Neuro-control of unmanned underwater vehicles

Unmanned underwater vehicles (UUVs) typically operate in uncertain and changing environments. Since the dynamics of UUVs are highly nonlinear and their hydrodynamic coefficients vary with different operating conditions, a high-performance control system of a UUV is needed to have the capacities of learning and adaptation to the variations in the UUV's dynamics. This paper presents the utilization of an adaptive neuro-control scheme as a controller for controlling a UUV in six degrees of freedom. No prior offline training phase and no explicit knowledge of the structure of the vehicle are required, and the proposed scheme exploits the advantages of both neural network control and adaptive control. Asymptotic convergence of the UUV's tracking errors and stability of the presented control system is guaranteed on the basis of the Lyapunov theory. In this paper, neural network architectures based on radial basis functions and multilayer structures have been used to evaluate the performance of the adaptive controller via computer simulation.     

Robust fault and icing diagnosis in unmanned aerial vehicles using LPV interval observers

This paper proposes a linear parameter varying (LPV) interval unknown input observer for the robust fault diagnosis of actuator faults and ice accretion in unmanned aerial vehicles (UAVs) described by an uncertain model. The proposed interval observer evaluates the set of values for the state, which are compatible with the nominal fault‐free and icing‐free operation and can be designed in such a way that some information about the nature of the unknown inputs affecting the system can be obtained, thus allowing the diagnosis to be performed. The proposed strategy has several advantages. First, the LPV paradigm allows taking into account operating point variations. Second, the noise rejection properties are enhanced by the presence of the integral term. Third, the interval estimation property guarantees the absence of false alarms. Linear matrix inequality–based conditions for the analysis/design of these observers are provided in order to guarantee the interval estimation of the state and the boundedness of the estimation. The developed theory is supported by simulation results, obtained with the uncertain model of a Zagi Flying Wing UAV, which illustrate the strong appeal of the methodology for identifying correctly unexpected changes in the system dynamics due to actuator faults or icing.     

Sonar-based guidance of unmanned underwater vehicles

This paper addresses the problem of the design and coordination of guidance and sonarbased motion estimation algorithms for unmanned underwater vehicles. In the framework of a two-layered hierarchical architecture uncoupling the system's dynamics and kinematics, a couple of guidance laws for approaching a target with the desired orientation and following an environmental feature have been designed with Lyapunov-based techniques. Suitable acoustic-based estimators of the corresponding operational variables have been designed and integrated with the guidance and control system. A finite state machine combined with a suitable interface for event generation allows the coordinated execution of basic guidance and motion estimation tasks to carry out more complex functions. Experimental results of pool trials of a prototype unmanned underwater vehicle executing free-space maneuvering, wall-following tasks and the more complex mission of following the perimeter of the trial pool are reported and discussed.     

基于行为协同和虚拟目标相结合的无人机实时航路规划

针对实时航路规划问题,综合考虑航路最优、平滑性、全局收敛性以及从威胁域的逃逸能力等限制时,还没有有效的规划算法.为此提出了一种基于行为协同和虚拟目标相结合的无人机实时航路规划方法.该方法将无人机的航路规划行为分为局部和全局行为:局部行为采用基于模糊控制的方法,用来实现威胁体规避;全局行为使用全局算法,通过全局目标和虚拟目标的切换实现了全局目标收敛和威胁域边界跟踪,然后通过模糊控制器对两种行为进行协同.最后通过分析、证明以及几种不同情形下的仿真表明该方法具有航路短、平滑和全局收敛的特点.     

Vision-based navigation of unmanned aerial vehicles

This paper presents a vision-based navigation strategy for a vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) using a single embedded camera observing natural landmarks. In the proposed approach, images of the environment are first sampled, stored and organized as a set of ordered key images (visual path) which provides a visual memory of the environment. The robot navigation task is then defined as a concatenation of visual path subsets (called visual route) linking the current observed image and a target image belonging to the visual memory. The UAV is controlled to reach each image of the visual route using a vision-based control law adapted to its dynamic model and without explicitly planning any trajectory. This framework is largely substantiated by experiments with an X4-flyer equipped with a fisheye camera.     

