Predictive Angular Potential Field-based Obstacle Avoidance for Dynamic UAV Flights

Schleich, Daniel; Behnke, Sven

doi:10.1109/iros47612.2022.9981677

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…Precision, calculated using equation (6), is the ratio of correctly predicted positive samples to all the detected samples.…”

Section: Methodsmentioning

confidence: 99%

“…However, when cybersecurity features cannot prevent intrusion by unscrupulous elements, detect UAVs in advance is essential, which highlights the urgent need for a robust UAV detection mechanism to safeguard public safety and privacy. In densely populated low-altitude airspaces, the 'see and avoid' capability for UAVs [6][7][8] becomes imperative to prevent collisions, necessitating effective detection strategies.…”

Section: Introductionmentioning

confidence: 99%

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EA-DINO: Improved method for unmanned aerial vehicle detection in airspace based on DINO

Cai,

Zhang,

2024

Eng. Res. Express

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In recent years, the increase in drone traffic and the potential for unauthorized surveillance has underscored the urgent need for technological advances in drone detection. Despite the rapid advancements in deep learning that have significantly improved object detection tasks, air-to-air unmanned aerial vehicle (UAV) detection continues to pose significant challenges. Challenges such as complex backgrounds, small size of UAVs in captured images, and variations in flight poses and angles pose significant difficulties for traditional deep learning approaches, mainly because of the inherent limitations of conventional convolutional neural network architectures in discriminating fine details against dynamically changing backdrops. To address these challenges, this study introduces EA-DINO, a new deep learning network based on enhanced aggregation (EA) and DINO. The network incorporates a series of improvements over DINO. First, the backbone is replaced with a Swin transformer, and agent attention is integrated. Second, an EA feature pyramid network is added to the network architecture. Experimental evaluations demonstrate that, in the context of air-to-air UAV detection complexities, the EA-DINO model achieves an $mAP_{50}$ of 96.6\% on the Det-Fly dataset, representing an improvement of 8.3\% over the baseline DINO model. This improvement is noteworthy compared with other mainstream models, illustrating the effectiveness of the proposed model in addressing the challenges of air-to-air UAV detection.

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“…Precision, calculated using equation (6), is the ratio of correctly predicted positive samples to all the detected samples.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EA-DINO: Improved method for unmanned aerial vehicle detection in airspace based on DINO

Cai,

Zhang,

2024

Eng. Res. Express

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“…This facilitates situation assessment and approach planning (Gawel et al, 2017). simultaneous localization and mapping (SLAM) and all-around obstacle avoidance (Schleich & Behnke, 2022) as well as waypoint navigation and exploration with minimal effort from the operator using a gamepad controller. The NUC's iGPU runs convolutional neural network inference for semantic segmentation and person detection from point cloud as well as color and thermal imagery (Bultmann et al, 2023(Bultmann et al, , 2021.…”

Section: Robotic Command Vehicle (Roblw)mentioning

confidence: 99%

“…It is based on the DJI Matrice 210 v2 platform and is equipped for onboard environment perception and navigation planning (Schleich & Behnke, 2021) with an Intel NUC8i7BEH computer, allowing continued operation in global navigation satellite system‐denied environments and even during short communication outages. An Ouster OS0‐128 Light Detection and Ranging (LiDAR) enables 3D simultaneous localization and mapping (SLAM) and all‐around obstacle avoidance (Schleich & Behnke, 2022) as well as waypoint navigation and exploration with minimal effort from the operator using a gamepad controller. The NUC's iGPU runs convolutional neural network inference for semantic segmentation and person detection from point cloud as well as color and thermal imagery (Bultmann et al, 2023, 2021).…”

Section: Deployment Setup Of the Drz Rtfmentioning

confidence: 99%

Lessons from robot‐assisted disaster response deployments by the German Rescue Robotics Center task force

Surmann,

Daun,

Schnaubelt

et al. 2023

Journal of Field Robotics

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Earthquakes, fire, and floods often cause structural collapses of buildings. However, the inspection of such damaged buildings poses a high risk for emergency forces or is even impossible. We present three recently selected missions of the Robotics Task Force of the German Rescue Robotics Center (DRZ), where both ground and aerial robots were used to explore destroyed buildings. We describe and reflect the missions as well as the lessons learned that have resulted from them. To make robots from research laboratories fit for real operations, realistic outdoor and indoor test environments were set up at the DRZ and used for tests in regular exercises by researchers and emergency forces. On the basis of this experience, the robots and their control software were significantly improved. Furthermore, expert teams of researchers and first responders were formed, each with realistic assessments of the operational and practical suitability of robotic systems.

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Collision-Free Trajectory Following With Augmented Artificial Potential Field Using UAVs

Goricanec,

Milas,

Markovic

et al. 2023

IEEE Access

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Predictive Angular Potential Field-based Obstacle Avoidance for Dynamic UAV Flights

Cited by 3 publications

References 17 publications

EA-DINO: Improved method for unmanned aerial vehicle detection in airspace based on DINO

EA-DINO: Improved method for unmanned aerial vehicle detection in airspace based on DINO

Lessons from robot‐assisted disaster response deployments by the German Rescue Robotics Center task force

Collision-Free Trajectory Following With Augmented Artificial Potential Field Using UAVs

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