Control of a Drone in Virtual Reality Using MEMS Sensor Technology and Machine Learning

Covaciu, Florin; Iordan, Anca

doi:10.3390/mi13040521

Cited by 17 publications

(9 citation statements)

References 39 publications

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“…Ref. [40] found that an intelligent flight assistant based on the simplest neural network architecture has the capacity to outperform human flight performance. Refs.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Intelligent Position Controller for Unmanned Aerial Vehicles (UAV) Based on Supervised Deep Learning

et al. 2023

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In recent years, multi-rotor UAVs have become valuable tools in several productive fields, from entertainment to agriculture and security. However, during their flight trajectory, they sometimes do not accurately perform a specific set of tasks, and the implementation of flight controllers in these vehicles is required to achieve a successful performance. Therefore, this research describes the design of a flight position controller based on Deep Neural Networks and subsequent implementation for a multi-rotor UAV. Five promising Neural Network architectures are developed based on a thorough literature review, incorporating LSTM, 1-D convolutional, pooling, and fully-connected layers. A dataset is then constructed using the performance data of a PID flight controller, encompassing diverse trajectories with transient and steady-state information such as position, speed, acceleration, and motor output signals. The tuning of hyperparameters for each type of architecture is performed by applying the Hyperband algorithm. The best model obtained (LSTMCNN) consists of a combination of LSTM and CNN layers in one dimension. This architecture is compared with the PID flight controller in different scenarios employing evaluation metrics such as rise time, overshoot, steady-state error, and control effort. The findings reveal that our best models demonstrate the successful generalization of flight control tasks. While our best model is able to work with a wider operational range than the PID controller and offers step responses in the Y and X axis with 97% and 98% similarity, respectively, within the PID’s operational range. This outcome opens up possibilities for efficient online training of flight controllers based on Neural Networks, enabling the development of adaptable controllers tailored to specific application domains.

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“…Ref. [40] found that an intelligent flight assistant based on the simplest neural network architecture has the capacity to outperform human flight performance. Refs.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Intelligent Position Controller for Unmanned Aerial Vehicles (UAV) Based on Supervised Deep Learning

et al. 2023

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“…A method for controlling a drone in virtual reality using machine learning and MEMS sensor technology is proposed in [25]. In order to predict the drone's motion, the authors use an IMU (Inertial Measurement Unit) to capture the drone's motion in three-dimensional space.…”

Section: Machine Learning Techniques In Drone Controlmentioning

confidence: 99%

“…The authors conclude that the memory-based strategy is a promising approach for UAVs with limited environmental knowledge to avoid obstacles. System can detect individuals and their distances in real-time to ensure social distance rules are followed [23] Analyzing drone data using machine learning methods Three stages: data acquisition, data processing, and data analysis Proposed framework and machine learning methods provide an efficient and effective method for continuing the investigation into drone-related incidents [24] Evaluating reinforcement learning and deep learning algorithms for controlling UAVs in various tasks Simulated environments and various metrics Deep learning models generally have higher accuracy and generalization, but each algorithm performs differently depending on the task and environment [25] Controlling a drone in virtual reality using machine learning and MEMS sensor technology IMU to capture drone motion in 3D space Machine learning algorithm accurately predicts drone motion, could be utilized for virtual reality drone control and training/simulation [26] Using RF signals to find and identify drones…”

Section: Machine Learning Techniques In Drone Controlmentioning

confidence: 99%

A Comprehensive Review of Classification and Application of Machine Learning in Drone Technology

Haque¹,

Chowdhury²,

Hassanalian³

2023

Preprint

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The use of drones for various applications has become increasingly popular in recent years, and machine learning has played a significant role in this trend. In this paper, we provide a comprehensive survey of the classification and application of machine learning in drones. The paper begins with an overview of the different types of machine learning algorithms and their applications in drones, including supervised learning, unsupervised learning, and reinforcement learning. Next, we present a detailed analysis of various real-world applications of machine learning in drones, such as object recognition, route planning, obstacle avoidance, search area optimization, and autonomous search. The paper also discusses the challenges and limitations of using machine learning in drones, such as data privacy, data quality, and computational requirements. Finally, the paper concludes with a discussion of the future directions of machine learning in drones and its potential impact on various industries and fields. This paper provides a valuable resource for researchers, practitioners, and students interested in the intersection of machine learning and drones.

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“…Accelerometers based on micro-electro-mechanical system (MEMS) are extensively applied in inertial integrated navigation systems, mobile vehicles and smart robotics [1][2][3]. They have also been used for in vivo monitoring in biomedical field, image stabilization device in portable camera, and virtual reality (VR) technology [4][5][6]. Based on their working principles, MEMS accelerometers can be classified into several categories, such as piezoresistive ones, capacitive ones, and tunnel effect ones [7].…”

Section: Introductionmentioning

confidence: 99%

Versatile and effective design platform for surface acoustic wave accelerometers

et al. 2023

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Micro-Electro-Mechanical Systems (MEMS) accelerometers have great potentials for applications in aerospace, autonomous driving and consumer electronics. However, most of their working principles are based on capacitive and resistance types, which cannot be easily used for wireless and passive sensing, while surface acoustic waves (SAWs) are the key solution for this problem. Due to complex acoustic-electric-mechanical coupling during accelerator operations, currently, there needs an accurate, reliable, and efficient design and simulation platform to improve the structure and performance of SAW based accelerometers. In this work, we proposed an accurate, reliable, and efficient modeling platform to optimize designs of SAW accelerometers, using a double-ended cantilever beam structure as an example. This model integrated elastic acoustic effect and the coupled wave equations under both the mechanical and electrical loading using the finite element analysis, and obtained acceleration-frequency responses of the accelerators. We have systematically simulated effects of thickness of piezoelectric film, wavelengths, and structural parameters of cantilever beams, and the simulation results are well consistent with the theoretical results. Finally, using the developed model, we designed a high-G SAW accelerometer (up to 20000 g), which achieved a high sensitivity (-41.8 Hz/g) and excellent linearity (0.9999), and another high sensitivity accelerometer (3.02 KHz/g), with a good linearity (0.9999) over a 100 g acceleration range.

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Control of a Drone in Virtual Reality Using MEMS Sensor Technology and Machine Learning

Cited by 17 publications

References 39 publications

Intelligent Position Controller for Unmanned Aerial Vehicles (UAV) Based on Supervised Deep Learning

Intelligent Position Controller for Unmanned Aerial Vehicles (UAV) Based on Supervised Deep Learning

A Comprehensive Review of Classification and Application of Machine Learning in Drone Technology

Versatile and effective design platform for surface acoustic wave accelerometers

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