Energy conservation based fuzzy tracking for unmanned aerial vehicle missions under a priori known wind information

Обозначены основные проблемы, связанные с разработкой интеллектуального решателя задач автоматической системы управления целенаправленным поведением интегрального беспилотного летательного аппарата в наземной проблемной среде. Рассмотрена конструкция типовых элементов представления знаний безотносительно к конкретной предметной области, позволяющих автономному беспилотному летальному аппарату, оснащенному манипулятором и системой технического зрения, решать сложные задачи в априори неописанных проблемных средах. Разработаны инструментальные средства разбиения сложных задач на подзадачи, обеспечивающие эффективный поиск их решения в пространстве состояний на основе типовых элементов представления знаний. Сформулированы основные методические положения, позволяющие синтезировать процедуры планирования целенаправленного поведения интегрального беспилотного летательного аппарата в сложных априори неописанных условиях функционирования. The main problems associated with the development of an intelligent problem solver of an automatic control system for the goal-seeking behavior of an integral unmanned aerial vehicle in a terrestrial problem environment are identified. The design of the typical elements of knowledge representation irrelative to the specific subject area is considered, allowing an autonomous unmanned aerial vehicle equipped with a manipulator and a vision system to solve complex problems in a priori undescribed problem environments. Tools are developed for partitioning complex tasks into sub-tasks, which provide an effective search for their solutions in the state space based on typical elements of knowledge representation. The main methodological provisions are formulated that allow synthesizing the planning procedures for the goal-seeking behavior of an integral unmanned aerial vehicle in a priori undescribed complex operating conditions.

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Lateral directional parameter estimation of a miniature unmanned aerial vehicle using maximum likelihood and Neural Gauss Newton methods

The Aeronautical Journal ◽

10.1017/aer.2018.36 ◽

2018 ◽

Vol 122 (1252) ◽

pp. 889-912

Author(s):

Subrahmanyam Saderla ◽

Dhayalan Rajaram ◽

A. K. Ghosh

Keyword(s):

Parameter Estimation ◽

Maximum Likelihood ◽

Unmanned Aerial Vehicle ◽

Rectangular Cross Section ◽

A Priori ◽

Flight Data ◽

Aerodynamic Model ◽

Estimated Parameters ◽

Aerial Vehicle ◽

Cramer Rao Bound

ABSTRACTThe current research paper describes the lateral-directional parameter estimation from flight data of a miniature Unmanned Aerial Vehicle (UAV) using Maximum Likelihood (ML), and Neural-Gauss-Newton (NGN) methods. An unmanned configuration with a cropped delta planform and thin rectangular cross-section has been designed, fabricated and instrumented. Exhaustive full-scale wind-tunnel tests were performed on the UAV to extract the form of aerodynamic model that has to be postulated a priori for parameter estimation. Rigorous flight tests have been performed to acquire the flight data for several prescribed manoeuvres. Four sets of compatible flight data have been used to carry out parameter estimation using classical ML and neural-network-based NGN methods. It is observed that the estimated parameters are consistent and the lower values of the Cramer-Rao bound for the corresponding estimates have shown significant confidence in the obtained parameters. Furthermore, to validate the aerodynamic model used and to enhance the confidence in the estimated parameters, a proof of match exercise has been carried out.

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A UAV Trajectory Planning Algorithm for Simultaneous Search and Track

Dynamic Systems and Control, Parts A and B ◽

10.1115/imece2005-81100 ◽

2005 ◽

Author(s):

John Tisdale ◽

J. Karl Hedrick

Keyword(s):

Unmanned Aerial Vehicle ◽

Constant Velocity ◽

A Priori ◽

Heuristic Method ◽

Reachability Graph ◽

Planning Algorithm ◽

Aerial Vehicle ◽

Target Path ◽

Areas Of Interest ◽

Turn Rate

This paper considers trajectories for an unmanned aerial vehicle (UAV) that must search an area while tracking a target. The UAV has a constrained turn rate and a constant velocity; it is assumed that there are certain areas of interest that have a higher search value than others. An algorithm is presented that seeks to maximize the value of the area searched while still maintaining the track. The problem is discretized in both time and the control; the motion of the UAV is constrained to the reachability graph, a subset of the forward reachable set. At each revisit, the target path is estimated for the next revisit. A heuristic method is used to determine the best UAV path, because the target path is not known a priori. Feasible paths are found by examining the terminating vertices of the reachability graph. A cooperative implementation, for a team of UAVs patrolling the same region, is developed. Simulation indicates the feasibility of the method for a real-time implementation. Trajectories for example scenarios are presented and discussed.

