The Model Identification for Small Unmanned Aerial Rotorcraft Based on Adaptive Ant Colony Algorithm

This paper proposes a model identification method to get high performance dynamic model of a small unmanned aerial rotorcraft.With the analysis of flight characteristics,a linear dynamic model is constructed by the small perturbation theory.Using the micro guidance navigation and control module,the system can record the control signals of servos,the state information of attitude and velocity information in sequence.After the data preprocessing,an adaptive ant colony algorithm is proposed to get optimal parameters of the dynamic model.With the adaptive adjustment of the pheromone in the selection process,the proposed model identification method can escape from local minima traps and get the optimal solution quickly.Performance analysis and experiments are conducted to validate the effectiveness of the identified dynamic model.Compared with real flight data,the identified model generated by the proposed method has a better performance than the model generated by the adaptive genetic algorithm.Based on the identified dynamic model,the small unmanned aerial rotorcraft can generate suitable control parameters to realize stable hovering,turning,and straight flight.     

Automated Flight Test and System Identification for Rotary Wing Small Aerial Platform Using Frequency Responses Analysis

This paper proposes an autopilot system that can be used to control the small scale rotorcraft during the flight test for linear-frequency-domain system identification. The input frequency-sweep is generated automatically as part of the autopilot control command. Therefore the bandwidth coverage and consistency of the frequency-sweep are guaranteed to produce high quality data for system identification. Beside that, we can set the safety parameters during the flight test （maximum roll/pitch value, minimum altitude, etc.） so the safety of the whole flight test is guaranteed. This autopilot system is validated using hardware in the loop simulator for hover flight condition.     

A novel beach litter analysis system based on UAV images and Convolutional Neural Networks

Beach litter monitoring programs play a key role in establishing effective management measures to preserve the ecological, scenic, and economic value of the coastal areas. In this study, an innovative analysis system is proposed for the automatic identification of beach debris (>2.5 cm) on aerial-photogrammetric images acquired by unmanned aerial vehicles (UAV) at different elevations. The workflow is based on a Convolutional Neural Network model designed for object segmentation and object recognition, and here used for instance segmentation tasks. Test cases were conducted along the Adriatic sector of the Apulia region (Italy), where the beaches have a remarkable economic importance, attracting national and international tourists, and ecological values, hosting species of high ecological value and protected areas. The results of the tests carried out in this study allowed defining 10 m as the desirable drone flight above ground. In addition, encouraging results have been obtained on the instance segmentation step, experimenting on real, synthetic and mixed data, produced by using a high-resolution blending technique. A beach litter density of 0.38 items m⁻² (on 0.66 items m⁻²), an F-score of 0.96 and a mAP of 0.67 has been achieved on real data. A novel metric for comparing works at the state-of-the-art (SOTA) in beach litter monitoring is also introduced, named “density-normalized F-score”. The proposed methodology represents a benchmark for the definition of a standardize procedure for the indirect evaluation and monitoring of the coastal environmental status. Besides allowing the investigation of large areas with limited human effort, the proposed system enables the evaluation of the beach litter spatial distribution and magnitude, providing useful information for the assessment of tailored beach quality indices.     

Modeling and Design of H-Infinity Controller for Piezoelectric Actuator LIPCA

<正> We proposed a dynamic model identification and design of an H-Infinity (i.e.H_∞) controller using a LightweightPiezo-Composite Actuator (LIPCA).A second-order dynamic model was obtained by using input and output data, and applyingan identification algorithm.The identified model coincides well with the real LIPCA.To reduce the resonating mode that istypical of piezoelectric actuators, a notch filter was used.A feedback controller using the H_∞ control scheme was designed basedon the identified dynamic model; thus, the LIPCA can be easily used as an actuator for biomemetic applications such as artificialmuscles or macro/micro positioning in bioengineering.The control algorithm was implemented using a microprocessor, analogfilters, and power amplifying drivers.Our simulation and experimental results demonstrate that the proposed control algorithmworks well in real environment, providing robust performance and stability with uncertain disturbances.     

