Madhu Babu Vankadari
ABSTRACT
In this paper, we present a low cost leader-follower formation control architecture of UAVs. The low cost architecture comprises of two AR. Drones and two Raspberry Pi. The computation of each drones has been done in cost effective Raspberry Pi. The relative localization among the drones has been done using Aruco Marker. A gradient descent based self-tuning PID controller is used by the follower drone to preserve the formation with respect to the leader drone. Experimental results as well as simulation results have shown in this paper.
- A.K Das, R Fierro, V Kumar, J.P Ostrowski, J Spletzer, and C.J Taylor. 2002. A vision-based formation control framework. IEEE Transactions on Robotics and Automation 18 (2002), 813--825.Google Scholar
Cross Ref
- R. Kurazume, S. Nagata, and S. Hirose. 1992. Cooperative positioning with multiple robots. In Robotics and Automation Conference Proceedings. IEEE, 1250--1257.Google Scholar
- R Kurazume and S Hirose. 2000. An experimental study of a cooperative positioning system. Autonomous Robots 8 (2000), 43--52. Google ScholarDigital Library
- A. DePriest S. Das O. Falardeau O. Dugas I. Rekleitis, P. Babin and P. Giguere. 2015. Experiments in Quadrotor Formation Flying Using On-Board Relative Localization. In Workshop on Vision-based Control and Navigation of Small, Leigh-weight UAVs. IROS.Google Scholar
- S. Roumeliotis and G. Bekey. 2000. Collective localization: A distributed kalman filter approach to localization of groups of mobile robots. In Robotics and Automation Conference Proceedings. IEEE, 2958--2965.Google Scholar
- S. Roumeliotis and G. Bekey. 2000. Synergetic localization for groups of mobile robots. In 39th IEEE Conf. on Decision and Control Proceedings, Vol. 4. 3477--3482.Google Scholar
- G. Dudek I. Rekleitis and E. Milios. 2003. Probabilistic cooperative localization and mapping in practice. In IEEE Int. Conf. on Robotics and Automations Proceedings. 1907--1912.Google Scholar
- W Daniel, R Gerhard, M Alessandro, T Drummond, and S Dieter. 2010. Real-time detection and tracking for augmented reality on mobile phones. IEEE Transactions on Visualization and Computer Graphics 16 (2010), 355--368. Google ScholarDigital Library
- G. Klein and D. Murray. 2007. Parallel tracking and mapping for small ar workspaces. In 6th IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR'17) Proceedings. IEEE Computer Society, 1--10. Google ScholarDigital Library
- K. Mikolajczyk and C. Schmid. 2001. Indexing based on scale invariant interest points. In International Conference on Computer Vision (ICCV) Proceedings. 525--531.Google Scholar
- D. G. Lowe. 1999. Object recognition from local scale-invariant features. In International Conference on Computer Vision (ICCV) Proceedings, Vol. 2. IEEE Computer Society. Google ScholarDigital Library
- P Bhattacharya and Ms Gavrilova. 2013. A survey of landmark recognition using the bag-of-words framework. Intelligent Computer Graphics 441 (2013), 243--263.Google Scholar
- T Krajnk, M Nitsche, J Faigl, P Vank, L Saska, M and Peuil, T Duckett, and M Mejail. 2014. A Practical Multirobot Localization System. Journal of Intelligent and Robotic Systems 76 (2014), 539--562. Google ScholarDigital Library
- G. Loianno J. Polin V. Kumar R. Tron, J. Thomas and K. Daniilidis. 2014. Vision-Based Formation Control of Aerial Vehicles. In Workshop on Distributed Control and Estimation for Robotic Vehicle Networks.Google Scholar
- Y. Ayatsuka J. Rekimoto and Cybercode. 2000. Designing augmented reality environments with visual tags. In Designing augmented reality environments (DARE'00). ACM, 1--10. Google ScholarDigital Library
- M Rohs and B Gfeller. 2004. Using camera-equipped mobile phones for interacting with real-world objects. Advances in Pervasive Computing (2004), 265--271.Google Scholar
- D. Claus and A. Fitzgibbon. 2005. Reliable automatic calibration of a marker-based position tracking system. In Workshop on the Applications of Computer Vision Proceedings. 300--305. Google ScholarDigital Library
- A. Domahidi M. Morari T. Nageli, C. Conte and O. Hilliges. 2014. Environment-independent formation flight for micro aerial vehicles. In Int. Conf. on Intelligent Robots and Systems. IEEE, 1141--1146.Google Scholar
- D. Wagner and D. Schmalstieg. 2007. Artoolkitplus for pose tracking on mobile devices. In Computer Vision Winter Workshop. 139--146.Google Scholar
- K. Schwabe M. Faessler, E. Mueggler and D. Scaramuzza. 2014. A Monocular Pose Estimation System based on Infrared LEDs. In Int. Conf. on Robotics and Automation. IEEE.Google Scholar
- A. Masselli K. E. Wenzel and A. Zell. 2012. Visual Tracking and Following of a Quadrocopter by another Quadrocopter. In Int.Conf. on Intelligent Robots and Systems. IEEE.Google Scholar
- A. Masselli J. J. Lugo and A. Zell. 2013. Following a quadrotor with another quadrotor using onboard vision. In European Conf. on Mobile Robots (ECMR). IEEE.Google Scholar
- R Munoz-Salinas and S Garrido-Jurado. 20013. Aruco library. http://sourceforge.net/projects/aruco/.Google Scholar
- P. Bristeau, F. Callou, D. Vissiere, and N. Petit. 2011. The navigation and control technology inside the AR. drone micro UAV. In IFAC World Congress. IFAC, 1477--1484.Google Scholar
- G. Jurado, R. Munoz-Salinas, F. J. Madrid-Cuevas, and M. J. Marin-Jimenez. 2014. Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recognition 47 (2014), 2280--2292. Google ScholarDigital Library
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