scholarly journals Preliminary Design of a Model-Free Synthetic Sensor for Aerodynamic Angle Estimation for Commercial Aviation

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5133 ◽  
Author(s):  
Angelo Lerro ◽  
Alberto Brandl ◽  
Manuela Battipede ◽  
Piero Gili
Keyword(s):  
Network Architecture ◽  
Simulated Data ◽  
Flight Test ◽  
Computational Effort ◽  
Unmanned Aircraft ◽  
Measure Data ◽  
Training Strategy ◽  
Model Free ◽  
Design Activities ◽  
Aerodynamic Angle

Heterogeneity of the small aircraft category (e.g., small air transport (SAT), urban air mobility (UAM), unmanned aircraft system (UAS)), modern avionic solution (e.g., fly-by-wire (FBW)) and reduced aircraft (A/C) size require more compact, integrated, digital and modular air data system (ADS) able to measure data from the external environment. The MIDAS project, funded in the frame of the Clean Sky 2 program, aims to satisfy those recent requirements with an ADS certified for commercial applications. The main pillar lays on a smart fusion between COTS solutions and analytical sensors (patented technology) for the identification of the aerodynamic angles. The identification involves both flight dynamic relationships and data-driven state observer(s) based on neural techniques, which are deterministic once the training is completed. As this project will bring analytical sensors on board of civil aircraft as part of a redundant system for the very first time, design activities documented in this work have a particular focus on airworthiness certification aspects. At this maturity level, simulated data are used, real flight test data will be used in the next stages. Data collection is described both for the training and test aspects. Training maneuvers are defined aiming to excite all dynamic modes, whereas test maneuvers are collected aiming to validate results independently from the training set and all autopilot configurations. Results demonstrate that an alternate solution is possible enabling significant savings in terms of computational effort and lines of codes but they show, at the same time, that a better training strategy may be beneficial to cope with the new neural network architecture.

Addressing pose estimation issues for machine vision based UAV autonomous serial refuelling

2007 ◽  
Vol 111 (1120) ◽  
pp. 389-396 ◽  
Author(s):  
G. Campa ◽  
M. R. Napolitano ◽  
M. Perhinschi ◽  
M. L. Fravolini ◽  
L. Pollini ◽  
...  
Keyword(s):  
Machine Vision ◽  
Pose Estimation ◽  
Estimation Algorithm ◽  
Computational Effort ◽  
Unmanned Aircraft ◽  
Light Sources ◽  
Estimation Algorithms ◽  
Trade Offs ◽  
Labeling Algorithms ◽  
Selection Of

Abstract This paper describes the results of an effort on the analysis of the performance of specific ‘pose estimation’ algorithms within a Machine Vision-based approach for the problem of aerial refuelling for unmanned aerial vehicles. The approach assumes the availability of a camera on the unmanned aircraft for acquiring images of the refuelling tanker; also, it assumes that a number of active or passive light sources – the ‘markers’ – are installed at specific known locations on the tanker. A sequence of machine vision algorithms on the on-board computer of the unmanned aircraft is tasked with the processing of the images of the tanker. Specifically, detection and labeling algorithms are used to detect and identify the markers and a ‘pose estimation’ algorithm is used to estimate the relative position and orientation between the two aircraft. Detailed closed-loop simulation studies have been performed to compare the performance of two ‘pose estimation’ algorithms within a simulation environment that was specifically developed for the study of aerial refuelling problems. Special emphasis is placed on the analysis of the required computational effort as well as on the accuracy and the error propagation characteristics of the two methods. The general trade offs involved in the selection of the pose estimation algorithm are discussed. Finally, simulation results are presented and analysed.

