Autonomous Precision Landing for the Joint Tactical Aerial Resupply Vehicle

Recker, Shawn; Gribble, Christiaan; Butkiewicz, Mark

doi:10.1109/aipr.2018.8707418

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…Recker et al [31] proposed a precision landing method based on target identification and position estimation in which the object detection neural network provides the bounding box to the photogrammetry target tracking algorithm to find the target (bar code) center. Chen et al [12] proposed a method to track objects (human in an experiment) from a vertical-facing camera using an object detection algorithm.…”

Section: Comparison Of Proposed Methods To State-of-the-art Methodsmentioning

confidence: 99%

“…We can implement this method on new and updated boards like Nvidia Jetson TX and Xavier series for higher processing capacity. The target tracking by the embedded unit in our approach process frames faster compared to the method proposed by [12,31] (TX2 platform) by 60 and 168% in terms of tracking efficiency. The applications and methods provided in the referred articles are different, but we make the comparison as far as tracking efficiency and processing speed are concerned.…”

Section: Angle (θ Pn )mentioning

confidence: 97%

“…Computer vision-based drone navigation facilitates precision landing. It can be Deep Neural Network (DNN)-and marker-based [30] or pattern-based (12.83 and 25.41 fps (frame per second) for 720 pixel resolution) [31]. Truong et al [32] proposed super-resolution image reconstruction using deep learning to enhance low resolution and blurred images.…”

Section: Introductionmentioning

confidence: 99%

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Multiple Drone Navigation and Formation Using Selective Target Tracking-Based Computer Vision

2021

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Autonomous unmanned aerial vehicles work seamlessly within the GPS signal range, but their performance deteriorates in GPS-denied regions. This paper presents a unique collaborative computer vision-based approach for target tracking as per the image’s specific location of interest. The proposed method tracks any object without considering its properties like shape, color, size, or pattern. It is required to keep the target visible and line of sight during the tracking. The method gives freedom of selection to a user to track any target from the image and form a formation around it. We calculate the parameters like distance and angle from the image center to the object for the individual drones. Among all the drones, the one with a significant GPS signal strength or nearer to the target is chosen as the master drone to calculate the relative angle and distance between an object and other drones considering approximate Geo-location. Compared to actual measurements, the results of tests done on a quadrotor UAV frame achieve 99% location accuracy in a robust environment inside the exact GPS longitude and latitude block as GPS-only navigation methods. The individual drones communicate to the ground station through a telemetry link. The master drone calculates the parameters using data collected at ground stations. Various formation flying methods help escort other drones to meet the desired objective with a single high-resolution first-person view (FPV) camera. The proposed method is tested for Airborne Object Target Tracking (AOT) aerial vehicle model and achieves higher tracking accuracy.

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Section: Comparison Of Proposed Methods To State-of-the-art Methodsmentioning

confidence: 99%

Section: Angle (θ Pn )mentioning

confidence: 97%