (images). Formation of images is associated with the imposition of distortions on them, which can affect the final result. It is possible to reduce the amount of distortion in different ways or a combination of them. One of the possible methods of primary data processing can be the use of an algorithm for combining data by means of their joint calibration, which will make it possible to achieve a relationship between a 3D point in the real world and the corresponding 2D image, a projection (each pixel) in the image taken by a calibrated camera. In this article, we will consider a thermal camera calibration algorithm based on the analysis of a test pattern.

Technology of self-driving in challenging road and weather conditions

Every year, the development of autonomous vehicle control systems is becoming more and more pressing due to more global projects in the field of transport, the development of new hard-to-reach areas, and the increasing volume of freight transportation. Particularly acute is the issue of improving control systems in regions with challenging road and weather conditions as well as insufficient visibility, which have a significant impact on traffic safety. It is required to develop algorithmic software for an intelligent autonomous vehicle control system used when transporting goods along a given route. The paper considers the control algorithm as well as the equipment of the control system and the tracking machine vision system. The results of experimental studies on the movement of an autonomous vehicle based on the KamAZ 6520 production vehicle are presented.

Decoupled Longitudinal and Lateral Vehicle Control Based Autonomous Lane Change System Adaptable to Driving Surroundings

The purpose of this study is to develop an autonomous lane change control system that adapts to variable surrounding conditions, to ensure vehicle safety and traffic flow stability. In this paper, we propose decision-making and control procedures for realizing autonomous lane changing; to this end, we consider not only behaviors for changing lanes but also those involved in approaching the lane changing state, with a focus on the controller design. A decoupled control structure and longitudinal trajectory-free control approach are suggested. We design a novel inter-vehicle spacing policy and a 3DOF lateral error vehicle dynamics model. To verify the effectiveness of our system, simulation experiments are performed for 12 scenarios, and system assessments are conducted based on four evaluation perspectives. The results confirm that our system can safely control the vehicle amidst various surrounding vehicle conditions and can also ensure vehicle motion stability. Furthermore, we solve the existing dynamic instability problem of lateral control, which arises through longitudinal acceleration variability. Another significant advantage of this model is that the controlled vehicle does not interfere with the target-lane traffic flow and smoothly synchronizes with the flow during lane changing.