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Graduate Doctoral Dissertations

Design framework of uav-based environment sensing, localization, and imaging system.

Yue Sun , University of Massachusetts Boston Follow

Date of Award

Document type.

Open Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Computer Science

First Advisor

Honggang Zhang

Second Advisor

Dan Simovici

Third Advisor

Marc Pomplun

In this dissertation research, we develop a framework for designing an Unmanned Aerial Vehicle or UAV-based environment sensing, localization, and imaging system for challenging environments with no GPS signals and low visibility. The UAV system relies on the various sensors that it carries to conduct accurate sensing and localization of the objects in an environment, and further to reconstruct the 3D shapes of those objects. The system can be very useful when exploring an unknown or dangerous environment, e.g., a disaster site, which is not convenient or not accessible for humans. In addition, the system can be used for monitoring and object tracking in a large scale environment, e.g., a smart manufacturing factory, for the purposes of workplace management/safety, and maintaining optimal system performance/productivity.

In our framework, the UAV system is comprised of two subsystems: a sensing and localization subsystem; and a mmWave radar-based 3D object reconstruction subsystem.

The first subsystem is referred to as LIDAUS (Localization of IoT Device via Anchor UAV SLAM), which is an infrastructure-free, multi-stage SLAM (Simultaneous Localization and Mapping) system that utilizes a UAV to accurately localize and track IoT devices in a space with weak or no GPS signals. The rapidly increasing deployment of Internet of Things (IoT) around the world is changing many aspects of our society. IoT devices can be deployed in various places for different purposes, e.g., in a manufacturing site or a large warehouse, and they can be displaced over time due to human activities, or manufacturing processes. Usually in an indoor environment, the lack of GPS signals and infrastructure support makes most existing indoor localization systems not practical when localizing a large number of wireless IoT devices. In addition, safety concerns, access restriction, and simply the huge amount of IoT devices make it not practical for humans to manually localize and track IoT devices. Our LIDAUS is developed to address these problems. The UAV in our LIDAUS system conducts multi-stage 3D SLAM trips to localize devices based only on Received Signal Strength Indicator (RSSI), the most widely available measurement of the signals of almost all commodity IoT devices. Our simulations and experiments of Bluetooth IoT devices demonstrate that our system LIDAUS can achieve high localization accuracy based only on RSSIs of commodity IoT devices.

Build on the first subsystem, we further develop the second subsystem for environment reconstruction and imaging via mmWave radar and deep learning. This subsystem is referred to as 3DRIMR/R2P (3D Reconstruction and Imaging via mmWave Radar/Radar to Point Cloud). It enables an exploring UAV to fly within an environment and collect mmWave radar data by scanning various objects in the environment. Taking advantage of the accurate locations given by the first subsystem, the UAV can scan an object from different viewpoints. Then based on radar data only, the UAV can reconstruct the 3D shapes of the objects in the space. mmWave radar has been shown as an effective sensing technique in low visibility, smoke, dusty, and dense fog environment. However, tapping the potential of radar sensing to reconstruct 3D object shapes remains a great challenge, due to the characteristics of radar data such as sparsity, low resolution, specularity, large noise, and multi-path induced shadow reflections and artifacts. Hence, it is challenging to reconstruct 3D object shapes based on the raw sparse and low-resolution mmWave radar signals.

To address the challenges, our second subsystem utilizes deep learning models to extract features from sparse raw mmWave radar intensity data, and reconstructs 3D shapes of objects in the format of dense and detailed point cloud. We first develop a deep learning model to reconstruct a single object’s 3D shape. The model first converts mmWave radar data to depth images, and then reconstructs an object’s 3D shape in point cloud format. Our experiments demonstrate the significant performance improvement of our system over the popular existing methods such as PointNet, PointNet++ and PCN. Then we further explore the feasibility of utilizing a mmWave radar sensor installed on a UAV to reconstruct the 3D shapes of multiple objects in a space. We evaluate two different models. Model 1 is 3DRIMR/R2P model, and Model 2 is formed by adding a segmentation stage in the processing pipeline of Model 1. Our experiments demonstrate that both models are promising in solving the multiple object reconstruction problem. We also show that Model 2, despite producing denser and smoother point clouds, can lead to higher reconstruction loss or even missing objects. In addition, we find that both models are robust to the highly noisy radar data obtained by unstable Synthetic Aperture Radar (SAR) operation due to the instability or vibration of a small UAV hovering at its intended scanning point. Our research shows a promising direction of applying mmWave radar sensing in 3D object reconstruction.

