Showing papers on "Autonomous system (mathematics) published in 2016"

A functional reference architecture for autonomous driving

Abstract: ContextAs autonomous driving technology matures toward series production, it is necessary to take a deeper look at various aspects of electrical/electronic (E/E) architectures for autonomous driving.ObjectiveThis paper describes a functional reference architecture for autonomous driving, along with various considerations that influence such an architecture. The functionality is described at the logical level, without dependence on specific implementation technologies.MethodEngineering design has been used as the research method, which focuses on creating solutions intended for practical application. The architecture has been refined and applied over a 5 year period to the construction of prototype autonomous vehicles in three different categories, with both academic and industrial stakeholders.ResultsThe architectural components are divided into categories pertaining to (i) perception, (ii) decision and control, and (iii) vehicle platform manipulation. The architecture itself is divided into two layers comprising the vehicle platform and a cognitive driving intelligence. The distribution of components among the architectural layers considers two extremes: one where the vehicle platform is as "dumb" as possible, and the other, where the vehicle platform can be treated as an autonomous system with limited intelligence. We recommend a clean split between the driving intelligence and the vehicle platform. The architecture description includes identification of stakeholder concerns, which are grouped under the business and engineering categories. A comparison with similar architectures is also made, wherein we claim that the presence of explicit components for world modeling, semantic understanding, and vehicle platform abstraction seem unique to our architecture.ConclusionThe concluding discussion examines the influences of implementation technologies on functional architectures and how an architecture is affected when a human driver is replaced by a computer. The discussion also proposes that reduction and acceleration of testing, verification, and validation processes is the key to incorporating continuous deployment processes.

Monitoring driver cognitive load using functional near infrared spectroscopy in partially autonomous cars

Abstract: In partially automated cars, it is vital to understand the driver state, especially the driver's cognitive load. This might indicate whether the driver is alert or distracted, and if the car can safely transfer control of driving. In order to better understand the relationship between cognitive load and the driver performance in a partially autonomous vehicle, functional near infrared spectroscopy (fNIRS) measures were employed to study the activation of the prefrontal cortex of drivers in a simulated environment. We studied a total of 14 participants while they drove a partially autonomous car and performed common secondary tasks. We observed that when participants were asked to monitor the driving of an autonomous car they had low cognitive load compared to when the same participants were asked to perform a secondary reading or video watching task on a brought in device. This observation was in line with the increased drowsy behavior observed during intervals of autonomous system monitoring in previous studies. Results demonstrate that fNIRS signals from prefrontal cortex indicate additional cognitive load during manual driving compared to autonomous. Such brain function metrics could be used with minimally intrusive and low cost sensors to enable real-time assessment of driver state in future autonomous vehicles to improve safety and efficacy of transfer of control.

System Identification-Based Sliding Mode Control for Small-Scaled Autonomous Aerial Vehicles With Unknown Aerodynamics Derivatives

Abstract: This paper proposes an autonomous system identification methodology for fixed wing autonomous aerial vehicles (AAVs) and implementation with a robust control technique. This system identification methodology does not require the user to provide any knowledge of the aerodynamic derivatives of the AAV. Hence, this control methodology is applicable to a wide range of fixed-wing AAVs. Since the system identification process generates errors due to the measurement noise and external disturbances, a robust control methodology, sliding mode control (SMC), is proposed to control the AAV based on the system identification results. To characterize the capability of the control methodology, experiments have been conducted on commercially available fixed-wing aircraft platforms using low-cost sensing equipment. Experimental results demonstrate that the autonomous system identification-based SMC enables the AAV to follow the desired trajectory without prior knowledge of the aerial vehicle or manual tuning of control system parameters.

Asymptotic Behavior of a Nonautonomous p-Laplacian Lattice System

Abstract: The existence of a pullback attractor for the nonautonomous p-Laplacian type equations on infinite lattices is established under certain natural dissipative conditions. In particular, there is no restriction on the power index q of the nonlinearity relative to the index p. The forward limiting behavior is also discussed and, under suitable assumptions on the time dependent terms, the lattice system is shown to be asymptotically autonomous with its pullback attractor component sets converging upper semi-continuously to the autonomous global attractor of the limiting autonomous system.

Methods and apparatus for reconfigurable power exchange for multiple uav types

Abstract: A reconfigurable system capable of autonomously exchanging material from unmanned vehicles of various types and sizes. The system comprises an environmental enclosure, a landing area, a universal mechanical system to load and unload material from the unmanned vehicle, and a central processor that manages the aforementioned tasks. The landing area may comprise a one or more visible or non-visible markers/emitters capable of generating composite images to assist in landing the unmanned vehicle upon the reconfigurable, autonomous system.