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Optimal control and state estimation for unmanned aerial vehicle under random vibration and uncertainty

Measurement and Control ◽

10.1177/0020294019866860 ◽

2019 ◽

Vol 52 (9-10) ◽

pp. 1264-1271 ◽

Cited By ~ 2

Author(s):

Mohammad Abdulrahman Al-Mashhadani

Keyword(s):

Optimal Control ◽

State Estimation ◽

Unmanned Aerial Vehicle ◽

Random Vibration ◽

Large Scale ◽

A Priori ◽

Number Of Factors ◽

Aerial Vehicle ◽

Vibration Noise ◽

Complicated Task

In the past decade, many approaches that attempted to solve the problem of optimal control and parameter estimation of an unmanned aerial vehicle with a priori uncertain parameters simply implied two ways to solve such problem. First, by the formation of optimal control based on a refined mathematical model of the unmanned aerial vehicle, and second, by using the estimation and identification methods of the model parameter of the unmanned aerial vehicle based on measured data from flight tests. However, the identification of the dynamic parameters of the unmanned aerial vehicle is a complicated task because of a number of factors such as random vibration noise, disturbance, and uncertainty of the sensor measurements. Due to the influence of random vibration noise, the problem of correlated vibration noises and uncertainty has encountered inevitably, and the accuracy of the state estimation for unmanned aerial vehicle is degraded. This study concentrates on the optimal control and state estimation for the unmanned aerial vehicle under the combination of both random vibration noise and uncertainty collected by the sensors. The effects of random vibrations at various stages of a large-scale flight that are a priori uncertain require the inclusion of identification algorithms in the optimal control loop. The results showed that the method used in the analysis had been able to provide accurate estimations.

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Planning the behavior of an integrated unmanned aerial vehicle based on the division of complex tasks into subtasks in the state space

MORSKIE INTELLEKTUAL`NYE TEHNOLOGII ◽

10.37220/mit.2020.49.3.033 ◽

2020 ◽

Author(s):

В.Б. Мелехин ◽

М.В. Хачумов

Keyword(s):

Behavior Problems ◽

State Space ◽

Unmanned Aerial Vehicle ◽

Semantic Network ◽

A Priori ◽

Semantic Networks ◽

The State ◽

Operating Conditions ◽

Complex Behavior ◽

Aerial Vehicle

В работе решается одна из актуальных проблем искусственного интеллекта связанная с разработкой модели представления и обработки знаний автономным интегральным беспилотным летательным аппаратом – роботом в процессе автоматического планирования целенаправленной деятельности в априори неописанных условиях проблемной среды. Предложенная модель базируется на применении различных сценариев в виде фрейм-микропрограмм поведения, фрейм-отношений и фрейм-действий, а также расплывчатых семантических сетей, обеспечивающих представление знаний безотносительно к конкретной предметной области. Это, в свою очередь, позволяет интегральному беспилотному летательному аппарату оснащенному манипулятором адаптироваться после посадки к априори неописанным условиям функционирования и решать на этой основе сложные задачи поведения. Использованы два способа шаблонного разбиения сложных задач поведения в пространстве состояний на более простые подзадачи, решение которых определяется на основе типовых элементов представления знаний и определения нечеткого вложенного изоморфизма и равенства одной расплывчатой семантической сети в другую. Разработаны процедуры планирования, которые позволяют интегральному беспилотному летательному аппарату эффективным образом выполнить преобразование текущей ситуации априори неописанной проблемной среды в ситуацию, определяемую заданной ему целью поведения, и на этой основе организовать целенаправленную деятельность в труднодоступных и агрессивных для человека средах. This work addresses one of the urgent problems of artificial intelligence related to the development of a model for the representation and processing of knowledge by an autonomous integrated unmanned aerial vehicle (robot) in the process of automatic planning of targeted activities in a priori undescribed conditions of a problem environment. The proposed model is based on the application of various scenarios in the form of frame-microprograms of behavior, frame-relations and frame-actions, as well as vague semantic networks that provide the representation of knowledge without reference to a specific subject area. This, in turn, allows an integrated unmanned aerial vehicle equipped with a manipulator to adapt after landing to a priori unknown operating conditions and to solve complex behavior problems on this basis. Two methods of template partitioning of complex behavior problems in the state space into simpler subtasks are used, the solution of which is determined on the basis of typical elements of knowledge representation and definition of fuzzy embedded isomorphism and equality of one vague semantic network to another. Planning procedures have been developed that allow the integrated unmanned aerial vehicle to efficiently transform the current situation of an a priori undescribed problem environment into a situation determined by the goal of behavior, and on this basis to organize goal-seeking activities in hard-to-reach and aggressive environments for humans.

