VAGADRONE: Intelligent and Fully Automatic Drone Based on Raspberry Pi and Android

Benhadhria, Saifeddine; Mansouri, Mohamed; Benkhlifa, Ameni; Gharbi, Imed; Jlili, Nadhem

doi:10.3390/app11073153

Cited by 17 publications

(4 citation statements)

References 10 publications

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“…Among hardware, computing resources like Raspberry Pie (RPI), flight controllers like Pixhawk, and micro-controllers like Arduino have effectively reduced the cost of educational UAVs [17]. Recent work has integrated RPI-based drones through android applications via the cloud to perform autonomous image processing capabilities with search and track features [18].…”

Section: Platforms For Uav's Flight Control and Simulationmentioning

confidence: 99%

“…ROS allows simultaneous simulation of flight dynamics, onboard sensors, and flight environments, with no change of codes, while shifting simulations to hardware and ensuring rapid proto-typing [19]. ROS has also been used to simulate multiple UAVs, supports communications among heterogeneous robots [18], and is an ultimate choice.…”

Section: Platforms For Uav's Flight Control and Simulationmentioning

confidence: 99%

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Gas concentration mapping and source localization for environmental monitoring through unmanned aerial systems using model-free reinforcement learning agents

Husnain,

Mokhtar,

Mohamed Shah

et al. 2024

PLoS ONE

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There are three primary objectives of this work; first: to establish a gas concentration map; second: to estimate the point of emission of the gas; and third: to generate a path from any location to the point of emission for UAVs or UGVs. A mountable array of MOX sensors was developed so that the angles and distances among the sensors, alongside sensors data, were utilized to identify the influx of gas plumes. Gas dispersion experiments under indoor conditions were conducted to train machine learning algorithms to collect data at numerous locations and angles. Taguchi’s orthogonal arrays for experiment design were used to identify the gas dispersion locations. For the second objective, the data collected after pre-processing was used to train an off-policy, model-free reinforcement learning agent with a Q-learning policy. After finishing the training from the training data set, Q-learning produces a table called the Q-table. The Q-table contains state-action pairs that generate an autonomous path from any point to the source from the testing dataset. The entire process is carried out in an obstacle-free environment, and the whole scheme is designed to be conducted in three modes: search, track, and localize. The hyperparameter combinations of the RL agent were evaluated through trial-and-error technique and it was found that ε = 0.9, γ = 0.9 and α = 0.9 was the fastest path generating combination that took 1258.88 seconds for training and 6.2 milliseconds for path generation. Out of 31 unseen scenarios, the trained RL agent generated successful paths for all the 31 scenarios, however, the UAV was able to reach successfully on the gas source in 23 scenarios, producing a success rate of 74.19%. The results paved the way for using reinforcement learning techniques to be used as autonomous path generation of unmanned systems alongside the need to explore and improve the accuracy of the reported results as future works.

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Section: Platforms For Uav's Flight Control and Simulationmentioning

confidence: 99%

Section: Platforms For Uav's Flight Control and Simulationmentioning

confidence: 99%

Gas concentration mapping and source localization for environmental monitoring through unmanned aerial systems using model-free reinforcement learning agents

Husnain,

Mokhtar,

Mohamed Shah

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

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“…There are many options when selecting the onboard computers, flight controllers, and sensors for the drone [45,46]. Most popular flight controllers include the Pixhawk series of flight controllers [47][48][49], Navio [50], and iNAV [51], to name a few. The onboard computers include the Raspberry Pi series [52] and the NVIDIA Jetson series [41] among the most popular.…”

Section: Types Of Onboard Computermentioning

confidence: 99%

Drone High-Rise Aerial Delivery with Vertical Grid Screening

Seth

James

Kuantama

et al. 2023

Drones

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Delivery drones typically perform delivery by suspending the parcel vertically or landing the drone to drop off the package. However, because of the constrained landing area and the requirement for precise navigation, delivering items to customers who reside in multi-story apartment complexes poses a unique challenge. This research paper proposes a novel drone delivery system for multi-story apartment buildings with balconies that employ two methods for Vertical Grid Screening (VGS), i.e., Grid Screening (GS) and Square Screening (SS), to detect unique markers to identify the precise balcony that needs to receive the product. The developed drone has a frame size of 295 mm and is equipped with a stereo camera and a ranging sensor. The research paper also explores the scanning and trajectory methods required for autonomous flight to accurately approach the marker location. The proposed machine learning system is trained on a YOLOv5 model for image recognition of the marker, and four different models and batch sizes are compared. The 32-batch size with a 960 × 1280 resolution model provides an average of 0.97 confidence for an extended range. This system is tested outdoors and shows an accuracy of 95% for a planned trajectory with 398 ms detection time as a solution for last-mile delivery in urban areas.

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“…Systems of various types of Unmanned Aerial Vehicles (UAV), including fixed wings [1][2][3], multirotor [4][5][6] and other hybrid type aircraft [7,8] are increasingly being used in both military and civilian applications. An increasing number of flying platforms, greater availability of entire UAV systems and new types of missions, make issues related to the full automation of the entire flight, including its terminal phases, more and more important.…”

Section: Introductionmentioning

confidence: 99%

Fixed Wing Aircraft Automatic Landing with the Use of a Dedicated Ground Sign System

et al. 2021

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The paper presents automatic control of an aircraft in the longitudinal channel during automatic landing. There are two crucial components of the system presented in the paper: a vision system and an automatic landing system. The vision system processes pictures of dedicated on-ground signs which appear to an on-board video camera to determine a glide path. Image processing algorithms used by the system were implemented into an embedded system and tested under laboratory conditions according to the hardware-in-the-loop method. An output from the vision system was used as one of the input signals to an automatic landing system. The major components are control algorithms based on the fuzzy logic expert system. They were created to imitate pilot actions while landing the aircraft. Both systems were connected with one another for cooperation and to control an aircraft model in a simulation environment. Selected results of tests presenting control efficiency and precision are shown in the final section of the paper.

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VAGADRONE: Intelligent and Fully Automatic Drone Based on Raspberry Pi and Android

Cited by 17 publications

References 10 publications

Gas concentration mapping and source localization for environmental monitoring through unmanned aerial systems using model-free reinforcement learning agents

Gas concentration mapping and source localization for environmental monitoring through unmanned aerial systems using model-free reinforcement learning agents

Drone High-Rise Aerial Delivery with Vertical Grid Screening

Fixed Wing Aircraft Automatic Landing with the Use of a Dedicated Ground Sign System

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