ATS: Aerial Tracking and Surveillance System

Kota, S.; Jamshidi, Mo

doi:10.1109/sysose.2008.4724193

Cited by 4 publications

(2 citation statements)

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“…Recently, unmanned aerial vehicles (UAVs) have emerged as a promising solution for situations where human intervention can be dangerous, unsafe, or even impossible, for example, disaster management 1-3 . First, UAVs can fly or hover and reach a disaster zone within a very short time compared with traditional rescue approaches.…”

Section: Introductionmentioning

confidence: 99%

On seamless and high‐bandwidth connectivity for cognitive multi‐unmanned aerial vehicle‐assisted networks

Hasan

Ejaz

Zghaibeh

et al. 2020

Trans Emerging Tel Tech

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Unmanned aerial vehicles (UAVs) can be useful in many different scenarios including disaster management. UAVs can immediately reach the disaster area and collect data that can help relief and rescue activities. Nevertheless, these advantages can be further improved when multiple UAVs coordinate for data collection from different vantage points simultaneously. However, seamless, high‐bandwidth communication between UAVs is required for this coordination. Nonetheless, due to the scarcity of bandwidth, a UAV network operating on an unlicensed ISM band may not be feasible. In this article, we follow a conceptual approach in which UAVs can access the licensed spectrum opportunistically. However, it is highly likely that various UAVs may have different sets of approved channels available and are unable to coordinate. Therefore, UAVs are divided into clusters to establish coordination in this scenario based on the availability of a common channel between different UAVs. Clustering based on a single common channel may still not be practical, due to the sporadic availability of common channels that often causes reclustering. Therefore, we present a multi‐UAV clustering scheme in which clusters of UAVs are established with more common channels to prevent repeated reclustering by having one main channel and the rest as backup channels. However, this may reduce the size of the cluster which can be addressed by limiting the number of backup channels. In this article, we present two variants of the proposed scheme: Multi‐UAV clustering‐I and multi‐UAV clustering‐II. The multi‐UAV clustering‐I scheme attempts to cluster UAVs with as many common channels as possible, whereas the multi‐UAV clustering‐II scheme attempts to limit the number of common channels to two. In addition, we propose a method for choosing the main channel from the different channels, where these channels are rated on the basis of bandwidth capacity and the channel with the highest rank is selected as the main channel. The remaining and the rest of the channels are kept as a backup. Simulation results demonstrate smooth and high‐bandwidth communication among UAVs.

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Section: Introductionmentioning

confidence: 99%

On seamless and high‐bandwidth connectivity for cognitive multi‐unmanned aerial vehicle‐assisted networks

Hasan

Ejaz

Zghaibeh

et al. 2020

Trans Emerging Tel Tech

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“…2 Furthermore, the quadrotor is capable of holonomic motion which is advantageous when traversing through cluttered or narrow environments. This is beneficial in many applications such as search and rescue, 3 surveillance, 4,5 and exploration in complex environments like underground mines and caverns, heavily wooded areas, warehouses, and indoor complexes such as malls and office buildings.…”

Section: Introductionmentioning

confidence: 99%

Kinodynamic Motion Planning for Holonomic UAVs in Complex 3D Environments

Chen

Waslander

2010

AIAA Guidance, Navigation, and Control Conference

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Due to their ability to hover, rotorcraft can be used in many cluttered and narrow environments that are unsuitable for other unmanned aerial vehicles (UAVs). However in such environments, it is usually difficult to find paths that are both collision-free and dynamically feasible. This paper introduces a computationally efficient algorithm for finding safe paths, through known static three dimensional environments, that satisfy the kinodynamic constraints of a quadrotor. First, a collision-free path that ignores kinodynamic constraints is found using probabilistic roadmaps (PRM). Next, a heuristic re-sampling algorithm is used to make improvements to this path. Finally, the path is separated into a series of motion primitives that satisfy kinodynamic constraints. To ensure corner path segments remain collision-free, a method of bounding the vehicle deviation from the piece-wise linear path at each corner is introduced. Finally, the control inputs required to traverse the transformed path are calculated using kinematics and become the feed-forward inputs for a position controller. In simulation, the algorithm is able to successfully compute safe kinodynamic paths through cluttered and narrow environments, while using limited computational resources.

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