Robust control of variable speed autonomous underwater vehicle

Set point tracking control of autonomous underwater vehicle (AUV) via robust model predictive control (RMPC) is considered. Input-constrained RMPC with integral action, which has been developed in our previous work, is used to control the AUV in this study. In order to derive a RMPC control rule, non-linear dynamics of AUV with six degree of freedom is linearized at certain operating points. So, horizontal and vertical plane dynamics of system are represented by linear models which have polytopic uncertainties. Since the derived control rule will be used in real time, the computation time should be reduced. To overcome this computational time problem and get rid of trial–error step of Algorithm 1, a new algorithm is proposed here. The simulations are carried out using the control rule based on this algorithm and these results are presented.     

Motion planning of autonomous vehicles in a non-autonomous vehicle environment without speed lanes

Planning is one of the key problems for autonomous vehicles operating in road scenarios. Present planning algorithms operate with the assumption that traffic is organised in predefined speed lanes, which makes it impossible to allow autonomous vehicles in countries with unorganised traffic. Unorganised traffic is though capable of higher traffic bandwidths when constituting vehicles vary in their speed capabilities and sizes. Diverse vehicles in an unorganised exhibit unique driving behaviours which are analysed in this paper by a simulation study. The aim of the work reported here is to create a planning algorithm for mixed traffic consisting of both autonomous and non-autonomous vehicles without any inter-vehicle communication. The awareness (e.g. vision) of every vehicle is restricted to nearby vehicles only and a straight infinite road is assumed for decision making regarding navigation in the presence of multiple vehicles. Exhibited behaviours include obstacle avoidance, overtaking, giving way for vehicles to overtake from behind, vehicle following, adjusting the lateral lane position and so on. A conflict of plans is a major issue which will almost certainly arise in the absence of inter-vehicle communication. Hence each vehicle needs to continuously track other vehicles and rectify plans whenever a collision seems likely. Further it is observed here that driver aggression plays a vital role in overall traffic dynamics, hence this has also been factored in accordingly. This work is hence a step forward towards achieving autonomous vehicles in unorganised traffic, while similar effort would be required for planning problems such as intersections, mergers, diversions and other modules like localisation.     

Foraging theory for multizone temperature control

Models from behavioral ecology, specifically foraging theory, are used to describe the decisions an animal forager must make in order to maximize its rate of energy gain and thereby improve its survival probability. Using a bioinspired methodology, we view an animal as a software agent, the foraging landscape as a spatial layout of temperature zones, and nutrients as errors between the desired and actual temperatures in the zones. Then, using foraging theory, we define a decision strategy for the agent that has an objective of reducing the temperature errors in order to track a desired temperature. We describe an implementation of a multizone temperature experiment, and show that the use of multiple agents defines a distributed controller that can equilibrate the temperatures in the zones in spite of interzone, ambient, and network effects. We discuss relations to ideas from theoretical ecology, and identify a number of promising research directions. It is our hope that the results of this paper will motivate other research on bioinspired methods based on behavioral ecology     

Planning and reasoning for autonomous vehicle control

A reasoning system to support the planning and control requirements of an autonomous land vehicle is described. This system is designed specifically to handle diverse terrain with maximal speed, efficacy, and versatility. the hierarchical architecture for this system is presented along with the detailed algorithms, heuristics, and planning methodologies for the component modules. the architecture is structured such that lower-level modules perform tasks requiring greatest immediacy, while higher-level modules perform tasks involving greater assimilation of sensor data, making use of large amounts of a priori knowledge. In describing the component modules of this system, specific techniques for mission planning, map-based route planning, local terrain navigation, and reflexive vehicle control are presented. These techniques have been demonstrated both in a detailed realtime simulation and on a small indoor robotic vehicle.     

An autonomous vehicle for people with motor disabilities

The authors describe the creation of the VAHM prototype, including the hardware and software functionalities and other aspects of this “smart” wheelchair     

Self-adaptive neuro-fuzzy systems for autonomous underwater vehicle control

This paper presents a systematic approach for developing a concise self-adaptive neurofuzzy inference system (SANFIS) with a fast hybrid parameter learning algorithm for on-line learning of the control knowledge for autonomous underwater vehicle (AUV) control. The multi-layered network structure of SANFIS incorporates fuzzy basis functions for better function approximations. We investigate three SANFIS structures with three different types of fuzzy IF-THEN rule-based models and cast the rule formation problem as a clustering problem. A recursive least-squares algorithm and a modified Levenberg-Marquardt algorithm with limited memory are exploited to accelerate the parameter learning process. This hybrid parameter learning algorithm together with an on-line clustering technique and rule examination provide SANFIS with the capability of selforganizing and self-adapting its internal structure (i.e. the fuzzy rules and term sets) for learning the required control knowledge for an AUV to follow desired trajectories. Computer simulations for modeling a control system for an AUV have been conducted to validate the effectiveness of the proposed SANFIS.     