Hypatia-trackRadar: A software for animal tracking using marine surveillance radars

Hypatia-trackRadar is a Java standalone application designed to help biologists extract and process bird movement data from marine surveillance radars. This application integrates simultaneous collection of radar data and field observations by allowing the user to link information gathered from visual observers (such as bird species and flock size) to the radar echoes. A virtual transparent sheet positioned on the radar screen allows the user to visually follow and track the echoes on the radar screen. The application translates the position of the echoes on the screen in a metric coordinate system. Based on time and spatial position of the echoes the software automatically calculates multiple flight parameters, such as ground speed, track length and duration. We validated Hypatia-trackRadar using an unmanned aerial vehicle. Here we present the features of this application software and its first use in a real case study in a raptor migration bottle-neck.     

Thermal soaring flight of birds and unmanned aerial vehicles

Thermal soaring saves much energy, but flying large distances in this form represents a great challenge for birds, people and unmanned aerial vehicles (UAVs). The solution is to make use of the so-called thermals, which are localized, warmer regions in the atmosphere moving upward with a speed exceeding the descent rate of birds and planes. Saving energy by exploiting the environment more efficiently is an important possibility for autonomous UAVs as well. Successful control strategies have been developed recently for UAVs in simulations and in real applications. This paper first presents an overview of our knowledge of the soaring flight and strategy of birds, followed by a discussion of control strategies that have been developed for soaring UAVs both in simulations and applications on real platforms. To improve the accuracy of the simulation of thermal exploitation strategies we propose a method to take into account the effect of turbulence. Finally, we propose a new GPS-independent control strategy for exploiting thermal updrafts.     

基于I/O特性的听觉非线性动态适应机制与前向掩蔽效应模型

听觉前向掩蔽效应是听觉动态非线性的重要表现, 也是听觉适应机制的重要表现, 由此产生听觉前后向参考编码机制,而传统的语音信号处理机制是帧与帧独立的线性处理方式,所以不能反映实际听觉系统的动态非线性适应特性。传统的听觉模型对这种动态非线性适应机制的模拟是通过自适应调节听觉耳蜗滤波器的增益来完成的, 这不管在计算量上还是在滤波器的稳定性设计上都有其缺陷。我们根据听觉的生理学机制,建立前馈自适应模型,从输入输出特性曲线上引出静态增益曲线,再在静态增益曲线上引入动态过程,由此建立的数学模型,利用心理学试验数据得到模型参数。试验结果表明,该模型能够很好地预测前向掩蔽效应,对听觉的动态非线性适应机制是一种很好的解释。     

The Eye in the Sky: Combined Use of Unmanned Aerial Systems and GPS Data Loggers for Ecological Research and Conservation of Small Birds

Technological advances for wildlife monitoring have expanded our ability to study behavior and space use of many species. But biotelemetry is limited by size, weight, data memory and battery power of the attached devices, especially in animals with light body masses, such as the majority of bird species. In this study, we describe the combined use of GPS data logger information obtained from free-ranging birds, and environmental information recorded by unmanned aerial systems (UASs). As a case study, we studied habitat selection of a small raptorial bird, the lesser kestrel Falco naumanni, foraging in a highly dynamic landscape. After downloading spatio-temporal information from data loggers attached to the birds, we programmed the UASs to fly and take imagery by means of an onboard digital camera documenting the flight paths of those same birds shortly after their recorded flights. This methodology permitted us to extract environmental information at quasi-real time. We demonstrate that UASs are a useful tool for a wide variety of wildlife studies.     

Model Identification of a Micro Air Vehicle

This paper is focused on the model identification of a Micro Air Vehicle （MAV） in straight steady flight condition. The identification is based on input-output data collected from flight tests using both frequency and time dorrtain techniques. The vehicle is an in-house 40 cm wingspan airplane. Because of the complex coupled, multivariable and nonlinear dynamics of the aircraft, linear SISO structures for both the lateral and longitudinal models around a reference state were derived. The aim of the identification is to provide models that can be used in future development of control techniques for the MAV.     

