Abstract
In recent years, Unmanned Aerial Vehicles (UAVs) have been used in many military and civil application areas, due to their increased endurance, performance, portability, and their larger payload-carrying, computing and communication capabilities. Because of UAVs’ complex operation areas and complicated constraints related to the assigned task, they have to fly on a path, which is calculated online and/or offline to satisfy these constraints and to check some control points in the operation theatre. If the number of control points and constraints increases, finding a feasible solution takes up too much time in this large operation area. In this case, the use of multi-UAVs decreases operation completion time; however, this usage increases the complexity of finding a feasible path problem. This problem is typically NP-hard and genetic algorithms have been successfully utilized for solving it in the last few decades. This paper presents how a flyable trajectory can be constructed for multi-UAV systems by using a Genetic Algorithm (GA) in a known environment and at a constant altitude. A GA is implemented parallel in a multi-core environment to increase the performance of the system. First, a feasible path is calculated by using a parallel GA, and then the path is smoothed by using Bezier curves to convert it flyable. Preliminary results show that the proposed method provides an effective and feasible path for each UAV in an Unmanned Aerial System with multi-UAVs. The proposed system is realized in Java with a GUI for showing results. This paper also outlines future work that can be conducted on the multi-UAV path planning.
Similar content being viewed by others
References
Al-Sultan, K.S., Aliyu, M.D.S.: A new potential field-based algorithm for path planning. J. Intell. Robot. Syst. 17(3), 265–282 (1996)
Peng, H., Su, F., Bu, Y., Zhang, G., Shen, L.: Cooperative area search for multiple UAVs based on RRT and decentralized receding horizon optimization. In: 7th Asian Control Conference, ASCC 2009, pp. 298–303 (2009)
Roberge, V., Tarbouchi, M., Labonte, G.: Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Trans. Ind. Inf. 9(1), 132–141 (2013)
Zhang, C., Zhen, Z., Wang, D., Li, M.: UAV path planning method based on ant colony optimization. In: Control and Decision Conference (CCDC), 2010 Chinese, pp. 3790–3792 (2010)
Qu, Y., Pan, Q., Yan, J.: Flight path planning of UAV based on heuristically search and genetic algorithms. In: 31st Annual Conference of IEEE Industrial Electronics Society, IECON 2005 (2005)
Nikolos, I.K., Valavanis, K.P., Tsourveloudis, N.C., Kostaras, A.: Evolutionary algorithm based offline/online path planner for UAV navigation. IEEE Trans. Syst. Man Cybern. Cybern. 33, 898–912 (2003)
Lei, L., Shiru, Q.: Path planning for unmanned air vehicles using an improved artificial bee colony algorithm. In: 31st Chinese Control Conference (CCC) 2012, pp. 2486–2491 (2012)
Sahingoz, O.K.: Large scale wireless sensor networks with multi-level dynamic key management scheme. J. Syst. Archit. 59(9), 801–807 (2013). doi:10.1016/j.sysarc.2013.05.022
United States Cost Guards: http://www.uscg.mil/acquisition/uas/. Last access August 2013
Sahingoz, O.K.: Flyable path planning for a multi-UAV system with genetic algorithms and bezier curves. In: 2013 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 41–48 (2013)
Bekmezci, I., Sahingoz, O.K., Temel, S.: Flying ad-hoc networks (FANETs): a survey. Ad. Hoc. Netw. 11(3), 1254–1270 (2013)
Sahingoz, O.K.: Mobile networking with UAVs: opportunities and challenges. In: International Conference on Unmanned Aircraft Systems (ICUAS-2013), pp. 933–941 (2013)
Bektas, T.: The multiple traveling salesman problem: an overview of formulations and solution procedures. Omega 34, 209–219 (2006)
Ozalp, N., Sahingoz, O.K.: Optimal UAV path planning in a 3D threat environment by using parallel evolutionary algorithms. In: International Conference on Unmanned Aircraft Systems (ICUAS) 2013, pp. 308–317 (2013)
Complete Guide to World Wind: http://goworldwind.org/features/. Accessed May 2013
Bell, D.G., Kuehnel, F., Maxwell, C., Kim, R., Kasraie, K., Gaskins, T., Hogan, P., Coughlan, J.: NASA world wind: open source GIS for mission operations. In: IEEE Aerospace Conference, pp. 1–9 (2007)
Giardini, G., Kalmár-Nagy, T.: Genetic algorithm for combinatorial path planning: the subtour problem. Math. Probl. Eng. 2011, 31 (2011). doi:10.1155/2011/483643
Shim, V.A., Tan, K.C., Cheong, C.Y.: A hybrid estimation of distribution algorithm with decomposition for solving the multiobjective multiple traveling salesman problem. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42(5), 682–691 (2012)
Rasche, C., Stern, C., Kleinjohann, L., Kleinjohann, B.: A distributed multi-UAV path planning approach for 3D environments. In: 5th International Conference on Automation, Robotics and Applications (ICARA), pp. 7–12 (2011)
Ergezer, H., Leblebicioglu, K.: Path planning for multiple unmanned aerial vehicles. In: 20th Signal Processing and Communications Applications Conference (SIU), pp. 1–4 (2012)
Moon, S., Oh, E., Shim, D.H.: An integral framework of task assignment and path planning for multiple unmanned aerial vehicles in dynamic environments. J. Intell. Robot. Syst. 70(1–4), 303–313 (2012)
Grotli, E.I., Johansen, T.A.: Path planning for UAVs under communication constraints using SPLAT! and MILP. J. Intell. Robot. Syst. 65(1–4), 265–282 (2012)
Nigam, N., Bieniawski, S., Kroo, I., Vian, J.: Control of multiple UAVs for persistent surveillance: algorithm and flight test results. IEEE Trans. Control Syst. Technol. 20(5), 1236–1251 (2012)
Besada-Portas, E., de la Torre, L., Moreno, A., Risco-Martín, J.L.: On the performance comparison of multi-objective evolutionary UAV path planners. Inf. Sci. 238, 111–125 (2013). ISSN 0020–0255
Lim, C.W., Ryoo, C.K., Choi, K., Cho, J.H.: Path generation algorithm for intelligence, surveillance and reconnaissance of an UAV. In: SICE Annual Conference 2010, pp. 1974–1977 (2010)
Kan, E., Lim, M., Yeo, S., Ho, J., Shao, Z.: Contour based path planning with B-spline trajectory generation for unmanned aerial vehicles (UAVs) over hostile terrain. J. Intell. Learn. Syst. Appl. 3(3), 122–130 (2011). doi:10.4236/jilsa.2011.33014
Kan, E.M., Lim, M.H., Yeo, S.P., Shao, Z.H., Ho, J.S.: Radar-aware path planning with B-spline trajectory generation for unmanned aerial vehicles (UAVs). Int. J. Reason. Based Intell. Syst. 2(3–4), 226–236 (2010)
Choi, J., Curry, R., G. Elkaim: Piecewise Bezier curves path planning with continuous curvature constraint for autonomous driving. In: Machine Learning and Systems Engineering. Lecture Notes in Electrical Engineering, pp. 31–45 (2010)
Bezier Curve: http://en.wikipedia.org/wiki/Bezier_curve. Last access August 2013
Travelling Salesman Problem Libraries: (online) http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/tsp/. Last access August 2013
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sahingoz, O.K. Generation of Bezier Curve-Based Flyable Trajectories for Multi-UAV Systems with Parallel Genetic Algorithm. J Intell Robot Syst 74, 499–511 (2014). https://doi.org/10.1007/s10846-013-9968-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10846-013-9968-6