Machine vision and fuzzy logic-based navigation control of a goal-oriented mobile robot

Abstract: This work addresses a new goal oriented navigation framework for autonomous system. In the proposed work, a hybrid control system, comprising deliberative and behavior-based architectures, has been developed. Deliberative layer employs a monocular vision camera to obtain the position of the goal while behavior-based framework makes use of the motor schema technique for safe navigation. Fuzzy logic is also adopted in order to enhance the performance of the navigation system. A rigorous series of experiments has been conducted using two navigation methods, which are the proposed control system and the conventional navigation technique utilizing the potential field method for achieving the desired goals. Both systems are implemented in the simulated experiments using Stage simulator. By employing these two approaches, it is possible to present a comparison of the navigation results between the systems utilizing different navigation techniques. The experimental results reveal that the proposed system produces better navigation performance compared to the conventional method in terms of safe and successful navigation, with a smoother trajectory and consistent motion.

Visibility Enhancement using Autonomous Multicamera Controls with Situational Role Assignment for Teleoperated Work Machines

Abstract: The aim of this study is to provide a machine operator with enhanced visibility and more adaptive visual information suited to the work situation, particularly advanced unmanned construction. Toward that end, we propose a method for autonomously controlling multiple environmental cameras. Situations in which the yaw, pitch, and zoom of cameras should be controlled are analyzed. Additionally, we define imaging objects, including the machine, manipulators, and end points; and imaging modes, including tracking, zoom, posture, and trajectory modes. To control each camera simply and effectively, four practical camera roles with different combinations of the imaging objects and modes were defined: overview machine, enlarge end point, posture-manipulator, and trajectory-manipulator. A real-time role assignment system is described for assigning the four camera roles to four out of six cameras suitable for the work situation e.g., reaching, grasping, transport, and releasing on the basis of the assignment-priority rules. To test this system, debris-removal tasks were performed in a virtual reality simulation to compare performance among fixed camera, manual control camera, and autonomous control camera systems. The results showed that the autonomous system was the best of the three at decreasing the number of grasping misses and erroneous contacts and simultaneously increasing the subjective usability and time efficiency.

Technologies for customized crowd-sourced features, automated safety and quality assurance with a technical computing environment

Abstract: Technologies for customized crowd-sourced update and validation include a computing device having a technical computing environment (TCE)-based engine that receives user information from one or more user devices, executes a TCE model with the user information to generate behavior data of the TCE model, and generates a software update for the TCE model based on the behavior data. The TCE model may be a model for an autonomous system such as a self-driving vehicle, and the software update may be a safety update for the autonomous vehicle. The user information may include sensor data, such as distance detection sensor data. The computing device may transmit an incentive such as a software update, feature update, or safety software update to the user devices. The computing device may also receive information associated with the TCE model from one or more developer devices. Other embodiments are described and claimed.

Dynamics of a Class of Nonautonomous Lorenz-Type Systems

Abstract: The dynamical properties of a kind of Lorenz-type systems with time-varying parameters are studied. The time-varying ultimate bounds are estimated, and a simple method is provided to generate strange attractors, including both the strange nonchaotic attractors (SNAs) and chaotic attractors. The approach is: (i) take an autonomous system with an ultimate bound from the Lorenz family; (ii) add at least two time-varying parameters with incommensurate frequencies satisfying certain conditions; (iii) choose the proper initial position and time by numerical simulations. Three interesting examples are given to illustrate this method with computer simulations. The first one is derived from the classical Lorenz model, and generates an SNA. The second one generates an SNA or a Lorenz-like attractor. The third one exhibits the coexistence of two SNAs and a Lorenz-like attractor. The nonautonomous Lorenz-type systems present more realistic models, which provide further understanding and applications of the numerical analysis in weather and climate predication, synchronization, and other fields.