Routing Based on Security

2014 ◽  
pp. 365-380
Author(s):  
I. A. Almerhag
Keyword(s):  
Network Security ◽  
Standard Method ◽  
Blocking Probability ◽  
Computational Effort ◽  
Routing Algorithms ◽  
Measure Data ◽  
Security Metrics ◽  
Essential Requirement ◽  
Blocking Probabilities ◽  
Common Technique

Even though it is an essential requirement of any computer system, there is not yet a standard method to measure data security, especially when sending information over a network. However, the most common technique used to achieve the three goals of security is encryption. Three security metrics are derived from important issues of network security in this chapter. Each metric demonstrates the level of achievement in preserving one of the security goals. Routing algorithms based on these metrics are implemented to test the proposed solution. Computational effort and blocking probability are used to assess the behavior and the performance of these routing algorithms. Results show that the algorithms are able to find feasible paths between communicating parties and make reasonable savings in the computational effort needed to find an acceptable path. Consequently, higher blocking probabilities are encountered, which is the price to be paid for such savings.

scholarly journals Angle of Attack Sensor for Small Fixed-Wing Unmanned Aerial Vehicles

Proceedings ◽  
2020 ◽  
Vol 39 (1) ◽  
pp. 19
Author(s):  
Wanngoen ◽  
Saetunand ◽  
Saengphet ◽  
Tantrairatn
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Flow Direction ◽  
Air Flow ◽  
Angle Of Attack ◽  
Flight Test ◽  
Unmanned Aircraft ◽  
Aerodynamic Parameter ◽  
Aerial Vehicles ◽  
Flow Changes ◽  
Good Agreement

The angle of attack (AOA) is an important parameter for estimating aerodynamic parameter the performance and stability of aircraft. Currently, AOA sensors are used in general aircraft. However, there is no a reasonable-price AOA sensor that is compatible to a small fixed-wing unmanned aerial vehicles (UAVs). This research aims to designs and constructs angle of attract (AOA) sensor for small fixed-wing unmanned aircraft. Mechanism Design, which is similar to aerodynamic wheatear vane, can operate in airspeed 10–30 m/s. The direction of airfoil aligns with the air flow direction. When the AOA of the UAV changes, the air flow changes the direction, resulting in the change of airfoil direction. The high-resolution rotary encoder, that was used to measure the angle of the airfoil, was installed with the fin airfoil. For experiment, the accuracy of the AOA sensor was validated by comparing the angles obtained from the encoder with the standard rotary table in static and wind tunnel. Finally, the AOA sensor, which was attached on aircraft, was verified and recorded in flight test. As the results of the measurement, the airfoil angles detected by the encoder were in good agreement with the standard angles.

Improved Architecture Designs for a Low Cost Personal Remote Sensing Platform: Flight Control and Safety

2011 ◽  
Author(s):  
Calvin Coopmans ◽  
Long Di ◽  
Austin Jensen ◽  
Aaron A. Dennis ◽  
YangQuan Chen
Keyword(s):  
Remote Sensing ◽  
Flight Control ◽  
Large Scale ◽  
Current System ◽  
Low Cost ◽  
Flight Test ◽  
Unmanned Aircraft ◽  
Personal Remote Sensing ◽  
Sensing Platform ◽  
Fail Safe

Remote sensing is a field traditionally dominated by expensive, large-scale operations. This paper presents our efforts to improve our unmanned aircraft (UA) platforms for low-cost personal remote sensing purposes. Safety concerns are first emphasized regarding the local airspace and multiple fail-safe features are shown in the current system. Then the AggieAir unmanned system architecture is briefly described including the Paparazzi UA autopilot, AggieAir JAUS implementation, AggieNav navigation unit and payload integration. Some preliminary flight test results and images acquired using an example thermal IR payload system are also shown. Finally Multi-UAV and heterogeneous platform capabilities are discussed with respect to their applications. Based on our approaches on the new architecture design, personal remote sensing on smaller-scale operations can be more beneficial and common.