Fine-scale flight strategies of gulls in urban airflows indicate risk and reward in city living

Birds modulate their flight paths in relation to regional and global airflows in order to reduce their travel costs. Birds should also respond to fine-scale airflows, although the incidence and value of this remains largely unknown. We resolved the three-dimensional trajectories of gulls flying along a built-up coastline, and used computational fluid dynamic models to examine how gulls reacted to airflows around buildings. Birds systematically altered their flight trajectories with wind conditions to exploit updraughts over features as small as a row of low-rise buildings. This provides the first evidence that human activities can change patterns of space-use in flying birds by altering the profitability of the airscape. At finer scales still, gulls varied their position to select a narrow range of updraught values, rather than exploiting the strongest updraughts available, and their precise positions were consistent with a strategy to increase their velocity control in gusty conditions. Ultimately, strategies such as these could help unmanned aerial vehicles negotiate complex airflows. Overall, airflows around fine-scale features have profound implications for flight control and energy use, and consideration of this could lead to a paradigm-shift in the way ecologists view the urban environment.This article is part of the themed issue ‘Moving in a moving medium: new perspectives on flight’.     

An adaptive control algorithm for fed-batch culture

The implementation of adaptive control for a fed-batch culture in order to maximize the output of product based on a self-adjusting model is discussed in the present work. Optimization methods were applied to the generalized mathematical model of a fed-batch fermentation process to determine control algorithms that could be used for on-line process control. The efficiency of the proposed adaptive algorithms was investigated by simulating a model system. The model of amylotytic enzyme fermentation that was proposed by the authors was taken from a real process. Dynamic modelling has shown that the main problem of realization is connected with the on-line identification of the adaptive model's parameters. To avoid this problem, we have introduced special limitations on the parameters' time variations that increased the convergence of the identification algorithm. The results of the investigation have shown the efficiency of the proposed adaptive algorithms, and the results of this work should be investigated for real process control.     

Hybrid adaptive optimal control of anaerobic fluidized bed bioreactor for the de-icing waste treatment

Hybrid adaptive control strategy was developed and tested for the degradation of propylene glycol, a major component in de-icing waste, in an anaerobic fluidized bed bioreactor (AFBR). A linearized model with time-varying parameters was first employed to describe the dynamic behavior of the AFBR using a recursive off-line system identification method. A hybrid adaptive control strategy was then tested using a recursive off-line system identification routine followed by an on-line adaptive optimal control algorithm. The objective of the controller was to achieve the desired set point value of the propionate concentration (stand-alone control output variable) by manipulating the dilution rate (control input variable). To do so, the optimal control law was developed by minimizing a cost function with constraint equations. This novel idea was successfully applied to the underlying system for 200 h. The set point (700 mg HPrl(-1)) was achieved even in the case where the feed concentration suddenly increased by 50% (9000 mg HPrl(-1) to 13500 mg HPrl(-1)).     

Time series canopy phenotyping enables the identification of genetic variants controlling dynamic phenotypes in soybean

Advances in plant phenotyping technologies are dramatically reducing the marginal costs of collecting multiple phenotypic measurements across several time points. Yet, most current approaches and best statistical practices implemented to link genetic and phenotypic variation in plants have been developed in an era of single-time-point data. Here, we used time-series phenotypic data collected with an unmanned aircraft system for a large panel of soybean (Glycine max (L.) Merr.) varieties to identify previously uncharacterized loci. Specifically, we focused on the dissection of canopy coverage (CC) variation from this rich data set. We also inferred the speed of canopy closure, an additional dimension of CC, from the time-series data, as it may represent an important trait for weed control. Genome-wide association studies (GWASs) identified 35 loci exhibiting dynamic associations with CC across developmental stages. The time-series data enabled the identification of 10 known flowering time and plant height quantitative trait loci (QTLs) detected in previous studies of adult plants and the identification of novel QTLs influencing CC. These novel QTLs were disproportionately likely to act earlier in development, which may explain why they were missed in previous single-time-point studies. Moreover, this time-series data set contributed to the high accuracy of the GWASs, which we evaluated by permutation tests, as evidenced by the repeated identification of loci across multiple time points. Two novel loci showed evidence of adaptive selection during domestication, with different genotypes/haplotypes favored in different geographic regions. In summary, the time-series data, with soybean CC as an example, improved the accuracy and statistical power to dissect the genetic basis of traits and offered a promising opportunity for crop breeding with quantitative growth curves.     