Recommended Citation

Sun, Yue, "Design Framework of UAV-Based Environment Sensing, Localization, and Imaging System" (2022). Graduate Doctoral Dissertations . 795. https://scholarworks.umb.edu/doctoral_dissertations/795

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Adaptive control of Unmanned Aerial Systems

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Optimal Task Scheduling and Flight Planning for Multi-Task Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs), also known as drones, play an important role in various areas due to their agility and versatility. Integrated with many embedded components, the UAV is capable of conducting multiple tasks simultaneously. Coordinating different tasks to a multi-task UAV can be challenging. The reason is that tasks may require different levels of commitment and tolerate different latencies. Another reason is that multi-tasking can give rise to difficulties in the UAV's energy management, as many UAVs are battery-powered. In this thesis, we study the optimal flight planning, control, and routing for the multi-task UAV.The main contributions of this thesis can be summarized as follows.• This thesis presents a novel energy-efficient UAV flight planning framework, which integrates UAVs into intelligent transportation systems for energy-efficient, delay-sensitive delivery services. The UAV can dynamically choose actions from cruise speed, full speed, recharging at a roadside charging station, or hitchhiking and recharging on a collaborative vehicle. The objective is to minimize the energy consumption of the UAV and ensure timely delivery. We reveal the conditions under which the UAV's flight planning changes in terms of the remaining flight distance or the elapsed time. Consequently, the optimal flight planning can be instantly made by comparing with the thresholds.• This thesis presents a new online control framework for multi-task UAVs, which allows a UAV to perform in-situ sensing while delivering goods. A new finite-horizon Markov decision process (FH-MDP) problem is formulated to ensure timely delivery, minimize the UAV's energy consumption, and maximize its reward for in-situ sensing. We prove the monotonicity and subadditivity of the FH-MDP, such that the FH-MDP has an optimal, monotone deterministic Markovian policy. We find that the optimal policy consists of flight distancerelated and time-related thresholds at which the optimal action of the UAV switches. As a result, the optimal actions of the UAV can be obtained by comparing its state with the thresholds at a linear complexity.• This thesis presents a novel multi-task UAV routing framework, which aims to minimize the UAV's energy consumption, maximize its sensing reward, and ensure its timely arrival at the destination. We interpret possible flight waypoints as location-dependent tasks, hence accommodating the waypoints and in-situ sensing in a unified process of task selection. We construct a weighted time-task graph, and transform the optimal routing of the UAV to a weighted routing problem, which can be optimally solved by the celebrated Bellman-Ford algorithm.

• Smart cities; Unmanned aerial vehicles; Energy consumption; Sensors; Aerospace engineering; Energy; Robotics; Transportation; Urban planning

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Multidisciplinary Design Optimization of UAV Under Uncertainty

2017, Journal of Aerospace Technology and Management

Abstract: Uncertainty-based multidisciplinary design optimization considers probabilistic variables and parameters and provides an approach to account for sources of uncertainty in design optimization. The aim of this study was to apply a decoupling uncertainty-based multidisciplinary design optimization method without any dependence on probability mathematics. Existing approaches of uncertainty- based multidisciplinary design optimization are based on probability mathematics (transformation to standard space), calculating an approximation of the constraint functions in standard space and finding the most probable point, which is the best possible one. The current approach used in this paper was inspired on interval modeling, so it is good when there is insufficient data to develop a good estimate of the probability density function shape or parameters. This approach has been implemented for an existing Unmanned Aerial Vehicle (UAV, Global Hawk) designed for purposes of comparison and validation. The advantages of the provided approach are independence of probability mathematics, appropriate when there is insufficient data to approximate the uncertainties variables, appropriate speed to calculate the best reliable response, and proper success rate in the presence of uncertainties.