A vision-based landing system for small unmanned aerial vehicles using an airbag

Statistics show that the landing accounts for the largest portion of all mishaps of unmanned aerial vehicles (UAVs) due to many difficulties including limited situational awareness of the external pilot and the limited maneuverability during the low speed flight before touchdown. In this paper, a vision-based automatic landing system using a dome-shaped airbag is proposed for small UAVs. Its isotropic shape allows airplanes to approach in any direction to avoid crosswind unlike net-assisted landing. The dome’s distinctive color improves the detection owing to its strong visual cue. Color- and shape-based detection vision algorithms are applied for robust detection under varying lighting conditions. Due to the insufficient accuracy of navigation sensors, a direct visual servoing is used for terminal guidance. The proposed algorithm is validated in a series of flight tests.     

Behaviour recognition of ground vehicle using airborne monitoring of unmanned aerial vehicles

This paper proposes a behaviour recognition methodology for ground vehicles moving within road traffic using unmanned aerial vehicles in order to identify suspicious or abnormal behaviour. With the target information acquired by unmanned aerial vehicles and estimated by filtering techniques, ground vehicle behaviour is first classified into representative driving modes, and then a string pattern matching theory is applied to detect suspicious behaviours in the driving mode history. Furthermore, a fuzzy decision-making process is developed to systematically exploit all available information obtained from a complex environment and confirm the characteristic of behaviour, while considering spatiotemporal environment factors as well as several aspects of behaviours. To verify the feasibility and benefits of the proposed approach, numerical simulations on moving ground vehicles are performed using realistic car trajectory data from an off-the-shelf traffic simulation software.     

Navigation of automated guided vehicles using magnet spot guidance method

Automated guided vehicle systems (AGVSs) are used to transport goods and products in most manufacturing systems. In this research, we use a cylindrical magnet spot, which is widely used in industrial AGVSs, to develop a guidance system for indoor AGV navigation. This paper describes the navigation and control system of an AGV by magnet spot guidance with a differential drive. Furthermore, Hall-effect sensors, encoders, and counters are employed to achieve control and continuous guidance. Existing guidance methods use a gyro sensor and dead reckoning with encoders to calibrate against steering angle errors. Here, the maximum value of the magnetic flux density of the magnet spot, which is obtained by the Hall-effect sensor, is used to calibrate against steering angle errors and as a navigation guide for the AGV. Furthermore, real-time corrections for wheel-skidding errors are accomplished with a fuzzy controller. Thus, high-precision continuous guidance with stable and satisfactory navigation at high speeds is achieved. This guidance method was applied to real manufacturing processes in a ceramic plant and steel-bar reinforcement plant to examine its control ability, stability, and effectiveness. The proposed method was found to be robust to disturbances and uncertainty problems during tracking.     

UGV协同系统研究进展*

无人地面车辆（UGV）在工业自动化、星球探索、灾后救援、智能交通以及军事作战等多任务领域都具有广阔的应用前景。UGV协同系统通过嵌入组织架构、合作策略、交互机制等协同内容,可以达到拓展环境感知范围、提高复杂环境理解适应能力和增强复杂任务工作效能的目的,受到了国内外广泛的关注。从UGV单体/群体体系结构、多重任务协同分配方法以及协同定位、编队、覆盖/探索等几个方面对目前国内外UGV协同工作关键技术进行了总结,给出了一个UGV协同系统的应用实例,并指出了系统发展趋势。     

Intelligent identification and control for autonomous guided vehicles using adaptive fuzzy-based algorithms

Fuzzy logic was first suggested as the mechanism by which humans drive cars. This paper addresses the use of fuzzy logic and algorithms towards the intelligent autonomous motion control of land vehicles. To cope with vehicle complexities, internal parametric changes, and with unpredictable environmental effects, the controllers that are presented, whilst heuristic in nature, are self-organizing or self-learning in that they generate automatically by observation an experiential rule base that models the vehicle, and via an appropriate performance index an optimal control rule base that is robust to large parametric changes. The methodology presented is applicable to any complex process which is too difficult to model or control using conventional methods, or which has relied on the experience of a human operator. An overview of fuzzy logic and static fuzzy logic control (akin to expert systems) is provided, together with illustrative examples.     