Development of the human interactive autonomy for the shared teleoperation of mobile robots

Abstract: Manipulation complexity of teleoperation increases the necessity of shared autonomy. However, designing an autonomy while keeping in mind the environmental uncertainty is challenging. This paper proposes a human interactive autonomy for a shared teleoperation. The central concept is a novel methodology of combining the innate cognitive ability of a human operator to arrive at an accurate, and precise solution of an autonomous system for mobile robot shared teleoperation. The resulting system is more efficient and less fatigued than direct teleoperation. This combined approach transfers the human operator's intention, shaped by his cognition about remote environment, to the autonomous system. The intention information input from the operator helps autonomous system perform the given teleoperation tasks more efficiently. For initial feasibility and proof of concept, the intention information is provided to the autonomous system in the form of a path which is acquired by our proposed sketch method. Our novel proposed sketch method allows the operator to sketch the path on the visual feedback image of the remote environments and the information is then transmitted to the autonomous system. Experimental results provide the feasibility and effectiveness of proposed method against prevalent control methods.

Distributed Resource Management in Systems of Systems: An Architecture Perspective

Abstract: This paper introduces a framework for studying the interactions of autonomous system components and the design of the connectivity structure in Systems of Systems SoSs. This framework, which uses complex network models, is also used to study the connectivity structure's impact on resource management. We discuss resource sharing as a mechanism that adds a level of flexibility to distributed systems and describe the connectivity structures that enhance components' access to the resources available within the system. The framework introduced in this paper explicitly incorporates costs of connection and the benefits that are received by direct and indirect access to resources and provides measures of the optimality of connectivity structures. We discuss central and a distributed schemes that, respectively, represent systems in which a central planner determines the connectivity structure and systems in which distributed components are allowed to add and sever connections to improve their own resource access. Furthermore, we identify optimal connectivity structures for systems with various heterogeneity conditions.

Self-driving system and method of vehicle

Abstract: A system of an autonomous vehicle and a method thereof according to the present invention can increase the recognition probability of a signal of a traffic light by using map information embedded in an autonomous-driving system inside the vehicle and a traffic light infrastructure, can determine which progression route is allowed by signal information transmitted ( about which exit lane is available on which entry lane) through V2X communication and a camera recognition result. Also, an autonomous vehicle can efficiently pass through an intersection by exchanging intersection pass route information between the autonomous vehicle and the traffic light infrastructure. The autonomous system of a vehicle according to the present invention includes an autonomous vehicle, and a server which controls the signal of the traffic light in a pedestrian crossing or intersection.

Bilateral shared autonomous system for MUMAV with nonpassive human and environment input interaction forces

Abstract: In this paper, the stability and synchronization control problem of bilateral shared autonomous system for miniature unmanned multirotor aerial vehicle (MUMAV) is addressed with nonpassive human and environment input forces. The master input interface design combines scaled position of the master manipulator with velocity signals of the MUMAV and reflected remote interaction forces. The slave input interaction interface is designed by combining scaled position and velocity of the master manipulator with the velocity of the remote slave MUMAV system. The data transmission between local master and remote MUMAV is assumed to be carried out by using dedicated internet communication network with negligible time delay. The convergence analysis is shown by using Lyapunov method. The analysis shows that the closed loop bilateral shared autonomous system is input-to-state stable and ultimately bounded with nonpassive human and environment input interaction force.

Aqua-Quad - solar powered, long endurance, hybrid mobile vehicle for persistent surface and underwater reconnaissance, part II - onboard intelligence

Abstract: The paper describes the control system development of a novel hybrid autonomous vehicle - Aqua-Quad, a Multi-Rotor Vertical Take Off and Landing aircraft with environmentally hardened electronics, exchangeable sensor suite, communication links, and a solar recharge system. The key objective of this multi-modal autonomous system is to enable energy-aware ultra-long endurance autonomy to facilitate near real-time capture and transition of information from the undersea domain to the air and further to the ground. The key benefit of this rapid in-situ data delivery is to enable timely and efficient decision making that, in turn, improves the collective efficiency of the Aqua-Quad as a system. Each individual vehicle is designed to have mission-relevant sensors and sufficient computational power to reduce the false alarm rate, and navigate in open seas in an energy-optimal manner by optimizing its route while either in search of targets or when tracking them. Higher operational efficiency is envisioned when a flock of Aqua-Quads operates cooperatively. The paper focuses on the design of energy-optimal path planning for a single vehicle in the presence of ocean currents. A novel modification of rapidly exploring random tree algorithm is developed to fully utilize the energy savings provided by the transportation mechanism of ocean flows.

Autonomous robot system architecture for automation of structural health monitoring

Abstract: Inspection of defects in civil infrastructure has been a constant field of research. In the majority of inspections, a technician is responsible to go physically to the field in order to detect and measure defects. Through the measurement results, engineers are able to perform the Structural Health Monitoring (SHM) of a measured structure. In this paper, a fully architecture of an autonomous system is proposed with the goal to automate the SHM task. The proposed system uses an autonomous robot, database and the proposed architecture to integrate all sub-systems for the automation of the SHM. Experimental results validate the technical feasibility of the proposed system.