scholarly journals Model-free detection of unique events in time series

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Zsigmond Benkő ◽  
Tamás Bábel ◽  
Zoltán Somogyvári
Keyword(s):  
Simulated Data ◽  
Normal Activity ◽  
Detection Algorithm ◽  
Superior Performance ◽  
Data Series ◽  
Data Sets ◽  
Data Set ◽  
Detection Algorithms ◽  
Model Free ◽  
Unique Event

AbstractRecognition of anomalous events is a challenging but critical task in many scientific and industrial fields, especially when the properties of anomalies are unknown. In this paper, we introduce a new anomaly concept called “unicorn” or unique event and present a new, model-free, unsupervised detection algorithm to detect unicorns. The key component of the new algorithm is the Temporal Outlier Factor (TOF) to measure the uniqueness of events in continuous data sets from dynamic systems. The concept of unique events differs significantly from traditional outliers in many aspects: while repetitive outliers are no longer unique events, a unique event is not necessarily an outlier; it does not necessarily fall out from the distribution of normal activity. The performance of our algorithm was examined in recognizing unique events on different types of simulated data sets with anomalies and it was compared with the Local Outlier Factor (LOF) and discord discovery algorithms. TOF had superior performance compared to LOF and discord detection algorithms even in recognizing traditional outliers and it also detected unique events that those did not. The benefits of the unicorn concept and the new detection method were illustrated by example data sets from very different scientific fields. Our algorithm successfully retrieved unique events in those cases where they were already known such as the gravitational waves of a binary black hole merger on LIGO detector data and the signs of respiratory failure on ECG data series. Furthermore, unique events were found on the LIBOR data set of the last 30 years.

scholarly journals End-to-End Deep Reinforcement Learning for Image-Based UAV Autonomous Control

2021 ◽  
Vol 11 (18) ◽  
pp. 8419
Author(s):  
Jiang Zhao ◽  
Jiaming Sun ◽  
Zhihao Cai ◽  
Longhong Wang ◽  
Yingxun Wang
Keyword(s):  
Reinforcement Learning ◽  
Network Architecture ◽  
Control Method ◽  
Control Policy ◽  
Input Image ◽  
Autonomous Control ◽  
Policy Network ◽  
Model Free ◽  
Control Command ◽  
End To End

To achieve the perception-based autonomous control of UAVs, schemes with onboard sensing and computing are popular in state-of-the-art work, which often consist of several separated modules with respective complicated algorithms. Most methods depend on handcrafted designs and prior models with little capacity for adaptation and generalization. Inspired by the research on deep reinforcement learning, this paper proposes a new end-to-end autonomous control method to simplify the separate modules in the traditional control pipeline into a single neural network. An image-based reinforcement learning framework is established, depending on the design of the network architecture and the reward function. Training is performed with model-free algorithms developed according to the specific mission, and the control policy network can map the input image directly to the continuous actuator control command. A simulation environment for the scenario of UAV landing was built. In addition, the results under different typical cases, including both the small and large initial lateral or heading angle offsets, show that the proposed end-to-end method is feasible for perception-based autonomous control.

scholarly journals Variational Model-based Policy Optimization

2021 ◽  
Author(s):  
Yinlam Chow ◽  
Brandon Cui ◽  
Moonkyung Ryu ◽  
Mohammad Ghavamzadeh
Keyword(s):  
Objective Function ◽  
Simulated Data ◽  
Parameter Tuning ◽  
Variational Model ◽  
Continuous Control ◽  
Data Generation ◽  
Model Based ◽  
Model Free ◽  
And Performance ◽  
Policy Optimization