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Coupled Flexible and Flight Dynamics Modeling and Simulation of a Full-Wing Solar-Powered Unmanned Aerial Vehicle

• Published: 28 February 2021
• Volume 101 , article number  56 , ( 2021 )

Cite this article

• An Guo   ORCID: orcid.org/0000-0003-3988-0116 1 ,
• Zhou Zhou 1 ,
• Xiaoping Zhu 2 &
• Xin Zhao 1  

292 Accesses

5 Citations

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The full-wing solar-powered unmanned aerial vehicle (UAV) adopts a large aspect ratio wing, a lightweight structural design, and a differential throttle control scheme to maximize flight endurance. Large structural deformation often occurs when the wing is heavily loaded, which affects its flight stability, trajectory tracking accuracy, and flight performance. The traditional rigid-body flight dynamics cannot accurately describe the actual dynamic behavior when the wing is deformed. To fully consider the coupling effect of the structural deformation and the flight motion, we derive a UAV combo model consisting of a flexible wing and rigid fuselage. In the model, we also include strain formulation (s-beam) for structural modeling, finite-state induced flow theory for aerodynamic modeling, static and dynamic combined experiments for engine modeling, and the rigid-body flight dynamic equation. Besides, a model modification method based on flight data is applied to improve the accuracy of the structural parameters. Simulation results show that the wingtip deformation and motion characteristics of the rigid- and combo-system are quite different: the combo model exhibits a certain lag in comparison with the rigid-body, with the amplitude of the motion parameters reduced by 50%, frequency 15%, system kinetic energy 11.8%, and the elevator control efficiency more than 40%.

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Acknowledgments

This work was supported in part by the Equipment Pre-research Project of China under Grant 41411010401, the National Key R&D Program in Shaanxi Province under Grant 2018ZDCXL-GY-03004, and the Innovation Program of Research Institutions under Grant TC2018DYDS24. The authors would like to express their sincere gratitude to the Editor-in-Chief, the Guest Editors, and the anonymous reviewers whose insightful comments have helped to improve the quality of this paper considerably.

Availability of Data and Materials

All data generated or analyzed during this study are included in this published manuscript, and all authors of this paper are responsible for the authenticity of the data.

This work was supported in part by the Equipment Pre-research Project of China under Grant 41411010401, the National Key R&D Program in Shaanxi Province under Grant 2018ZDCXL-GY-03004, and the Innovation Program of Research Institutions under Grant TC2018DYDS24.

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Authors and affiliations.

School of Aeronautics, Northwestern Polytechnical University, Xi’an, 710072, China

An Guo, Zhou Zhou & Xin Zhao

Science and Technology on UAV Laboratory, Northwestern Polytechnical University, Xi’an, China

Xiaoping Zhu

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Contributions

The first author, An Guo , undertook research acquisition, UAV modeling, data analysis, figure and table production, experimental design and completion of the paper, wrote the manuscript and revised the paper based on relevant comments, and was responsible for all aspects of the paper to ensure its authenticity.

The second author, Zhou Zhou , undertook the overall idea design, numerical analysis, and experimental design in the research work of this paper, and participated in the writing and review of the paper, and was responsible for all aspects of the paper as the corresponding author and the head of the funded project to ensure the authenticity of the paper.

The third author, Xiaoping Zhu , undertook the system modeling and experimental design of the UAV in the research work of this paper, made important contributions to the field test design of the UAV, helped to complete the verification of the simulation results, and was responsible for all aspects of the paper to ensure the authenticity of the paper.

The fourth author, Xin Zhao , undertook the literature search, simulation experimental verification, and helped to complete the writing and revision of the paper, and made important contributions to the work of the paper, and could be responsible for all aspects of the paper to ensure the authenticity of the paper.

Corresponding author

Correspondence to Zhou Zhou .

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All authors of this paper have been informed of the revision and publication of the paper, have checked all data, figures and tables in the manuscript, and are responsible for their truthfulness and accuracy.

Names of all contributing authors: An Guo ; Zhou Zhou ; Xiaoping Zhu ; Xin Zhao.

