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.     

Using shorelines for autonomous air vehicle guidance

We suggest the use of extended landmarks, such as shorelines, creeks, tree lines, and railroads, as well as roads for autonomous navigation of an unmanned air vehicle (UAV). In particular, we recommend the use of shorelines, because of their common availability, their ease of detection, and their significance in terms of events happening along them. Monitoring coastlines and waterways from low flying UAVs has many applications for military and civilian use. We report the development of a vision system that has enabled a prototype UAV to follow shorelines autonomously (without requiring maps or GPS). Using a near-infrared sensor the vision system distinguishes water from land (irrespective of water’s color) and issues commands to the autopilot to follow the coastline or the riverbank. One insight of this problem is that the control algorithm could be integrated deeply with the vision system. This has the benefit of delaying smoothing/regularization so that it could occur in the context of the control coordinate system rather than the image or ground coordinate system. The algorithm itself is simple, but it possibly points the way to future algorithms which could more closely couple image processing and control. Furthermore, the experience gained in this work may be of value in the development of vision systems for following other types of paths.     

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     

A unified optimal planner for autonomous parking vehicle

In order to reduce the controlling difficulty caused by trajectory meandering and improve the adaptability to parking into regular lots, a versatile optimal planner (OP) is proposed. Taking advantage of the low speed specificity of parking vehicle, the OP algorithm was modeled the planning problem as a convex optimization problem. Collision-free constraints were formalized into the shortest distance between convex sets by describing obstacles and autonomous vehicle as affine set. Since employing Lagrange dual function and combining KKT conditions, the collision-free constraints translated into convex functions. Taking the national standard into account, 5 kinds of regular parking scenario, which contain $0^\circ$, $30^\circ$, $45^\circ$, $60^\circ$ and $90^\circ$ parking lots, were designed to verify the OP algorithm. The results illustrate that it is benefit from the continuous and smooth trajectory generated by the OP method to track, keep vehicle''s stability and improve ride comfort, compared with A* and hybrid A* algorithms. Moreover, the OP method has strong generality since it can ensure the success rate no less than 82% when parking planning is carried out at the start node of 369 different locations. Both of evaluation criteria, as the pear error and RMSE in x direction, y axis and Euclidean distance d, and heading deviation $\theta$, are stable and feasible in real tests, which illustrates that the OP planner can satisfy the requirements of regular parking scenarios.     

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.     

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.     

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.     

基于事件触发机制的自主车辆队列协同控制

为应对通信过程存在的扰动以及减少自主车辆队列控制中的信息冗余、资源浪费,提出了一种基于事件触发机制的自主车辆队列协同控制算法,保证自主车辆队列的稳定运行。首先,针对有向通信拓扑结构下的自主车辆队列设计基于事件触发机制的协同控制算法,即使存在扰动约束,自主车辆也可以在该算法的控制下有效跟随领航车辆的速度,且与相邻车辆保持期望的安全距离。其次,通过设计Lyapunov函数以及分析Zeno行为,证明算法的有效性和安全性。最后,通过MATLAB仿真验证了控制算法的正确性。     

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     

Review of mission planning for autonomous marine vehicle fleets

The deployment of a fleet of autonomous marine vehicles (AMVs) allows for the parallelisation of missions, intervehicle support for longer deployment times, adaptability and redundancy to in situ mission changes, and effective use of the right vehicle for the right purpose. End users and operators of AMVs face challenges in planning complex missions due to the limitations of their vehicles, dynamic, operationally constrictive, and unstructured environments, and in minimising risks to equipment, the mission, and personnel. Automated mission planning for AMV fleets can be a tool to reduce the complexity of programming vehicle tasking, and to perform validity assessments for end user‐specified goals, allowing the operator to focus on risk assessment. We present a critical review of the current advances in automated planning for AMV fleets, investigating the limitations of available state‐of‐the‐art tools and providing a road map of the goals and challenges based on analysis of field reports and end user initiatives.     

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。     

A distributed fuzzy logic controller for an autonomous vehicle

Autonomous vehicles can be used in a variety of applications such as hazardous environments or intelligent highway systems. Fuzzy logic is an appropriate choice for this application as it can describe human behavior well. This paper proposes two fuzzy logic controllers for the steering and the velocity control of an autonomous vehicle. The two controllers are divided into separate modules to mimic the way humans think while driving. The steering controller is divided into four modules; one module drives the vehicle toward the target while another module avoids collision with obstacles. A third module drives the vehicle through mazes. The fourth module adjusts the final orientation of the target. The velocity controller is divided into three modules; the first module speeds up the vehicle to reach the target and slows it down as it moves toward the target. The second module controls the velocity in the neighborhood of obstacles. A third module controls the velocity of the vehicle as it turns sharp corners. A method for automatic tuning of the first module of the velocity controller is proposed to stabilize the velocity of the vehicle as it approaches the target. Two examples to demonstrate the interaction among the seven control modules are included. Results of the simulation are compared with those in the literature. © 2004 Wiley Periodicals, Inc.     

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.     

一种水下机器人运动的过程神经元控制

过程神经网络是传统神经网络的拓展, 增加了一个对于时间的聚合算子, 从而更好地模拟了生物神经元的信息处理机制. 这是由于水下机器人运动控制系统的输入、输出均是随时间连续变化的过程量. 结合S函数和预先规划思想, 建立水下机器人过程神经元的运动控制模型. 仿真试验证明,该新型控制模型, 对于水下机器人的运动非线性控制器具有设计简单、响应速度快、超调小、鲁棒性好等优点.     

自治水下机器人心智模型

自治水下机器人(Autonomous Underwater Vehicle, AUV)在复杂的环境中作业,对其智能水平提出了很高的要求。结合Agent理论的研究成果,在信念 愿望 意图（BDI）逻辑的基础上拓展出自治水下机器人心智逻辑（AML）,用于建立AUV心智模型,在无损AUV自治性和反应性的前提下,增强了其主动性和社会性。定义了AML的语法和语义,证明了系统的可靠性和完备性,给出了AUV的心智活动过程。仿真实验结果证明了AUV心智模型的正确性和有效性。