scholarly journals Video Surveillance Processing Algorithms utilizing Artificial Intelligent (AI) for Unmanned Autonomous Vehicles (UAVs)

MethodsX ◽  
2021 ◽  
pp. 101472
Author(s):  
Minh T. Nguyen ◽  
Linh H. Truong ◽  
Trang T.H. Le
Keyword(s):  
Video Surveillance ◽  
Autonomous Vehicles ◽  
Artificial Intelligent ◽  
Processing Algorithms ◽  
Unmanned Autonomous Vehicles

scholarly journals Collision Avoidance on Unmanned Aerial Vehicles Using Neural Network Pipelines and Flow Clustering Techniques

2021 ◽  
Vol 13 (13) ◽  
pp. 2643
Author(s):  
Dário Pedro ◽  
João P. Matos-Carvalho ◽  
José M. Fonseca ◽  
André Mora
Keyword(s):  
Collision Avoidance ◽  
Autonomous Vehicles ◽  
High Demand ◽  
Novel Approach ◽  
Vision Sensors ◽  
Learning Techniques ◽  
Stationary Objects ◽  
Computationally Intensive ◽  
Unmanned Autonomous Vehicles ◽  
Positive Results

Unmanned Autonomous Vehicles (UAV), while not a recent invention, have recently acquired a prominent position in many industries, and they are increasingly used not only by avid customers, but also in high-demand technical use-cases, and will have a significant societal effect in the coming years. However, the use of UAVs is fraught with significant safety threats, such as collisions with dynamic obstacles (other UAVs, birds, or randomly thrown objects). This research focuses on a safety problem that is often overlooked due to a lack of technology and solutions to address it: collisions with non-stationary objects. A novel approach is described that employs deep learning techniques to solve the computationally intensive problem of real-time collision avoidance with dynamic objects using off-the-shelf commercial vision sensors. The suggested approach’s viability was corroborated by multiple experiments, firstly in simulation, and afterward in a concrete real-world case, that consists of dodging a thrown ball. A novel video dataset was created and made available for this purpose, and transfer learning was also tested, with positive results.

scholarly journals Search-Based Planning and Reinforcement Learning for Autonomous Systems and Robotics

2020 ◽  
Author(s):  
Than Le
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Optimal Trajectory ◽  
Autonomous Systems ◽  
Autonomous Mobile Robots ◽  
Artificial Intelligent ◽  
Unstructured Environment ◽  
And Robotics ◽  
Over Time ◽  
Basic Prerequisite

<p>In this chapter, we address the competent Autonomous Vehicles should have the ability to analyze the structure and unstructured environments and then to localize itself relative to surrounding things, where GPS, RFID or other similar means cannot give enough information about the location. Reliable SLAM is the most basic prerequisite for any further artificial intelligent tasks of an autonomous mobile robots. The goal of this paper is to simulate a SLAM process on the advanced software development. The model represents the system itself, whereas the simulation represents the operation of the system over time. And the software architecture will help us to focus our work to realize our wish with least trivial work. It is an open-source meta-operating system, which provides us tremendous tools for robotics related problems.</p> <p>Specifically, we address the advanced vehicles should have the ability to analyze the structured and unstructured environment based on solving the search-based planning and then we move to discuss interested in reinforcement learning-based model to optimal trajectory in order to apply to autonomous systems.</p>

The Exploration of Autonomous Vehicles

2022 ◽  
pp. 930-944
Author(s):  
Anthony J. Gephardt ◽  
Elizabeth Baoying Wang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Intelligent Agents ◽  
Autonomous Vehicles ◽  
Learning Processes ◽  
Artificial Intelligent ◽  
The World ◽  
History Of ◽  
The Creation

This chapter explores the world of autonomous vehicles. Starting from the beginning, it covers the history of the automobile dating back to 1769. It explains how the first production automobile came about in 1885. The chapter dives into the history of auto safety, ranging from seatbelts to full-on autonomous features. One of the main focuses is the creation and implementation of artificial intelligent (AI), neural networks, intelligent agents, and deep Learning Processes. Combining the hardware on the vehicle with the intelligence of AI creates what we know as autonomous vehicles today.

Recent advances in remote sensing technologies for hydrocarbon exploration and environmental evaluation

2019 ◽  
Vol 38 (7) ◽  
pp. 554-555
Author(s):  
Yongyi Li ◽  
Roice Nelson ◽  
William Jeffery ◽  
Douglas Foster ◽  
Dominique Dubucq ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Environmental Sustainability ◽  
Autonomous Vehicles ◽  
Petroleum Industry ◽  
Hydrocarbon Exploration ◽  
Active Sensors ◽  
Environmental Evaluation ◽  
Satellite Systems ◽  
Unmanned Autonomous Vehicles ◽  
Surface Geology