轻小型无人机低空遥感及其在生态学中的应用进展

无人机低空遥感系统弥补了航天和航空遥感在影像分辨率、重访周期、云层影响以及高成本等方面的不足,为中观尺度的生态学研究提供了新方法.本文介绍了轻小型无人机低空遥感系统的组成,从物种、种群、群落和生态系统尺度综述了其在生态学中的应用现状,并指出目前存在的不足和未来的发展方向,以期为无人机生态学的后续研究提供参考.无人机生态学当前面临的挑战和未来发展的方向主要有物种形态和光谱特征库的建立、物种自动识别、光谱数据与植物生理生态过程之间关系的进一步挖掘、生态系统三维立体监测、多来源多尺度遥感数据融合等.随着无人机平台技术、传感器技术以及数据传输处理技术的成熟,以无人机低空遥感技术为基础的无人机生态学将迎来发展的机遇和曙光.     

Robust Models for Optic Flow Coding in Natural Scenes Inspired by Insect Biology

The extraction of accurate self-motion information from the visual world is a difficult problem that has been solved very efficiently by biological organisms utilizing non-linear processing. Previous bio-inspired models for motion detection based on a correlation mechanism have been dogged by issues that arise from their sensitivity to undesired properties of the image, such as contrast, which vary widely between images. Here we present a model with multiple levels of non-linear dynamic adaptive components based directly on the known or suspected responses of neurons within the visual motion pathway of the fly brain. By testing the model under realistic high-dynamic range conditions we show that the addition of these elements makes the motion detection model robust across a large variety of images, velocities and accelerations. Furthermore the performance of the entire system is more than the incremental improvements offered by the individual components, indicating beneficial non-linear interactions between processing stages. The algorithms underlying the model can be implemented in either digital or analog hardware, including neuromorphic analog VLSI, but defy an analytical solution due to their dynamic non-linear operation. The successful application of this algorithm has applications in the development of miniature autonomous systems in defense and civilian roles, including robotics, miniature unmanned aerial vehicles and collision avoidance sensors.     

基于无人机高清数码影像和高光谱遥感数据反演大豆典型生育期氮平衡指数

氮平衡指数(NBI)是反映作物长势的重要指标之一.通过测量NBI可以快速监测作物氮肥盈亏状况,为农业生产和管理提供精准信息.本文以无人机高清数码影像和高光谱遥感数据,以及地面实测大豆NBI数据为基础,分析大豆从开花期到成熟期,原始光谱和红外、近红外波段的导数光谱与NBI的相关性,筛选敏感波段并计算植被指数.采用经验模型法构建NBI反演模型,通过分析验证模型的决定系数(R^2)和均方根误差(RMSE),得出最佳反演模型.结果表明:大豆NBI与导数光谱反射率的相关性好于与原始光谱反射率的相关性;本文筛选的14个植被指数中,除了导数光谱光化学植被指数与大豆NBI呈不显著相关外,其余13个植被指数与大豆NBI呈极显著相关;利用13个植被指数构建NBI反演模型,并分析模型反演精度,结果显示,利用导数光谱差值植被指数构建NBI反演模型的精度最高,R^2和RMSE分别为0.771和3.077,利用该模型生成大豆典型生育期NBI分布图,能够反映大豆的长势状况.通过多载荷无人机获取的高清数码影像和高光谱遥感数据进行NBI估算,能够实时、动态、非破坏性、快速有效地监测大豆氮素营养状况,可为大豆氮肥精确管理提供简便实用的方法.     