Unsupervised anomaly detection in unmanned aerial vehicles

A real-time anomaly detection solution indicates a continuous stream of operational and labelled data that must satisfy several resources and latency requirements. Traditional solutions to the problem rely heavily on well-defined features and prior supervised knowledge, where most techniques refer to hand-crafted rules derived from known conditions. While successful in controlled situations, these rules assume that good data is available for them to detect anomalies; indicating that these rules will fail to generalise beyond known scenarios.To investigate these issues, current literature is examined for solutions that can be used to detect known and unknown anomalous instances whilst functioning as an out-of-the-box approach for efficient decision-making. The applicability of the isolation forest is discussed for engineering applications using the Aero-Propulsion System Simulation dataset as a benchmark where it is shown to outperform other unsupervised distance-based approaches. Also, the authors have carried out real-time experiments on an unmanned aerial vehicle to highlight further applications of the method. Finally, some conclusions are drawn concerning its simplicity and robustness in handling diagnostic problems.     

复杂环境下多无人机协作式地面移动目标跟踪

针对多无人机(UAV)协同地面移动目标跟踪问题展开研究.提出一种基于主动感知的问题求解框架,建立多UAV协同目标跟踪问题模型;在此基础上,采用分布式无色信息滤波实现目标状态融合估计与预测;然后,基于预测目标状态,结合滚动时域控制与遗传算法设计一种多UAV在线协同航迹规划算法.仿真结果表明:结合预测目标状态在线优化UAV...     

Active sonar-based bottom-following for unmanned underwater vehicles

The problem of high-precision bottom-following in the proximity of the seabed for open-frame unmanned underwater vehicles (UUVs) is addressed in this paper. The suggested approach consists of the integration of a guidance and control system with an active multi-hypothesis extended Kalman filter, able to estimate the motion of the vehicle with respect to the bottom profile. The guidance module is based on the definition of a suitable Lyapunov function associated with the bottom-following task, while the motion controller is a conventional autopilot, performing autoheading, autodepth, and autospeed. The motion of the vehicle is estimated from range and bearing measurements supplied by a high-frequency pencil-beam profiling sonar. Moreover, a general-purpose sensor-based guidance and control system for advanced UUVs, able to manage active sensing-based guidance and motion estimation modules, is presented. An application of the proposed architecture to execute high-precision bottom-following using Romeo, a prototype UUV, developed by the Robotics Dept. of the Istituto Automazione Navale, is described. Experimental results of tests, conducted in a high-diving pool with the vehicle equipped with a sonar profiler, are presented.     

Cooperative actuator fault accommodation in formation flight of unmanned vehicles using relative measurements

In this article, the cooperative fault accommodation in formation flight of unmanned vehicles is investigated through a hierarchical framework. Three levels are envisaged, namely a low-level fault recovery (LLFR), a formation-level fault recovery (FLFR) and a high-level (HL). In the LLFR module, a recovery controller is designed by using an estimate of the actuator fault. A performance monitoring module is introduced at the HL hierarchy to identify a partially low-level (LL) recovered vehicle due to inaccuracy in the fault estimate which results in violating the error specification of the formation mission. The HL supervisor then activates the FLFR module to compensate for the performance degradations of the partially LL recovered vehicle at the expense of the other healthy vehicles. Both centralised and decentralised control approaches are developed for our proposed cooperative fault recovery technique. A robust H _∞ controller is designed in which the parameters of the controller are adjusted to accommodate for the partially LL-recovered vehicle by enforcing that the other healthy vehicles allocate more control effort to compensate for the performance degradations of the faulty vehicle. Numerical simulations for a formation flight of five satellites are provided in the deep space, which do indeed confirm the validity and effectiveness of our proposed analytical work.     

Immunity-based autonomous guided vehicles control

The human immune system is a self-organizing and highly distributed multi-agent system. These properties impart a high degree of robustness and performance that has created great interest in implementing engineering systems. This adopted engineering analogue is called artificial immune system (AIS). This paper presents an immunity-based control framework, which has the ability to detect changes, adapt to dynamic environment and coordinate vehicles activities for goals achievement, to deploy a fleet of autonomous guided vehicles (AGVs) for material handling in an automated warehouse. A robust and flexible automated warehousing system is achieved through the self-organized and fully decentralized origination of AGVs.     