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Guo, A., Zhou, Z., Zhu, X. et al. Coupled Flexible and Flight Dynamics Modeling and Simulation of a Full-Wing Solar-Powered Unmanned Aerial Vehicle. J Intell Robot Syst 101 , 56 (2021). https://doi.org/10.1007/s10846-021-01343-z

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Received : 25 May 2020

Accepted : 08 February 2021

Published : 28 February 2021

DOI : https://doi.org/10.1007/s10846-021-01343-z

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• Solar-powered UAV
• Differential throttle
• Rigid-flexible combo model
• Strain-beam formulation
• Structure property modification
• Coupled flight dynamics
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Dr. Subrahmanyam Saderla

Department of aerospace engineering indian institute of technology kanpur research interests:, real time aircraft parameter estimation aerodynamic charecterstics of uavs using flight testing methods aircraft instrumentation and control aircraft simulation unmanned aerial vehicle design experimental flight dynamics drone design and development exploring ai based controller design for uavs for uavs operating in difficult terrains controller reconfiguration of uavs using online system identification, my active research areas are application of real time system identification for online fault diagnosis of an aircraft under distress, online system identification of aircraft undergoing icing condition, application of parameter estimation for uav certification, and adaptive control using realtime system identification..

Assistant Professor

Department of Aerospace Engineering

• Website: https://home.iitk.ac.in/~saderlas/
• Phone: +91-512-259-2009
• Degree: PhD (IIT Kanpur, IN)
• E-mail: [email protected]
• Unmanned aerial vehicles system identification and its applications
• System Identification for Flight Vehicles and Optimization techniques for UAV design

Development

Personal and Professional Information

Subrahmanyam Saderla

5+ years of teaching experience.

• UAV Laboratory, Department of Aerospace Engineering, IIT Kanpur, India
• +91-512-259-2009

[email protected]

Doctor of Philosophy

Thesis Title: Parameter Estimation using Flight Data of Unmanned Flight Vehicles at Low and Moderately High Angles of Attack Using Conventional Methods

Indian Institute of Technology Kanpur, India

Master of Technology

Thesis Title: Aerodynamic Characterizatin and Trajectory estimation of guided projectiles.

Bachelor of Technology

Institute of Aeronautical Engineering

Hyderabad, India

Professional Experience

Jan, 2018 - present.

Department of Aerospace Engineering, IIT Kanpur, India

Postdoctoral Researcher

Sep, 2016 - dec, 2017.

Gyeongsang National University, Jinju, South Korea

Chief Technical Officer (CTO)

Oct, 2015 - aug, 2016.

Srashta Automation Labs Pvt. Ltd., India

Project Scientist, Flight Dynamics

Jul, 2015 - sep, 2015, collaborations, vu-dynamics private limited.

Start-up incubated at IITK-FIRST under Faculty Entrepreneurship Program

Role: Director

TISA Aerospace Private Limited

Role: Advisor

LAB Facilities

Useful in developing the cutting-edge unmanned systems

UAV Testing and Prototyping Facilities

• Dynamic thrust calibration test bench for microjet engines, mini IC engines, Electric Ducted Fans and Brushless Motor
• Static Coaxial Thrust bench for BLDC motors
• UAV rapid prototyping setup using 3D printing
• Ground control Station for UAV communication and flight testing
• Drone battery pack testing and development setup (Lithium Ion and Lithium Polymer)
• Composite curing ovens with vacuum bagging facility (4m x 2m x 2m)
• UAV mold fabrication facility using 3axis CNC router (2.8m x 1.5m x 0.25m)
• Non-metallic template machining facility using laser cutting (1.5m x 1 m x 0.012m)
• Miniature calibration dynamic test rig facility for drone UAV ground station

Developed Technologies

• Real time crowd monitoring/surveillance solution using Aerostat
• Remote pilot training module for UAVs
• Software in the loop simulator for Kamikaze drone
• Tandem wing catapult/canister launched Kamikaze drone
• Hand launch flying wing drone for surveillance
• Pneumatic Catapult Launcher for UAVs
• Pneumatic Canister Launcher for foldable fixed wing UAVs

Publications

Published articles.