Seafloor map generation for autonomous underwater vehicle navigation

Elevation map generation is an essential component of any autonomous underwater vehicle designed to navigate close to the seafloor because elevation maps are used for obstacle avoidance, path planning and self localization. We present an algorithm for the reconstruction of elevation maps of the seafloor from side-scan sonar backscatter images and sparse bathymetric points co-registered within the image. Given the trajectory for the underwater vehicle, the reconstruction is corrected for the attitude of the side-scan sonar during the image generation process. To perform reconstruction, an arbitrary but computable scattering model is assumed for the seafloor backscatter. The algorithm uses the sparse bathymetric data to generate an initial estimate for the elevation map which is then iteratively refined to fit the backscatter image by minimizing a global error functional. Concurrently, the parameters of the scattering model are determined on a coarse grid in the image by fitting the assumed scattering model to the backscatter data. The reconstruction is corrected for the movement of the sensor by initially doing local reconstructions in sensor coordinates and then transforming the local reconstructions to a global coordinate system using vehicle attitude and performing the reconstruction again. We demonstrate the effectiveness of our algorithm on synthetic and real data sets. Our algorithm is shown to decrease the average elevation error when compared to real bathymetry from 4.6 meters for the initial surface estimate to 1.6 meters for the final surface estimate from a survey taken of the Juan de Fuca Ridge.     

Distributed multilane merging for connected autonomous vehicle platooning

In the context of coordination of connected autonomous vehicles (CAVs),the platooning opera-tion is a promising application.The formulation of a single stream o...     

Foraging theory for dimensionality reduction of clustered data

We present a bioinspired algorithm which performs dimensionality reduction on datasets for visual exploration, under the assumption that they have a clustered structure. We formulate a decision-making strategy based on foraging theory, where a software agent is viewed as an animal, a discrete space as the foraging landscape, and objects representing points from the dataset as nutrients or prey items. We apply this algorithm to artificial and real databases, and show how a multi-agent system addresses the problem of mapping high-dimensional data into a two-dimensional space.     

Security concepts for the dynamics of autonomous vehicle networks

The secure operation of autonomous vehicle networks in the presence of adversarial observation is examined, in the context of a canonical double-integrator-network (DIN) model. Specifically, we study the ability of a sentient adversary to estimate the full network’s state, from noisy local measurements of vehicle motions. Algebraic, spectral, and graphical characterizations are provided, which indicate the critical role of the inter-vehicle communication topology and control scheme in achieving security.     

水下无人潜器回收测向搜寻仪

设计了一款可高精度定位且支持多种射频源的水下无人潜器(AUV)回收测向搜寻仪。定向天线负责接收来自AUV上射频源的信号,搜寻仪首先通过低噪声射频放大模块、射频检波模块对该信号进行放大检波,极大地保证了信号在传输过程中的抗干扰性以及通信的最大距离。随后通过信号显示主控模块对信号强度值以及信号收发源之间的距离值进行显示,最终完成对AUV的射频定位。试验证明,该搜寻仪的适用频段为50 MHz~1 200 MHz,可以支持不同频率的射频信号源,在保证测向精度较高的情况下,测距的最大值可以达到10 km,误差可控制在10%以内。     

MARIUS: an autonomous underwater vehicle for coastal oceanography

An autonomous underwater vehicle (AW), named MARIUS, has been developed under the MAST Programme of the Commission of the European Communities. The primary envisioned missions of the prototype AW are environmental surveying and oceanographic data acquisition in coastal waters. The authors describe the design and implementation of the AW systems for vehicle and mission control, and report the results of the sea trials conducted with the vehicle in Sines, Portugal     

Intelligent acoustic rotor speed estimation for an autonomous helicopter

Acoustic sensing to gather information about a machine can be highly beneficial, but processing the data can be difficult. In this work, a variety of methodologies have been studied to extract rotor speed information from the sound signature of an autonomous helicopter, with no a-priori knowledge of its underlying acoustic properties.     

Curvature continuous path generation for autonomous vehicle using B-spline curves

In this paper we introduce a roadmap algorithm for generating collision-free paths in terms of cubic B-spline curves for unmanned vehicles used in mining operations. The algorithm automatically generates collision-free paths that are curvature continuous with an upper bounded curvature and a small slope discontinuity of curvature at knots, when we are given the locations of the obstacles, the boundary geometry of the working area, positions and directions of the vehicle at the start, loading, and the goal points. Our algorithm also allows us to find a switch back point where the vehicle reverses its direction to enter the loading area. Examples are provided to demonstrate the effectiveness of the proposed algorithms.     