Model-based reinforcement learning (RL) algorithms allow us to combine model-generated data with those collected from interaction with the real system in order to alleviate the data efficiency problem in RL. However, designing such algorithms is often challenging because the bias in simulated data may overshadow the ease of data generation. A potential solution to this challenge is to jointly learn and improve model and policy using a universal objective function. In this paper, we leverage the connection between RL and probabilistic inference, and formulate such an objective function as a variational lower-bound of a log-likelihood. This allows us to use expectation maximization (EM) and iteratively fix a baseline policy and learn a variational distribution, consisting of a model and a policy (E-step), followed by improving the baseline policy given the learned variational distribution (M-step). We propose model-based and model-free policy iteration (actor-critic) style algorithms for the E-step and show how the variational distribution learned by them can be used to optimize the M-step in a fully model-based fashion. Our experiments on a number of continuous control tasks show that our model-based (E-step) algorithm, called variational model-based policy optimization (VMBPO), is more sample-efficient and robust to hyper-parameter tuning than its model-free (E-step) counterpart. Using the same control tasks, we also compare VMBPO with several state-of-the-art model-based and model-free RL algorithms and show its sample efficiency and performance.

scholarly journals The rapid development of bespoke small unmanned aircraft

2017 ◽  
Vol 121 (1245) ◽  
pp. 1683-1710 ◽  
Author(s):  
C. A. Paulson ◽  
A. Sóbester ◽  
J. P. Scanlan
Keyword(s):  
Additive Manufacturing ◽  
Optimal Design ◽  
Rapid Development ◽  
Simulated Data ◽  
Unmanned Aircraft ◽  
Flight Testing ◽  
Performance Predictions ◽  
Aircraft Performance ◽  
Aircraft System

ABSTRACTThe ability to quickly fabricate small unmanned aircraft system (sUAS) through Additive Manufacturing (AM) methods opens a range of new possibilities for the design and optimisation of these vehicles. In this paper, we propose a design loop that makes use of surrogate modelling and AM to reduce the design and optimisation time of scientific sUAS. AM reduces the time and effort required to fabricate a complete aircraft, allowing for rapid design iterations and flight testing. Co-Kriging surrogate models allow data collected from test flights to correct Kriging models trained with numerically simulated data. The resulting model provides physically accurate and computationally cheap aircraft performance predictions. A global optimiser is used to search this model to find an optimal design for a bespoke aircraft. This paper presents the design loop and a case study which demonstrates its application.

scholarly journals Emotion as an emergent phenomenon of the neurocomputational energy regulation mechanism of a cognitive agent in a decision-making task

2019 ◽  
pp. 105971231988064
Author(s):  
Murat Kirtay ◽  
Lorenzo Vannucci ◽  
Ugo Albanese ◽  
Cecilia Laschi ◽  
Erhan Oztop ◽  
...  
Keyword(s):  
Decision Making ◽  
Visual Memory ◽  
Computational Effort ◽  
Regulation Mechanism ◽  
Energy Regulation ◽  
Cognitive Mechanisms ◽  
Emergent Phenomenon ◽  
Model Free ◽  
The Cost ◽  
Perception Action

Biological agents need to complete perception-action cycles to perform various cognitive and biological tasks such as maximizing their wellbeing and their chances of genetic continuation. However, the processes performed in these cycles come at a cost. Such costs force the agent to evaluate a tradeoff between the optimality of the decision making and the time and computational effort required to make it. Several cognitive mechanisms that play critical roles in managing this tradeoff have been identified. These mechanisms include adaptation, learning, memory, attention, and planning. One of the often overlooked outcomes of these cognitive mechanisms, in spite of the critical effect that they may have on the perception-action cycle of organisms, is “emotion.” In this study, we hold that emotion can be considered as an emergent phenomenon of a plausible neurocomputational energy regulation mechanism, which generates an internal reward signal to minimize the neural energy consumption of a sequence of actions (decisions), where each action triggers a visual memory recall process. To realize an optimal action selection over a sequence of actions in a visual recalling task, we adopted a model-free reinforcement learning framework, in which the reward signal—that is, the cost—was based on the iteration steps of the convergence state of an associative memory network. The proposed mechanism has been implemented in simulation and on a robotic platform: the iCub humanoid robot. The results show that the computational energy regulation mechanism enables the agent to modulate its behavior to minimize the required neurocomputational energy in performing the visual recalling task.

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