Acoustic motion estimation and guidance for unmanned underwater vehicles

The problem of navigation, guidance and control of Unmanned Underwater Vehicles (UUVs) is addressed in this paper. A task-function based guidance system and an acoustic motion estimation module have been integrated with a conventional UUV autopilot within a two-layered hierarchical architecture for closed-loop control. The design of the guidance system is based on suitable Lyapunov functions that can handle the different manoeuvres involved in approaching a target. Range and bearing information provided by a pencil beam profiling sonar are processed by an Extended Kalman Filter based algorithm for motion estimation in a structured environment. The resulting Navigation Guidance and Control (NGC) system has been tested on Roby2, the UUV testbed developed at the Istituto Automazione Navale of Italy's National Research Council. The experimental set-up as well as modalities and results are discussed.     

Classical and fuzzy-genetic autopilot design for unmanned aerial vehicles

In this paper, an efficient strategy is proposed to design the altitude hold mode autopilot for a UAV which is non-minimum phase, and its model includes both parametric uncertainties and unmodeled nonlinear dynamics. This work has been motivated by the challenge of developing and implementing an autopilot that is robust with respect to these uncertainties. By combination of classic controller as the principal section of the autopilot and the fuzzy logic controller to increase the robustness in a single loop scheme, it is tried to exploit both methods advantages. The multi-objective genetic algorithm is used to mechanize the optimal determination of fuzzy logic controller parameters based on an efficient cost function that comprises undershoot, overshoot, rise time, settling time, steady state error and stability. Simulation results show that the proposed strategy performances are desirable in terms of the time response characteristics for both phugoid mode and short period mode, the robustness, and the adaptation of itself with respect to the large commands.     

Recognition of a landing platform for unmanned aerial vehicles by using computer vision-based techniques

The use of Unmanned Aerial Vehicles (UAVs) is growing significantly for many and varied purposes. During the mission, an outdoor UAV is guided by following the planned path using GPS signals. However, the GPS capability may become defective or the environment may be GPS-denied, and an additional safety aid is therefore required for the automatic landing phase that is independent of GPS data. Most UAVs are equipped with machine vision systems which, together with onboard analysis, can be used for safe, automatic landing. This contributes greatly to the overall success of autonomous flight.This paper proposes an automatic expert system, based on image segmentation procedures, that assists safe landing through recognition and relative orientation of the UAV and platform. The proposed expert system exploits the human experience that has been incorporated into the machine vision system, which is mapped into the proposed image processing modules. The result is an improved reliability capability that could be incorporated into any UAV, and is especially robust for rotary wing UAVs. This is clearly a desirable fail-safe capability.     

Detection of bodies in maritime rescue operations using unmanned aerial vehicles with multispectral cameras

In this study, we use unmanned aerial vehicles equipped with multispectral cameras to search for bodies in maritime rescue operations. A series of flights were performed in open‐water scenarios in the northwest of Spain, using a certified aquatic rescue dummy in dangerous areas and real people when the weather conditions allowed it. The multispectral images were aligned and used to train a convolutional neural network for body detection. An exhaustive evaluation was performed to assess the best combination of spectral channels for this task. Three approaches based on a MobileNet topology were evaluated, using (a) the full image, (b) a sliding window, and (c) a precise localization method. The first method classifies an input image as containing a body or not, the second uses a sliding window to yield a class for each subimage, and the third uses transposed convolutions returning a binary output in which the body pixels are marked. In all cases, the MobileNet architecture was modified by adding custom layers and preprocessing the input to align the multispectral camera channels. Evaluation shows that the proposed methods yield reliable results, obtaining the best classification performance when combining green, red‐edge, and near‐infrared channels. We conclude that the precise localization approach is the most suitable method, obtaining a similar accuracy as the sliding window but achieving a spatial localization close to 1 m. The presented system is about to be implemented for real maritime rescue operations carried out by Babcock Mission Critical Services Spain.     

基于DenseNet的无人汽车制动意图识别方法

无人汽车制动意图内部数据由于识别深度增加,会出现过度膨胀现象,导致制动意图数据收集完整度低、识别准确率差。提出基于DenseNet的无人汽车制动示意图识别方法。选择数据深度收集系统,收集无人汽车制动意图内部数据,结合电池保护模型深度分解汽车内部运行过程的能耗,以收集的初始内部数据为标准,整合无人汽车制动意图识别数据,拆分整合数据,防止数据过度膨胀。利用DenseNet的高学习度以及自适应学习性,加权均衡处理内部数据标定函数,设置一组基函数,并选择相应的DenseNet复制内部数据函数,自适应分析复制后的数据,完成制动意图识别。实验结果表明,制动意图数据收集完整度提高15.21%,识别准确率增强了23.68%。