scholarly journals Learning control of unmanned aerial vehicles using artificial intelligence-based methods

2020 ◽  
Author(s):  
◽  
Andriy Sarabakha
Keyword(s):  
Artificial Intelligence ◽  
Unmanned Aerial Vehicles ◽  
Learning Control ◽  
Aerial Vehicles

A Two-Step Optimization-Based Iterative Learning Control for Quadrotor Unmanned Aerial Vehicles

2021 ◽  
Vol 143 (7) ◽  
Author(s):  
Revant Adlakha ◽  
Minghui Zheng
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Design Problem ◽  
Iterative Learning Control ◽  
Design Method ◽  
Learning Control ◽  
Iterative Learning ◽  
Learning Performance ◽  
Aerial Vehicles ◽  
Quadrotor Unmanned Aerial Vehicles ◽  
Optimization Based Design

Abstract This paper presents a two-step optimization-based design method for iterative learning control and applies it onto the quadrotor unmanned aerial vehicles (UAVs) trajectory tracking problem. Iterative learning control aims to improve the tracking performance through learning from errors over iterations in repetitively operated systems. The tracking errors from previous iterations are injected into a learning filter and a robust filter to generate the learning signal. The design of the two filters usually involves nontrivial tuning work. This paper presents a new two-optimization design method for the iterative learning control, which is easy to obtain and implement. In particular, the learning filter design problem is transferred into a feedback controller design problem for a purposely constructed system, which is solved based on H-infinity optimal control theory thereafter. The robust filter is then obtained by solving an additional optimization to guarantee the learning convergence. Through the proposed design method, the learning performance is optimized and the system's stability is guaranteed. The proposed two-step optimization-based design method and the regarding iterative learning control algorithm are validated by both numerical and experimental studies.

scholarly journals Evolving Rule-Based Explainable Artificial Intelligence for Unmanned Aerial Vehicles

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 17001-17016 ◽  
Author(s):  
Blen M. Keneni ◽  
Devinder Kaur ◽  
Ali Al Bataineh ◽  
Vijaya K. Devabhaktuni ◽  
Ahmad Y. Javaid ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Unmanned Aerial Vehicles ◽  
Rule Based ◽  
Aerial Vehicles ◽  
Explainable Artificial Intelligence

scholarly journals Secure UAV-Based System to Detect Small Boats Using Neural Networks

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Moisés Lodeiro-Santiago ◽  
Pino Caballero-Gil ◽  
Ricardo Aguasca-Colomo ◽  
Cándido Caballero-Gil
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Unmanned Aerial Vehicles ◽  
High Sensitivity ◽  
Search And Rescue ◽  
Detection Systems ◽  
Aerial Vehicles ◽  
Current Detection

This work presents a system to detect small boats (pateras) to help tackle the problem of this type of perilous immigration. The proposal makes extensive use of emerging technologies like Unmanned Aerial Vehicles (UAV) combined with a top-performing algorithm from the field of artificial intelligence known as Deep Learning through Convolutional Neural Networks. The use of this algorithm improves current detection systems based on image processing through the application of filters thanks to the fact that the network learns to distinguish the aforementioned objects through patterns without depending on where they are located. The main result of the proposal has been a classifier that works in real time, allowing the detection of pateras and people (who may need to be rescued), kilometres away from the coast. This could be very useful for Search and Rescue teams in order to plan a rescue before an emergency occurs. Given the high sensitivity of the managed information, the proposed system includes cryptographic protocols to protect the security of communications.

scholarly journals Special Issue on Unmanned Aerial Vehicles (UAVs)

2020 ◽  
Vol 10 (22) ◽  
pp. 8078
Author(s):  
Sunghun Jung
Keyword(s):  
Applied Sciences ◽  
Artificial Intelligence ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Collision Avoidance ◽  
Acceptance Rate ◽  
Special Issue ◽  
Aerial Vehicles ◽  
Machine Interface ◽  
Communication Control

This editorial paper was a special issue of Applied Sciences belonging to the section of mechanical engineering in MDPI journal and summarized the collected manuscripts regarding the unmanned aerial vehicles (UAVs) related technologies, including communication, control, collision avoidance, modeling, path planning, human-machine interface (HMI), artificial intelligence (AI), etc. Chronologically, this special issue was started to be coordinated at the end of Oct 2018, prepared for a month and opened to collect manuscripts from the middle of Nov 2018 until the end of Dec 2019. During almost a year, 26 papers were published online out of 50 submitted papers which results in 52% acceptance rate.

scholarly journals Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation

Sensors ◽  
2016 ◽  
Vol 16 (1) ◽  
pp. 97 ◽  
Author(s):  
Luis Gonzalez ◽  
Glen Montes ◽  
Eduard Puig ◽  
Sandra Johnson ◽  
Kerrie Mengersen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Unmanned Aerial Vehicles ◽  
Wildlife Monitoring ◽  
Aerial Vehicles

Artificial Intelligence Methods in Aviation Specialist Training for the Analysis and Transmission of Operational Meteorological Information

2020 ◽  
pp. 306-322
Author(s):  
Sergiy I. Rudas ◽  
Evgeniya A. Znakovska ◽  
Dmitriy I. Bondarev
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Unmanned Aerial Vehicles ◽  
Information Technologies ◽  
Meteorological Data ◽  
Specialist Training ◽  
Air Traffic Controllers ◽  
Aerial Vehicles ◽  
Artificial Intelligence Methods ◽  
Learning Machine

The authors present methods for the application of artificial intelligence for operational meteorological information (OPEC). The means of communication for distribution of meteorological data using information technologies are presented. Practical courses for aviation specialists (pilots, air traffic controllers, operators of unmanned aerial vehicles) are considered in which artificial intelligence methods are used: datamining, deep learning, machine learning, using information technologies.

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