Abstract
In this paper autonomous formation control for Unmanned Aerial Vehicles (UAVs) has been discussed and a real time solution has been put forward by benefiting General Purpose Graphical Processing Units (GPGPU) accelerated potential field approach while ensuring obstacle and collision avoidance in unknown environment by using real-time sensors. GPGPU accelerated real time formation control for UAVs was designed and the basic model of the approach has been explained in our previous work (Cetin and Yilmaz 2014). As the deficiencies of the previous approach, autonomous real time collision and coordinated obstacle avoidance features in unknown environments are also handled while maintaining formation flight conditions in this work. With these features, improved autonomous formation control approach is discussed as a real time solution. The computation is performed by using Graphical Processing Units (GPUs) as parallel computation architectures by benefiting from Single Instruction Multiple Data (SIMD) type parallel algorithms. Classic binary map conversation, connected component labeling and minimum bounding box algorithms which are commonly used for image processing applications, has been evaluated for real time obstacle detection and avoidance features by developing GPGPU suitable parallel algorithms. Real-time solution has been developed by integrated these parallel algorithms with parallel Artificial Potential Field (APF) computation algorithm. Simulation results are proved that this novel autonomous improved formation control approach is successful and it would be used in real time applications like UAV formation flight missions.
Similar content being viewed by others
References
Cetin, O., Yilmaz, G.: GPGPU accelerated real-time potential field based formation control for unmanned aerial vehicles. In: International Conference on Unmanned Aerial Vehicles, ICUAS14, pp. 103–114. Orlando (2014). doi:10.1109/ICUAS.2014.6842245
Gu, Y., Campa, G., Innocenti, M.: Formation Flight Control. Int. J. Aerosp. Eng. 798981, 2 (2011). doi:10.1155/2011/798981
Elkaim, G., Kelbley, R.: A lightweight formation control methodology for a swarm of non-holonomic vehicles. In: Aerospace Conference (2006)
Ding, J., Fan, Q.: A multi-UAV tight formation flight controller. IEEE Int. Conf. Comput. Sci. Autom. Eng. (CSAE) 1, 60–64 (2012). doi:10.1109/CSAE.2012.6272548
Do, K.D.: Formation control of mobile agents using local potentia functions. In: Proceedings of the American Control Conference (2006)
Pachter, M., Azzo, J.J.D’., Proud, A.W.: Tight formation flight control. J. Guid. Control Dyn. 24(2), 246–254 (2001)
Liping, Y.: Decentralized formation flight control of multiple fixed-wing UAVs with a virtual leader. In: Control Conference (CCC), 31st Chinese, pp 6368–6375 (2012)
Wang, X., Vivek, Y., Balakrishnan, S.N.: Cooperative UAV formation flying with obstacle/collision avoidance. IEEE Trans. Control Syst. Technol. 15(4), 672–679 (2007). doi:10.1109/TCST.2007.899191
Anderson, B.D.O., Fidan, B., Yu, C., Walle, D.: UAV formation control: Theory and application. Recent Adv. Learn. Control Lect. Notes Control Inf. Sci. 371, 15–33 (2008)
Kim, J., Khosla, P.K.: Real-time obstacle avoidance using harmonic potential functions. IEEE Trans. Robot. Autom. 8(3), 338,349 (1992). doi:10.1109/70.143352
Narayanan, P., Harish, J.: Accelerating large graph algorithms on the GPU using CUDA. High Perform. Comput. – HiPC 2007, Lect. Notes Comput. Sci. 4873, 197–208 (2007)
Braillon, C., Pradalier, C., Crowley, J.L., Laugier, C.: Real-time moving obstacle detection using optical flow models, pp 466–471. IEEE, Intelligent Vehicles Symposium (2006). doi:10.1109/IVS.2006.1689672
Gallup, D., Pollefeys, M., Frahm, J.M.: 3D reconstruction using an n-layer heightmap. Pattern Recogn. Lect. Notes Comput. Sci. 6376, 1–10 (2010)
Hea, L., Chaob, Y., Suzukic, K., Wud, K.: Fast connected-component Labeling. Pattern Recogn. 42(9), 1977–1987 (2009)
Hawick, K.A., Leist, A., Playne, D.P.: Parallel graph component labelling with GPUs and CUDA Parallel Computing 36, 655–678 (2010)
Chen, Y.H.: Determining parting direction based on minimum bounding box and fuzzy logics. Int. J. Mach. Tools Manuf. 37(9), 1189–1199 (1997)
Barnes, L.E., Fields, M.A., Valavanis, K.P.: Swarm formation control utilizing elliptical surfaces and limiting functions. IEEE Trans. Syst., Man, Cybern.—Part B: Cybern. 39, 6 (2009)
Whitepaper, NVIDIA’s Next Generation CUDA Compute Architecture: Kepler GK110, http://www.nvidia.com/content/PDF/kepler/NVIDIA-Kepler-GK110-Architecture-Whitepaper.pdf. Accessed 14 Nov 2014
Luebke, D., all, et: GPGPU: general-purpose computation on graphics hardware. In: Proceedings of the 2006 ACM/IEEE conference on Supercomputing, Article No. 208 (1995)
Author information
Authors and Affiliations
Corresponding author
Additional information
Research supported by The Scientific and Technological Research Council of Turkey, with project number 112E281.
Rights and permissions
About this article
Cite this article
Cetin, O., Yilmaz, G. Real-time Autonomous UAV Formation Flight with Collision and Obstacle Avoidance in Unknown Environment. J Intell Robot Syst 84, 415–433 (2016). https://doi.org/10.1007/s10846-015-0318-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10846-015-0318-8