Unmanned aerial vehicle swarm distributed cooperation method based on situation awareness consensus and its information processing mechanism

2020 ◽  
Vol 188 ◽  
pp. 105034
Author(s):  
Yang Gao ◽  
Dongsheng Li
Keyword(s):  
Information Processing ◽  
Unmanned Aerial Vehicle ◽  
Situation Awareness ◽  
Aerial Vehicle ◽  
Distributed Cooperation ◽  
Processing Mechanism

Synthetic vision system for improving unmanned aerial vehicle operator situation awareness

2005 ◽  
Author(s):  
Gloria L. Calhoun ◽  
Mark H. Draper ◽  
Michael F. Abernathy ◽  
Michael Patzek ◽  
Francisco Delgado
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Situation Awareness ◽  
Vision System ◽  
Synthetic Vision ◽  
Aerial Vehicle

The Role of Affective Computing for Improving Situation Awareness in Unmanned Aerial Vehicle Operations

2019 ◽  
pp. 295-305
Author(s):  
Jonathan Bishop
Keyword(s):  
Missing Data ◽  
Cognitive Load ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Situation Awareness ◽  
Affective Computing ◽  
Affect Recognition ◽  
Aerial Vehicles ◽  
Aerial Vehicle

Unmanned aerial vehicles (UAVs), commonly known as drones, are a robotic form of military aircraft that are remotely operated by humans. Due to lack of situation awareness, such technology has led to the deaths of civilians through the inaccurate targeting of missile or gun attacks. This chapter presents the case for how a patented invention can be used to reduce civilian casualties through attaching an affect recognition sensor to a UAV that uses a database of strategies, tactics and commands to better instruct fighter pilots on how to respond while in combat so as to avoid misinterpreting civilians as combatants. The chapter discusses how this system, called VoisJet, can reduce many of the difficulties that come about for UAV pilots, including reducing cognitive load and opportunity for missing data. The chapter concludes that using UAVs fitted with VoisJet could allow for the reduction of the size of standing armies so that defence budgets are not overstretched outside of peacetime.

The Role of Affective Computing for Improving Situation Awareness in Unmanned Aerial Vehicle Operations

2015 ◽  
pp. 404-414
Author(s):  
Jonathan Bishop
Keyword(s):  
Missing Data ◽  
Cognitive Load ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Situation Awareness ◽  
Affective Computing ◽  
Affect Recognition ◽  
Aerial Vehicles ◽  
Aerial Vehicle

Unmanned aerial vehicles (UAVs), commonly known as drones, are a robotic form of military aircraft that are remotely operated by humans. Due to lack of situation awareness, such technology has led to the deaths of civilians through the inaccurate targeting of missile or gun attacks. This chapter presents the case for how a patented invention can be used to reduce civilian casualties through attaching an affect recognition sensor to a UAV that uses a database of strategies, tactics and commands to better instruct fighter pilots on how to respond while in combat so as to avoid misinterpreting civilians as combatants. The chapter discusses how this system, called VoisJet, can reduce many of the difficulties that come about for UAV pilots, including reducing cognitive load and opportunity for missing data. The chapter concludes that using UAVs fitted with VoisJet could allow for the reduction of the size of standing armies so that defence budgets are not overstretched outside of peacetime.

Shipping Container Counting Approach Using Unmanned Aerial Vehicle (UAV) and ArcGIS

2019 ◽  
Vol 16 (2) ◽  
pp. 15
Author(s):  
Afiq Abdullah ◽  
Jasmee Jaafar ◽  
Khairul Nizam Tahar ◽  
Mohamad Hezri Razali
Keyword(s):  
Information Processing ◽  
Unmanned Aerial Vehicle ◽  
Digital Terrain Model ◽  
Aerial Images ◽  
Geographical Information ◽  
Surface Model ◽  
Standard Size ◽  
Shipping Container ◽  
Counting Approach ◽  
Aerial Vehicle

In Malaysia, the existing of counting approach on the shipping container at depot is carried out by manual based system. This has made the efficiency of the method to be questioned which can be solved through automation. Under previous studies, Unmanned Aerial Vehicle (UAV) is demonstrated for automatic counting of cars and trees. Therefore, the possibility for shipping container counting is highly required in which promotes low-cost alternative and automatedpilot for data collection. Based on this study, the aerial images captured using UAV is combined with geographical information processing software, ArcGIS, towards automated approach for container counting. The overlapping aerial images are post-processed using photogrammetric technique to produce Digital Surface Model (DSM) that represents the ground and above surface feature’s elevations. Then, the constructed DSM is filtered to develop Digital Terrain Model (DTM) where it represents the ground surface’s elevation only. Then, container’s candidates are isolated using subtraction of the DTM from DSM to generate normalized DSM (nDSM) which represents the heights of container’s stacks. From the standard size and height of one container from ISO, the number of containers is extracted. The ModelBuilder tool available in ArcGIS is customized for automated geographical information processing. From results, the proposed approach contributed to 100% of counting accuracy. Keywords: Unmanned Aerial Vehicle, Counting, Shipping Container, ArcGIS, ModelBuilder

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