Gravity-defying Behaviors: Identifying Models for Protoaves

Most current phylogenetic hypotheses based upon cladistic methodologyassert that birds are the direct descendants of derived maniraptorantheropod dinosaurs, and that the origin of avian flight necessarilydeveloped within a terrestrial context (i.e., from the "groundup"). Most theoretical aerodynamic and energetic models or chronologicallyappropriate fossil data do not support these hypotheses forthe evolution of powered flight. The more traditional modelfor the origin of flight derives birds from among small arborealearly Mesozoic archosaurs ("thecodonts"). According to thismodel, protoavian ancestors developed flight in the trees viaa series of intermediate stages, such as leaping, parachuting,gliding, and flapping. This model benefits from the assemblageof living and extinct arboreal vertebrates that engage in analogousnon-powered aerial activities using elevation as a source ofgravitational energy. Recent reports of "feathered theropods"notwithstanding, the evolution of birds from any known groupof maniraptoran theropods remains equivocal.     

Computation of a Stabilizing Set of Feedback Matrices bf a Large-scale Nonlinear Musculoskeletal Dynamic Model

The purpose of this study is to present a general mathematical framework to compute a set of feedback matrices which stabilize an unstable nonlinear anthropomorphic musculoskeletal dynamic model. This method is activity specific and involves four fundamental stages. First, from muscle activation data (input) and motion degrees-of-freedom (output) a dynamic experimental model is obtained using system identification schemes. Second, a nonlinear musculoskeletal dynamic model which contains the same number of muscles and degrees-of-freedom and best represents the activity being considered is proposed. Third, the nonlinear musculoskeletal model (anthropomorphic model) is replaced by a family of linear systems, parameterized by the same set of input/ output data (nominal points) used in the identification of the experimental model. Finally, a set of stabilizing output feedback matrices, parameterized again by the same set of nominal points, is computed such that when combined with the anthropomorphic model, the combined system resembles the structural form of the experimental model. The method is illustrated in regard to the human squat activity.     

Dynamic Modeling, Testing, and Stability Analysis of an Ornithoptic Blimp

In order to study omithopter flight and to improve a dynamic model of flapping propulsion,a series of tests are conducted on a flapping-wing blimp.The blimp is designed and constructed from mylar plastic and balsa wood as a test platform for aerodynamics and flight dynamics.The blimp,2.3 meters long and 420 gram mass,is propelled by its flapping wings.Due to buoyancy the wings have no lift requirement so that the distinction between lift and propulsion can be analyzed in a flight platform at low flight speeds.The blimp is tested using a Vicon motion tracking system and various initial conditions are tested including accelerating flight from standstill,decelerating from an initial speed higher than its steady state,and from its steady-state speed but disturbed in pitch angle.Test results are used to estimate parameters in a coupled quasi-steady aerodynamics/Newtonian flight dynamics model.This model is then analyzed using Floquet theory to determine local dynamic modes and stability.It is concluded that the dynamic model adequately describes the vehicle's nonlinear behavior near the steady-state velocity and that the vehicle's linearized modes are akin to those of a fixed-wing aircraft.     

Bayesian estimation of group sizes for a coastal cetacean using aerial survey data

Many small cetacean, sirenian, and pinniped species aggregate in groups of large or variable size. Accurate estimation of group sizes is essential for estimating the abundance and distribution of these species, but is challenging as individuals are highly mobile and only partially visible. We developed a Bayesian approach for estimating group sizes using wide‐angle aerial photographic or video imagery. Our approach accounts for both availability and perception bias, including a new method (analogous to distance sampling) for estimating perception bias due to small image size in wide‐angle images. We demonstrate our approach through an application to aerial survey data for an endangered population of beluga whales (Delphinapterus leucas) in Cook Inlet, Alaska. Our results strengthen understanding of variation in group size estimates and allow for probabilistic statements about the size of detected groups. Aerial surveys are a standard tool for estimating the abundance and distribution of various marine mammal species. The role of aerial photographic and video data in wildlife assessment is expected to increase substantially with the widespread uptake of unmanned aerial vehicle technology. Key aspects of our approach are relevant to group size estimation for a broad range of marine mammal, seabird, other waterfowl, and terrestrial ungulate species.