Kumar, N., Kishore, G., Saderla, S. , and Kim, Y., 2023. An adaptive law based online system identification of cropped delta unmanned aerial vehicles from flight tests, Aerospace Science and Technology, Volume 141. 108526, ISSN 1270-9638, https://doi.org/10.1016/j.ast.2023.108526

Kumar, N., Saderla, S. , and Kim, Y., 2023. Aerodynamic characterisation of delta wing unmanned aerial vehicle using non-gradient-based estimator. The Aeronautical Journal, 127(1314), 1435-1451. https://doi.org/10.1017/aer.2023.2

Samuel J, J., Kumar, N., Saderla, S. , and Kim, Y., 2023. Parametric model identification of delta wing UAVs using filter error method augmented with particle swarm optimisation. The Aeronautical Journal, 127(1312), 982-1008. https://doi.org/10.1017/aer.2022.100

Kumar, N., Saderla, S. , and Kim, Y., 2023. System identification of cropped delta UAVs from flight test methods using particle Swarm-Optimisation-based estimation. The Aeronautical Journal, 127(1307), 76-96. https://doi.org/10.1017/aer.2022.46

Kumar, N., S. Saderla , 2022. System identification of cropped delta UAVs from flight test methods using particle Swarm-Optimisation-based estimation. The Aeronautical Journal , .

S. Saderla , Dhayalan R, Kirtiman Singh, Neetesh Kumar and Ghosh, A.K., 2019. Longitudinal and lateral directional aerodynamic characterization of reflex wing UAV from flight tests using ML, LS and NGN methods. The Aeronautical Journal , 123 (1269), 1807-1839.

Gwang-gyo Seo , Yoonsoo Kim, S. Saderla , 2019. Kalman-filter based online system identification of fixed-wing aircraft in upset condition. Aerospace Science and Technology , 89, 307-317.

S. Saderla , Yoonsoo Kim, A K Ghosh, 2018. Online system Identification of mini cropped delta UAVs using flight test methods. Aerospace Science and Technology , 80, 337-353.

S. Saderla , Rajaram, D., Ghosh, A., 2018. Lateral directional parameter estimation of a miniature unmanned aerial vehicle using maximum likelihood and Neural Gauss-Newton methods. The Aeronautical Journal , 122 (1252), 889-912.

Dhayalan R., S. Saderla , Ajoy Kanti Ghosh, 2018. Parameter estimation of UAV from flight data using neural network. Aircraft Engineering and Aerospace Technology , 90 (Issue 2), 302-311.

S. Saderla , Dhayalan, R., Ghosh, A., 2017. Non-linear aerodynamic modelling of unmanned cropped delta configuration from experimental data. The Aeronautical Journal , 121 (1237), 320-340.

S. Saderla , Rajaram, Dhayalan, Ghosh, A K, 2017. Parameter Estimation of Unmanned Flight Vehicle Using Wind Tunnel Testing and Real Flight Data. Journal of Aerospace Engineering (ASCE) , 30 (1), 04016078.

S. Saderla , R. Dhayalan, A. K. Ghosh, 2017. Parameter estimation from near stall flight data using conventional and neural-based methods. Defense Science Journal, 67 (1), 03-11.

S. Saderla , R. Dhayalan, A. K. Ghosh, , "Aerodynamic parameter estimation using neuro-fuzzy model based method," 2017 First International Conference on Recent Advances in Aerospace Engineering (ICRAAE), Coimbatore, India, 2017, pp. 1-5, doi: 10.1109/ICRAAE.2017.8297220

S. Saderla , Rajaram, Dhayalan, Ghosh, A K, 2016. Longitudinal parameter estimation from real flight data of unmanned cropped delta flat plate configuration. International Journal of Intelligent Unmanned Systems , 4(1), 2-22.

List of current students/staff

Ph.D. (On-going)

Neetesh kumar.

“Real-time System Identification for Reconfigurable Control of Marginally Stable High-speed Aircraft.”

Gulivindala Kishore

“Fault Detection, Isolation, and Reconfiguration Methods for UAV”

Sagar Ghosal

Student name.

“Thesis title”

• Roll Number:
• E-mail: [email protected]

M.Tech. (On-going)

2022 - 2024 batch.

"Terminal guidance of Fixed wing unmanned aerial vehicle in Real time estimation"

Sonam Chorol

"System Identification with software-in-loop simulation using gain tuning in PX4 algorithm for non-linear flight envelope."

Mohammad Sadeeq Shah

"Design optimisation of VTOL UAV."

2023 - 2025 Batch

Vamshi krishna r.

M.Tech. Student (On-going)

Project Title: Terminal guidance of Fixed wing unmanned aerial vehicle in Real time estimation.