Threat evaluation based on automatic sensor signal characterisation and anomaly detection

Abstract: Autonomous cyber physical systems are increasingly common in a wide variety of application domains, with a correspondingly wide range of functionalities and types of sensing and actuation. At the same time, the variety and frequency of cyber attacks is increasing in correspondence with the increasing popularity and functionality of these systems, from in-vehicle driver assistance to smart city infrastructure and robotics. These technologies rely on a variety of sensors, actuating nodes and control communications. Each sensor adds context by which the autonomous system can better understand its environment, but each sensor also provides opportunities for attack, as has been observed in a variety of attacks on different systems. In this paper, we introduce a model to observe signal characteristics, including noise level patterns, on sensor data streams and incorporate this information to differentiate between normal or abnormal behaviour of a robotic vehicle. This model forms the basis of an automated threat detection scheme, which we test using a purpose-built testbed. Experiments are conducted in a controlled environment using stochastic elements to introduce certain levels of randomness during the experiment. The results indicate that the system is able to distinguish the behaviour of a robotic vehicle under different levels of environmental volatility and is able to identify a sensory channel attack against it.

Stability criteria for a multi-city epidemic model with travel delays and infection during travel

Abstract: We present a compartmental SIR (susceptible–infected–recovered) model to describe the propagation of an infectious disease in a human population, when individuals travel between p different cities. The time needed for travel between any two locations is incorporated, and we assume that disease progression is possible during travel. The model is equivalent to an autonomous system of differential equations with multiple delays, and each delayed term is defined through a system of ordinary differential equations. We establish some necessary and sufficient conditions for the disease-free equilibrium of the model to be asymptotically stable.

Kernel design for coordination of autonomous, time-varying multi-agent configurations

Abstract: The coordination of agents in an autonomous system can greatly increase its ability to perform missions in a wide array of applications including distributed computing, coordination of mobile autonomous agents, and cooperative sensing. To expand the functionality of these systems to a wider array of applications, a need exists for coordinated control algorithms driving the system of nodes or agents to any prescribed state configuration in both time and space using only information passed between communicating agents. Using tools from graph theory, this paper derives a graph transformation method that maps the kernel of a graph's Laplacian matrix to any desired state configuration vector while retaining inter-agent communication characteristics of the graph. Using the transformation, this paper derives a theoretically-justified, decentralized control algorithm driving kinematic agents to any relative time-varying state configuration. Theoretical results are illustrated with numerical examples including load distribution in a computing network and surveillance of a moving target with kinematic agents.

Hamiltonian dynamics and control of a joint autonomous land–air operation

Abstract: Rapid technological advances in artificial intelligence and additive manufacturing have fueled the speculation that large numbers of cheap autonomous robots swarming to achieve mission objectives with minimal human control will revolutionize search-and-rescue, disaster relief and humanitarian assistance operations. These kinds of operations usually occur in a GPS-denied and communication austere environment, where only nearest-neighbors networking among the autonomous robots may be possible. Hence, the ability to predict and control the real-time global dynamic behavior of this high-dimensional, highly complex and highly nonlinear, decentralized autonomous system, immersed in an uncertain and changing environment—and yet successfully performing at the edge of chaos—will be critical. This paper proposes a rigorous computational framework for prediction and control of a large-scale joint swarm of unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs), performing a joint autonomous land–air operation. We present two cases for this joint UGV + UAV swarm: (1) a discrete Hamiltonian dynamics and affine control over Euclidean groups of individual UGVs and UAVs in a swarm of hundreds of vehicles and (2) a continuous 2-soliton ‘signature’ evolution of a joint UGV and UAV swarm of thousands of vehicles.

A hierarchical autonomous system based space information network architecture and topology control

Abstract: SIN (Space Information Network) has recently emerged as a promising approach to solving the collaboration difficulty among current space programs. However, because of the SIN’s large scale, high component complexity, and dynamic characteristics, designing a proper SIN architecture is challenging. Firstly, we propose a novel SIN architecture, which is composed of GEO (Geostationary Earth Orbit) satellites as backbone network nodes, LEO (Low Earth Orbit) or other types of satellites as enhanced coverage nodes, and high-altitude platforms to meet the service requirements of emergency or hotspot applications. Unlike most existing studies, the proposed architecture is AS (Autonomous-System) based. We decouple the complex SIN into simpler sub-networks using a hierarchical AS model. Then, we propose a topology control algorithm to minimize the time delay among sub-AS networks. We prove that the proposed algorithm achieves logical k-connectivity provided that the original physical topology has k-connectivity. Simulation results validate the theoretic analysis and effectiveness of the algorithm.