Strategies for simultaneous multiple autonomous underwater vehicle operation and control

Under sampling of the coastal oceans remains a persistent problem for standard oceano-graphic measurement practice wherein an instrument package is tethered to a research vessel. The overhead costs associated with operating a large research vessel impose a strict minimum on the cost of data collected. Owing to the overheads, significant improvements in sampling technology on the tethered platform can only produce modest gains in the cost effectiveness. In contrast, untethered vehicles if operated simultaneously have the potential to increase cost effectiveness significantly by distributing the overhead costs over several sampling platforms. Furthermore, synoptic and pseudosynoptic data can be collected with multiple autonomous underwater vehicles (AUVs), thereby providing the type of information critical to dynamic process modeling unattainable with non-synoptic data. While the goal of simultaneous multiple-vehicle operation has been espoused over the last few years, AUV technology and practice have until recently been too immature to realize that potential. Recently, Florida Atlantic University (FAU) has developed a new series of modular AUVs with the express purpose of supporting multiple sensors and multiple-vehicle operation. This series of vehicle is called the Ocean Explorer of which three have been produced so far. This paper will explore some of the associated navigation, tracking, control and deployment problems associated with multiple-vehicle operation in coastal applications. In addition, the characteristics of the component level intelligent distributed control system, integrated data logger and vehicle control system will be discussed. In particular this paper will discuss how FAU has applied the concepts of elastic constraint propagation and the symmetric fuzzy decision-making model to AUV control systems. Some results of early experiments in synoptic data collection with a conductivity, temperature and depth (CTD) sensor using multiple AUVs for the determination of horizontal structure will be described.     

Model based vehicle detection and tracking for autonomous urban driving

Situational awareness is crucial for autonomous driving in urban environments. This paper describes the moving vehicle detection and tracking module that we developed for our autonomous driving robot Junior. The robot won second place in the Urban Grand Challenge, an autonomous driving race organized by the U.S. Government in 2007. The module provides reliable detection and tracking of moving vehicles from a high-speed moving platform using laser range finders. Our approach models both dynamic and geometric properties of the tracked vehicles and estimates them using a single Bayes filter per vehicle. We present the notion of motion evidence, which allows us to overcome the low signal-to-noise ratio that arises during rapid detection of moving vehicles in noisy urban environments. Furthermore, we show how to build consistent and efficient 2D representations out of 3D range data and how to detect poorly visible black vehicles. Experimental validation includes the most challenging conditions presented at the Urban Grand Challenge as well as other urban settings.     

Inertial navigation sensor integrated motion analysis for autonomous vehicle navigation

Many types of existing vehicles contain an inertial navigation system (INS) that can be utilized to greatly improve the performance of motion analysis techniques and make them useful for practical military and civilian applications. This article presents the results obtained with a maximally passive system of obstacle detection for ground-based vehicles and rotorcraft. Automatic detection of these obstacles and the necessary guidance and control actions triggered by such detection will facilitate autonomous vehicle navigation. Our approach to obstacle detection employs motion analysis of imagery collected by a passive sensor during vehicle travel to generate range measurements to world points within the field of view of the sensor. The approach makes use of INS data and scene analysis results to improve interest point selection, the matching of the interest points, and the subsequent motion-based range computations, tracking, and obstacle detection. In this article, we concentrate on the results obtained using lab and outdoor imagery. The range measurements that are made by INS integrated motion analysis are compared to a limited amount of ground truth that is available. © 1992 John Wiley & Sons, Inc.     

Neurofuzzy identification of an autonomous underwater vehicle

Neurofuzzy modelling is ideally suited to many nonlinear system identification and data modelling applications. By combining the attractive attributes of fuzzy systems and neural networks transparent models of ill-defined systems can be identified. Available expert a priori knowledge is used to construct an initial model. Data modelling techniques from the neural network, statistical and conventional system identification communities are then used to adapt these models. As a result accurate parsimonious models which are transparent and easy to validate are identified. Recent advances in the datadriven identification algorithms have now made neurofuzzy modelling appropriate for high-dimensional problems for which the expert knowledge and data may be of a poor quality. In this paper neurofuzzy modelling techniques are presented. This powerful approach to system identification is demonstrated by its application to the identification of an Autonomous Underwater Vehicle (AUV).     

电动车测速报警系统

设计了一种基于80C51单片机的电动车测速报警系统,实现了电动车速度的实时显示以及超速后的自行报警。帮助交通管理人员及时提醒过往车辆,减少交通事故。经试用,达到了设计要求。