Project Title: System Identification with software-in-loop simulation using gain tuning in PX4 algorithm for non-linear flight envelope.

MS[R] Student (On-going)

Project Title: Design optimisation of VTOL UAV.

Project Title: NA.

Ph.D. (Completed)

Thesis Title.

M.Tech. (Completed)

Pokar monil.

Design Optimization of UAV Propellers for Hover Performance Using Particle Swarm Optimization.

Gaurav Kumar

Attitude Stabilization and Command Tracking of a Delta Wing UAV using Adaptive Sliding Mode Control.

Tadesse Yohannes

Design of Autopilot for Delta Wing UAVs.

Samuel Jenkins Paul J

Controller in Loop System Identification of an Unstable Combat UAV.

Aishwarya Rani

Dynamics and Control of Canard Guided Artillery Shell Projectile.

Alvin Sebastian

Modelling, Simulation, and Control of Active Side Stick in Modern Combat Aircraft.

Shivam Goswami

System identification with software-in-loop simulation using gain tuning in PX4 algorithm.

Challa Sai Kumar

Implementation of Formation Control for a manoeuvring Swarm of Fixed-Wing UAVs.

Omkar Babasaheb Rajmane

2021 - 2023 batch.

"Obstacle detection and avoidance for a fixed wing unmanned aerial vehicle using machine learning."

M. Shiva Teja Reddy

"Real Time power-plant fault identification of an electrically powered UAV"

Former Research Fellows

Lab members, ashish kumar.

Project Associate

C. Bhushan Vishwakarma

Jaya suriya k, ven leonin k, rishi pandya r, vishal verma.

Technical Assistant

Receptionist

On-going and completed projects

On-going Projects

Real-time flight vehicles distress management using online system identification.

Agency: Science and Engineering Research Board (SERB)

Reconfigurable control of fixed wing high speed unmanned aerial vehicles

Uav laboratory.

Agency: IIT Kanpur

Payload dropping drones for defence applications.

Agency: Uttar Pradesh Defence Corridor

Completed Projects

Miniature pneumatic recoil management mechanism for micro-uav.

Agency: DRDO Young Scientist Laboratory (DYSL-AT)

Design optimization, prototype development and wind tunnel testing of a foldable Tandem wing UAV

Agency: Asymmetric Technologies, DRDO

Development of Crowd Monitoring Solution Using Aerostat

Agency: Govt. of UP and IITK

Development of Solar and VTOL Air Taxi

Agency: VTOL Aviation India Ltd.

Unmanned Aircraft Vehicles

Agency: Uttar Pradesh Expressways Industrial Development Authority

Flight Demonstration and Flight Data Collection For Latest Ghatak Configuration Irc 2.3 Rd

Agency: Aeronautical Development Agency (ADA)

Design, Development, and testing of Tandem Wing

Design, development, and testing of aerostat, analytical study of a supercavitating projectile including stability analysis in different mediums.

Agency: Navel Science and Technological Laboratory (NSTL), DRDO

Modification of Conventional Artillery Rocket to A Guided Rocket With Freely Spinning Tail

Agency: ARMAMENT RESEARCH BOARD

Autonomous Flight Test of a Low RCS Aircraft Configuration with Ducted Fan for Multiple Flight Modes

Prototype development of cloud seeding equipment.

Agency: IITK and Govt. of UP

Indigenous UAV Developments

Agency: Larsen and Toubro Limited

Initiation Research Grant for System Identification

UAV Laboratory, Department of Aerospace Engineering, IIT Kanpur, IN, 208016

+915122592009

Please feel free to contact me!

Title: Hierarchical Path Planning and Control of a Small Fixed-wing UAV: Theory and Experimental Validation

Associated Organization(s)

Collections, supplementary to, permanent link, date issued, resource type, resource subtype, rights statement.

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FAST-UAV: an open-source framework for optimal drone design with a multidisciplinary approach

SizingLab/FAST-UAV

Folders and files.

Repository files navigation

Future Aircraft Sizing Tool - Unmanned Aerial Vehicles

FAST-UAV is a Python tool dedicated to optimal drone design with a multi-disciplinary approach.

Based on the FAST-OAD and OpenMDAO frameworks, it allows to easily switch between models to address different types of configurations.

