Pregled bibliografske jedinice broj: 1256220
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 // Field Robotics, 2 (2022), 1; 222-240 doi:10.55417/fr.2022009 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 1256220 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Brain over Brawn: Using a Stereo Camera to Detect, Track, and Intercept a Faster UAV by Reconstructing the Intruder’s Trajectory
Autori
Barišić, Antonella ; Petric, Frano ; Bogdan, Stjepan
Izvornik
Field Robotics (2771-3989) 2
(2022), 1;
222-240
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
aerial robotics, computer vision, perception, position estimation, visual serving
Sažetak
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.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo, Informacijske i komunikacijske znanosti
POVEZANOST RADA
Projekti:
EK-H2020-810321 - Twinning koordinacijska akcija za širenje izvrsnosti i sudjelovanja u zračnoj robotici – AeRoTwin (AeRoTwin) (Bogdan, Stjepan, EK ) ( CroRIS)
--KK.01.1.1.01.009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (DATACROSS) (Šmuc, Tomislav; Lončarić, Sven; Petrović, Ivan; Jokić, Andrej; Palunko, Ivana) ( CroRIS)
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