Proposal of a wireless measurement system for temperature monitoring of biological active materials

Abstract: Industrial systems for the collection of environmental data play an important part in the production processes and also in storage facilities. When storing large volumes of bio-materials such as wood chips (wood pellets) or different silage, problems arise with dangerous self-heating of the stored material. This paper deals with the design of an autonomous system for collecting such data. In the view of the needed measurements, there were designed and developed measuring probes with wireless data transmission. The proposal includes an information system for archiving and evaluation of measured data.

A low-cost visual navigation and mapping system for Unmanned Aerial Vehicle using LSD-SLAM algorithm

Abstract: The growing need of Disaster management requires some specialties in sophisticated technologies. Navigating an Unmanned Aerial Vehicle (UAV) in an unknown indoor environment is a complex task. This work tries to implement an autonomous system, which navigates the Low-cost Quadcopter with an on-board computer, sensors and Ground station which carries out a spatially consistent probabilistic model for its navigation and 3D mapping. The Simultaneous Localization and Mapping (SLAM) is also implemented in this work in an efficient way such that, it utilizes much fewer resources than other algorithms in order to operate on lower Footprint processors. It also gives a comprehensive analysis of performance-cost tradeoffs for the improved system.

A Class of Integer Order and Fractional Order Hyperchaotic Systems via the Chen System

Abstract: In this article, we investigate the generation of a class of hyperchaotic systems via the Chen chaotic system using both integer order and fractional order differential equation systems. Based on the Chen chaotic system, we designed a system with four nonlinear ordinary differential equations. For different parameter sets, the trajectory of the system may diverge or display a hyperchaotic attractor with double wings. By linearizing the ordinary differential equation system with divergent trajectory and designing proper switching controls, we obtain a chaotic attractor. Similar phenomenon has also been observed in linearizing the hyperchaotic system. The corresponding fractional order systems are also considered. Our investigation indicates that, switching control can be applied to either linearized chaotic or nonchaotic differential equation systems to create chaotic attractor.

Autonomous system level network topology identification method

Abstract: The invention discloses an autonomous system level network topology identification method. The method comprises the steps of: firstly, collecting data of global Traceroute detection points; further obtaining IP path data of the Traceroutes; according to the IP path data, utilizing a matching mode based on a maximized path pair to enable Ip-to-As mapping to be most accurate; then collecting AS path data of globle BGP data collectors; combined with AS path data of the Traceroutes, carrying out corresponding comparison and modification; and merging the two kinds of data, and obtaining a more complete global autonomous system level network topology. According to the invention, active measurement based on the Traceroutes and passive measurement based on a BGP are combined for identifying the AS level network topology, and the integrity and the accuracy of the topology are improved to a certain degree.

Can Turing machine be curious about its Turing test results? Three informal lectures on physics of intelligence.

Abstract: What is the nature of curiosity? Is there any scientific way to understand the origin of this mysterious force that drives the behavior of even the stupidest naturally intelligent systems and is completely absent in their smartest artificial analogs? Can we build AI systems that could be curious about something, systems that would have an intrinsic motivation to learn? Is such a motivation quantifiable? Is it implementable? I will discuss this problem from the standpoint of physics. The relationship between physics and intelligence is a consequence of the fact that correctly predicted information is nothing but an energy resource, and the process of thinking can be viewed as a process of accumulating and spending this resource through the acts of perception and, respectively, decision making. The natural motivation of any autonomous system to keep this accumulation/spending balance as high as possible allows one to treat the problem of describing the dynamics of thinking processes as a resource optimization problem. Here I will propose and discuss a simple theoretical model of such an autonomous system which I call the Autonomous Turing Machine (ATM). The potential attractiveness of ATM lies in the fact that it is the model of a self-propelled AI for which the only available energy resource is the information itself. For ATM, the problem of optimal thinking, learning, and decision-making becomes conceptually simple and mathematically well tractable. This circumstance makes the ATM an ideal playground for studying the dynamics of intelligent behavior and allows one to quantify many seemingly unquantifiable features of genuine intelligence.