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Monitoring snow depth change across a range of landscapes with ephemeral snow packs using Structure from Motion applied to lightweight unmanned aerial vehicle videos

10.5194/tc-2018-82 ◽

2018 ◽

Cited By ~ 1

Author(s):

Richard Fernandes ◽

Christian Prevost ◽

Francis Canisius ◽

Sylvain G. Leblanc ◽

Matt Maloley ◽

...

Keyword(s):

Unmanned Aerial Vehicle ◽

Structure From Motion ◽

Snow Depth ◽

A Priori ◽

Horizontal Resolution ◽

Flat Surfaces ◽

Alpine Regions ◽

Surface Models ◽

Order Of Magnitude ◽

Aerial Vehicle

Abstract. Snow depth (SD) can vary by more than an order of magnitude over length scales of metres due to topography, vegetation and microclimate. Differencing of digital surface models derived from Structure from Motion (SfM) processing of airborne imagery has been used to produce SD maps with between ∼2 cm to ∼15 cm horizontal resolution and accuracies on the order of ±10 cm over both relatively flat surfaces with little or no vegetation and over alpine regions. Studies indicate that accuracy is lower in the presence of vegetation above or below the snowpack and in rough topography; suggesting that some biases may be temporally persistent. Moreover, flight and image parameters vary across studies but they are typically not related a priori to an expected uncertainty in SD. This study tests two hypotheses: i) that SD change can be more accurately estimated when differencing snow covered elevation surfaces rather than the absolute snow depth based on differencing a snow covered and snow free surface and ii) the vertical accuracy of SfM processing of imagery acquired by commercial light weight unmanned aerial vehicle (UAV) systems can be adequately modelled using conventional photogrammetric theory. Moreover, these hypotheses are tested over areas with ephemeral snow pack conditions and across a range of micro-topography and vegetation cover. Weekly SD maps with

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Cooperative Navigation between a Ground Vehicle and an Unmanned Aerial Vehicle in GNSS-Challenged Environments

Proceedings of the 29th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2016) ◽

10.33012/2016.14623 ◽

2016 ◽

Author(s):

Victor O. Sivaneri ◽

Jason N. Gross

Keyword(s):

Unmanned Aerial Vehicle ◽

Ground Vehicle ◽

Cooperative Navigation ◽

Aerial Vehicle

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Study on Fault Diagnosis of Planetary Gearbox in Unmanned Aerial Vehicle Using Multi sensor Data

Journal of Applied Reliability ◽

10.33162/jar.2020.12.20.4.332 ◽

2020 ◽

Vol 20 (4) ◽

pp. 332-342

Author(s):

Hyung Jun Park ◽

Seong Hee Cho ◽

Kyung-Hwan Jang ◽

Jin-Woon Seol ◽

Byung-Gi Kwon ◽

...

Keyword(s):

Fault Diagnosis ◽

Unmanned Aerial Vehicle ◽

Sensor Data ◽

Planetary Gearbox ◽

Aerial Vehicle

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Results of Spatial Structure of Atmosphere Radiation in a Spectral Range (1.5–2) µm Research

Light & Engineering ◽

10.33383/2017-061 ◽

2018 ◽

pp. 7-13

Author(s):

Anton M. Mishchenko ◽

Sergei S. Rachkovsky ◽

Vladimir A. Smolin ◽

Igor V . Yakimenko

Keyword(s):

Spatial Structure ◽

Unmanned Aerial Vehicle ◽

Wavelength Range ◽

Spectral Range ◽

Near Ir ◽

Optoelectronic Systems ◽

Aerial Vehicle

Results of experimental studying radiation spatial structure of atmosphere background nonuniformities and of an unmanned aerial vehicle being the detection object are presented. The question on a possibility of its detection using optoelectronic systems against the background of a cloudy field in the near IR wavelength range is also considered.

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