Interception of Non-Cooperative UAVs

Moreira, Marina Figueiredo; Papp, Endre; Ventura, Rodrigo

doi:10.1109/ssrr.2019.8848952

Cited by 6 publications

(6 citation statements)

References 17 publications

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“…To the best of our knowledge, all current interception approaches require the interceptor to be faster than the intruder (Moreira et al, 2019), some even up to two times faster than the target (Yang and Quan, 2020), which will be increasingly difficult to achieve, especially since some researchers in the field estimate that within 5 years there could be UAVs with a top speed of 100 m/s (Bond et al, 2019). In this work, inspired by the challenging target speeds of MBZIRC2020 and constrained by limitations of our hardware, we explore the possibility of intercepting a faster and more agile target with a slower UAV.…”

Section: Contributions and Related Workmentioning

confidence: 99%

“…Intercepting a runaway UAV is a well-researched topic in the aerial robotics community (Beard et al, 2002), (Moreira et al, 2019), (Hehn and D'Andrea, 2012), but Challenge 1 presents more structure than a simple "runaway UAV" scenario. This simplification arises from the fact that the target's trajectory shape is known in advance, while other parameters, such as size, location, and, more importantly, orientation, are not.…”

Section: Introductionmentioning

confidence: 99%

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Brain over Brawn: Using a Stereo Camera to Detect, Track, and Intercept a Faster UAV by Reconstructing the Intruder’s Trajectory

Barisic¹,

Petric²,

Bogdan³

2022

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This paper presents our approach to intercepting a faster intruder UAV, inspired by the MBZIRC 2020 Challenge 1. By utilizing a priori knowledge of the shape of the intruder’s trajectory, we can calculate an interception point. Target tracking is based on image processing by a YOLOv3 Tiny convolutional neural network, combined with depth calculation using a gimbal-mounted ZED Mini stereo camera. We use RGB and depth data from the camera, devising a noise-reducing histogram-filter to extract the target’s 3D position. Obtained 3D measurements of target’s position are used to calculate the position, orientation, and size of a figure-eight shaped trajectory, which we approximate using a Bernoulli lemniscate. Once the approximation is deemed sufficiently precise, as measured by the distance between observations and estimate, we calculate an interception point to position the interceptor UAV directly on the intruder’s path. Our method, which we have significantly improved based on the experience gathered during the MBZIRC competition, has been validated in simulation and through field experiments. Our results confirm that we have developed an efficient, visual-perception module that can extract information describing the intruder UAV’s motion with precision sufficient to support interception planning. In a majority of our simulated encounters, we can track and intercept a target that moves 30% faster than the interceptor. Corresponding tests in an unstructured environment yielded 9 out of 12 successful results.

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Section: Contributions and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Brain over Brawn: Using a Stereo Camera to Detect, Track, and Intercept a Faster UAV by Reconstructing the Intruder’s Trajectory

Barisic¹,

Petric²,

Bogdan³

2022

View full text Add to dashboard Cite

show abstract

“…In contrast, academic publications on UAS-based C-UAS solutions, detailed enough for maturity assessments, are-according to our findings-almost non-existent. An exception is reference [64], which proposed a navigation, guidance and control architecture to generate interception paths. This component was validated on a real drone intercepting virtual target drones, thus reaching ARL 2.…”

Section: Counter Uav Systems (C-uas)mentioning

confidence: 99%

Maturity Levels of Public Safety Applications using Unmanned Aerial Systems: a Review

Stampa

Sutorma

Jahn

et al. 2021

J Intell Robot Syst

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Unmanned Aerial Systems (UAS) are becoming increasingly popular in the public safety sector. While some applications have so far only been envisioned, others are regularly performed in real-life scenarios. Many more fall in between and are actively investigated by research and commercial communities alike. This study reviews the maturity levels, or “market-readiness”, of public safety applications for UAS. As individual assessments of all applications suggested in the literature are infeasible due to their sheer number, we propose a novel set of application categories: Remote Sensing, Mapping, Monitoring, Human-drone Interaction, Flying Ad-hoc Networks, Transportation, and Counter UAV Systems. Each category’s maturity is assessed through a literature review of contained applications, using the metric of Application Readiness Levels (ARLs). Relevant aspects such as the environmental complexity and available mission time of addressed scenarios are taken into account. Following the analysis, we infer that improvements in autonomy and software reliability are the most promising research areas for increasing the usefulness and acceptance of UAS in the public safety domain.

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“…To the best of our knowledge, all of the interception approaches require the interceptor to be faster than the intruder (Moreira et al, 2019), some even up to two times faster than the target (Yang and Quan, 2020), which will be increasingly difficult to achieve, especially since some researchers in the field estimate that within 5 years there could be UAVs with a top speed of 100 m/s (Bond et al, 2019). In this work, inspired by the challenging target speeds of MBZIRC2020 and constrained by limitations of our hardware, we explore the possibility of intercepting a faster and more agile target with a slower UAV.…”

Section: Contributions and Related Workmentioning

confidence: 99%

“…Intercepting a runaway UAV is a well-researched topic in the aerial robotics community (Beard et al, 2002), (Moreira et al, 2019), (Hehn and D'Andrea, 2012), but the Challenge 1 of the MBZIRC2020 competition is more structured than a "runaway UAV" scenario. The structure in the challenge arises from the fact that the shape of the target trajectory is known in advance, while other parameters such as size, location and more importantly orientation of the trajectory are not known in advance.…”

Section: Introductionmentioning

confidence: 99%

Brain over Brawn: Using a Stereo Camera to Detect, Track, and Intercept a Faster UAV by Reconstructing the Intruder's Trajectory

Barisic,

Petric,

Bogdan

2021

Preprint

View full text Add to dashboard Cite

The work presented in this paper demonstrates our approach to intercepting a faster intruder UAV, inspired by the MBZIRC2020 Challenge 1. By leveraging the knowledge of the shape of the intruder's trajectory we are able to calculate the interception point. Target tracking is based on image processing by a YOLOv3 Tiny convolutional neural network, combined with depth calculation using a gimbal-mounted ZED Mini stereo camera. We use RGB and depth data from ZED Mini to extract the 3D position of the target, for which we devise a histogram-of-depth based processing to reduce noise. Obtained 3D measurements of target's position are used to calculate the position, the orientation and the size of a figure-eight shaped trajectory, which we approximate using lemniscate of Bernoulli. Once the approximation is deemed sufficiently precise, measured by Hausdorff distance between measurements and the approximation, an interception point is calculated to position the intercepting UAV right on the path of the target. The proposed method, which has been significantly improved based on the experience gathered during the MBZIRC competition, has been validated in simulation and through field experiments. The results confirmed that an efficient visual perception module which extracts information related to the motion of the target UAV as a basis for the interception, has been developed. The system is able to track and intercept the target which is 30% faster than the interceptor in majority of simulation experiments. Tests in the unstructured environment yielded 9 out of 12 successful results.

show abstract

Interception of Non-Cooperative UAVs

Cited by 6 publications

References 17 publications

Brain over Brawn: Using a Stereo Camera to Detect, Track, and Intercept a Faster UAV by Reconstructing the Intruder’s Trajectory

Brain over Brawn: Using a Stereo Camera to Detect, Track, and Intercept a Faster UAV by Reconstructing the Intruder’s Trajectory

Maturity Levels of Public Safety Applications using Unmanned Aerial Systems: a Review

Brain over Brawn: Using a Stereo Camera to Detect, Track, and Intercept a Faster UAV by Reconstructing the Intruder's Trajectory

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