Currently, FAST-UAV is bundled with analytical models for multi-rotor, fixed-wing and quad-plane (hybrid VTOL) UAVs.

🚀 Quick start

FAST-UAV requires Python 3.8 or 3.9. It is recommended to install FAST-UAV in a virtual environment ( conda , venv ...):

To install FAST-UAV, run the following commands in a terminal:

Now that FAST-UAV is installed, you can start using it through Jupyter notebooks . To do so, create a new folder for FAST-UAV, cd into this folder, and type this command in your terminal:

Then run the Jupyter server as indicated in the obtained message.

This project is part of Félix Pollet's PhD thesis, which is available here . If you use FAST-UAV as part of your work in a scientific publication, please consider citing the following papers:

🔥 Related publications

M. Budinger, A. Reysset, A. Ochotorena, and S. Delbecq. Scaling laws and similarity models for the preliminary design of multirotor drones. Aerospace Science and Technology, 2020, 98, pp.1-15. https://doi.org/10.1016/j.ast.2019.105658 . https://hal.science/hal-02997598 .

S. Delbecq, M. Budinger, A. Ochotorena, A. Reysset, and F. Defay. Efficient sizing and optimization of multirotor drones based on scaling laws and similarity models. Aerospace Science and Technology, 2020, 102, pp.1-23. https://doi.org/10.1016/j.ast.2020.105873 . https://hal.science/hal-02997596 .

F. Pollet, S. Delbecq, M. Budinger, and J.-M. Moschetta. Design optimization of multirotor drones in cruise. 32nd Congress of the International Council of the Aeronautical Sciences, Sep 2021, Shanghai, China. https://hal.science/hal-03832135/ .

S. Delbecq, M. Budinger, C. Coic, and N. Bartoli. Trajectory and design optimization of multirotor drones with system simulation. AIAA Scitech 2021 Forum, Jan. 2021, VIRTUAL EVENT, United States. https://doi.org/10.2514/6.2021-0211 . https://hal.science/hal-03121520 .

J. Liscouet, F. Pollet, J. Jézégou, M. Budinger, S. Delbecq, and J.-M. Moschetta. A Methodology to Integrate Reliability into the Conceptual Design of Safety-Critical Multirotor Unmanned Aerial Vehicles. Aerospace Science and Technology, 2022, 127, pp.107681. https://doi.org/10.1016/j.ast.2022.107681 . https://hal.science/hal-03956142 .

F. Pollet, S. Delbecq, M. Budinger, J.-M. Moschetta, and J. Liscouët. A Common Framework for the Design Optimization of Fixed-Wing, Multicopter and VTOL UAV Configurations. 33rd Congress of the International Council of the Aeronautical Sciences, Sep. 2022, Stockholm, Sweden. https://hal.science/hal-03832115/

F. Pollet, M. Budinger, S. Delbecq, J. -M. Moschetta, and J. Liscouët. Quantifying and Mitigating Uncertainties in Design Optimization Including Off-the-Shelf Components: Application to an Electric Multirotor UAV. Aerospace Science and Technology, 2023, pp.108179. https://doi.org/10.1016/j.ast.2023.108179 .

F. Pollet, M. Budinger, S. Delbecq, J. -M. Moschetta, and T. Planès. Environmental Life Cycle Assessments for the Design Exploration of Electric UAVs. Aerospace Europe Conference 2023 – 10th EUCASS – 9th CEAS, Jul. 2023, Lausanne, Switzerland. https://doi.org/10.13009/EUCASS2023-548 . https://hal.science/hal-04229799 .

DroneApp sizing tool

The software is released under The GNU General Public License v3.0 .

🤝 Questions and contributions

Feel free to contact us if you have any question or suggestion, or if you wish to contribute with us on FAST-UAV!

• Scott DELBECQ [email protected]
• Félix POLLET [email protected]
• Marc BUDINGER [email protected]

For developers, please follow the following procedure:

• Fork the GitHub repository of FAST-UAV
• Clone your forked repository onto your local machine with git clone
• cd into your FAST-UAV project and install the required dependencies with Poetry using the poetry install command.
• Start making changes to the forked repository
• Open a pull request to merge those changes back into the original repository of FAST-UAV.

Contributors 2

• Jupyter Notebook 97.6%
• Python 2.4%