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Unmanned Aerial Devices

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Species level mapping of a seagrass bed using an unmanned aerial vehicle and deep learning technique.

PeerJ
BACKGROUND: Seagrass beds are essential habitats in coastal ecosystems, providing valuable ecosystem services, but are threatened by various climate change and human activities. Seagrass monitoring by remote sensing have been conducted over past deca...

Protecting endangered megafauna through AI analysis of drone images in a low-connectivity setting: a case study from Namibia.

PeerJ
Assessing the numbers and distribution of at-risk megafauna such as the black rhino () is key to effective conservation, yet such data are difficult to obtain. Many current monitoring technologies are invasive to the target animals and expensive. Sat...

Visual attention prediction improves performance of autonomous drone racing agents.

PloS one
Humans race drones faster than neural networks trained for end-to-end autonomous flight. This may be related to the ability of human pilots to select task-relevant visual information effectively. This work investigates whether neural networks capable...

Different stages of disease detection in squash plant based on machine learning.

Journal of biosciences
To increase agriculture production, accurate and fast detection of plant disease is required. Expert advice is needed to detect disease in plants, nutrition deficiencies or any other abnormalities caused by extreme weather conditions. But this proces...

Coverage Path Planning Methods Focusing on Energy Efficient and Cooperative Strategies for Unmanned Aerial Vehicles.

Sensors (Basel, Switzerland)
The coverage path planning (CPP) algorithms aim to cover the total area of interest with minimum overlapping. The goal of the CPP algorithms is to minimize the total covering path and execution time. Significant research has been done in robotics, pa...

Facilitating the Work of Unmanned Aerial Vehicle Operators Using Artificial Intelligence: An Intelligent Filter for Command-and-Control Maps to Reduce Cognitive Workload.

Human factors
OBJECTIVE: Evaluating the ability of a Gibsonian-inspired artificial intelligence (AI) algorithm to reduce the cognitive workloads of military Unmanned Aerial Vehicle (UAV) operators.

IoT Security and Computation Management on a Multi-Robot System for Rescue Operations Based on a Cloud Framework.

Sensors (Basel, Switzerland)
There is a growing body of literature that recognizes the importance of Multi-Robot coordination and Modular Robotics. This work evaluates the secure coordination of an Unmanned Aerial Vehicle (UAV) via a drone simulation in Unity and an Unmanned Gro...

Detecting Human Actions in Drone Images Using YoloV5 and Stochastic Gradient Boosting.

Sensors (Basel, Switzerland)
Human action recognition and detection from unmanned aerial vehicles (UAVs), or drones, has emerged as a popular technical challenge in recent years, since it is related to many use case scenarios from environmental monitoring to search and rescue. I...

Efficient Informative Path Planning via Normalized Utility in Unknown Environments Exploration.

Sensors (Basel, Switzerland)
Exploration is an important aspect of autonomous robotics, whether it is for target searching, rescue missions, or reconnaissance in an unknown environment. In this paper, we propose a solution to efficiently explore the unknown environment by unmann...

PACNav: a collective navigation approach for UAV swarms deprived of communication and external localization.

Bioinspiration & biomimetics
This article proposes Persistence Administered Collective Navigation (PACNav) as an approach for achieving the decentralized collective navigation of unmanned aerial vehicle (UAV) swarms. The technique is based on the flocking and collective navigati...