Remote sensing detects and monitors the physical and spatial characteristics of the earth's oceans, surface, and atmosphere by measuring the reflected or scattered downwelling or emitted upwelling electromagnetic radiation or acoustic signal using passive or active sensors at a distance. It plays an important role in today's energy and environmental sustainability efforts. Remote sensing from spaceborne, airborne, terrestrial, and marine platforms has long been used in hydrocarbon exploration to map surface geology, topography, and hydrocarbon seepages, as well as to evaluate environments that relate to petroleum industry activities. Since the mid-2000s, remote sensing technologies have undergone substantial advances in data acquisition, processing, and interpretation. In the last decade, rapid advances in satellite systems, unmanned autonomous vehicles (UAVs), sensors, and scale of surveys have further expanded applications.

scholarly journals Emerging Inter-Swarm Collaboration for Surveillance Using Pheromones and Evolutionary Techniques

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2566
Author(s):  
Daniel H. Stolfi ◽  
Matthias R. Brust ◽  
Grégoire Danoy ◽  
Pascal Bouvry
Keyword(s):  
Dynamic Systems ◽  
Autonomous Vehicles ◽  
Water Surface ◽  
Mobility Model ◽  
Collaborative Approach ◽  
Ground Vehicles ◽  
Area Of Interest ◽  
Covered Area ◽  
Different Types ◽  
Unmanned Autonomous Vehicles

In this article, we propose a new mobility model, called Attractor Based Inter-Swarm collaborationS (ABISS), for improving the surveillance of restricted areas performed by unmanned autonomous vehicles. This approach uses different types of vehicles which explore an area of interest following unpredictable trajectories based on chaotic solutions of dynamic systems. Collaborations between vehicles are meant to cover some regions of the area which are unreachable by members of one swarm, e.g., unmanned ground vehicles on water surface, by using members of another swarm, e.g., unmanned aerial vehicles. Experimental results demonstrate that collaboration is not only possible but also emerges as part of the configurations calculated by a specially designed and parameterised evolutionary algorithm. Experiments were conducted on 12 different case studies including 30 scenarios each, observing an improvement in the total covered area up to 11%, when comparing ABISS with a non-collaborative approach.

Guest Editorial: Communications Challenges and Dynamics for Unmanned Autonomous Vehicles

2012 ◽  
Vol 30 (5) ◽  
pp. 849-851 ◽  
Author(s):  
Gerard Parr ◽  
Steve Hailes ◽  
Jonathan P. How ◽  
Joe McGeehan ◽  
Y. Jay Guo
Keyword(s):  
Autonomous Vehicles ◽  
Guest Editorial ◽  
Unmanned Autonomous Vehicles

scholarly journals Search-Based Planning and Reinforcement Learning for Autonomous Systems and Robotics

2020 ◽  
Author(s):  
Than Le
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Optimal Trajectory ◽  
Autonomous Systems ◽  
Autonomous Mobile Robots ◽  
Artificial Intelligent ◽  
Unstructured Environment ◽  
And Robotics ◽  
Over Time ◽  
Basic Prerequisite

<p>In this chapter, we address the competent Autonomous Vehicles should have the ability to analyze the structure and unstructured environments and then to localize itself relative to surrounding things, where GPS, RFID or other similar means cannot give enough information about the location. Reliable SLAM is the most basic prerequisite for any further artificial intelligent tasks of an autonomous mobile robots. The goal of this paper is to simulate a SLAM process on the advanced software development. The model represents the system itself, whereas the simulation represents the operation of the system over time. And the software architecture will help us to focus our work to realize our wish with least trivial work. It is an open-source meta-operating system, which provides us tremendous tools for robotics related problems.</p> <p>Specifically, we address the advanced vehicles should have the ability to analyze the structured and unstructured environment based on solving the search-based planning and then we move to discuss interested in reinforcement learning-based model to optimal trajectory in order to apply to autonomous systems.</p>

scholarly journals Investigating the Impact of Energy Source Level on the Self-Guided Vehicle System Performances, in the Industry 4.0 Context

2020 ◽  
Vol 12 (20) ◽  
pp. 8541
Author(s):  
Massinissa Graba ◽  
Sousso Kelouwani ◽  
Lotfi Zeghmi ◽  
Ali Amamou ◽  
Kodjo Agbossou ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Industry 4.0 ◽  
True Path ◽  
Long Term Effect ◽  
Industrial Operations ◽  
Single Vehicle ◽  
Energy Consideration ◽  
Source Level ◽  
Unmanned Autonomous Vehicles ◽  
The Impact

Automated industrial vehicles are taking an imposing place by transforming the industrial operations, and contributing to an efficient in-house transportation of goods. They are expected to bring a variety of benefits towards the Industry 4.0 transition. However, Self-Guided Vehicles (SGVs) are battery-powered, unmanned autonomous vehicles. While the operating durability depends on self-path design, planning energy-efficient paths become crucial. Thus, this paper has no concrete contribution but highlights the lack of energy consideration of SGV-system design in literature by presenting a review of energy-constrained global path planning. Then, an experimental investigation explores the long-term effect of battery level on navigation performance of a single vehicle. This experiment was conducted for several hours, a deviation between the global trajectory and the ground-true path executed by the SGV was observed as the battery depleted. The results show that the mean square error (MSE) increases significantly as the battery’s state-of-charge decreases below a certain value.

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