Abstract
This paper introduces a dynamic path planning method for the UAV that can avoid both static and moving obstacles. The condition with sudden threats can better reflect the real situation of the UAV in the real environment. First of all, the A* algorithm is adopted to generate an optimal path in a known environment in this method. Then, in the situation of static sudden threats, a series of candidate paths are generated by the principle of cubic spline second-order continuity. In order to make the static sudden threat at the center of a cluster of candidate paths, they need to be adjusted. After that, this path cluster completely surrounds the sudden threat and has symmetry about the sudden threat. When encountering a sudden threat of movement, factors such as the speed, acceleration and certain parameters of the movement obstacle or the UAV are considered, and a correlation model of the dynamic sudden threat is established. Finally, the total cost function is established to select the optimal obstacle avoidance path, and the total cost function contains four sub-cost functions, they are static security cost function, smoothness cost function, consistency cost function and dynamic security cost function. The simulation results demonstrate the effectiveness of the proposed method.
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References
Ollero, A., Kondak, K.: 10 years in the cooperation of unmanned aerial system. In: 2012 IEEE/RSJ International Conference on Intelligence Robots and Systems, p 5450C5451 (2012)
Jiang, B., Bishop, A.N., Anderson, B.D., Drake, S.P.: Optimal path planning and sensor placement for mobile target detection. Automatica 60, 127C139 (2015)
Guruprasad, K., Ghose, D.: Deploy and search strategy for multi-agent systems using Voronoi partitions. In: 4th International Symposium on Voronoi Diagrams in Science and Engineering, p 91C100 (2007)
Hayat, S., Yanmaz, E., Muzaffar, R: Survey on unmanned aerial vehicle networks for civil applications: a communications viewpoint. IEEE Communications Surveys and Tutorials 18(4), 2624C2661 (2016)
Sung, I., Nielsen, P.: Zoning a service area of unmanned aerial vehicles for package delivery services. J. Intell. Robot. Syst. (2019)
Babel, L.: Coordinated target assignment and UAV path planning with timing constraints. J. Intell. Robot. Syst. 94, 857 (2019)
Yao, W., Lu, H., Zeng, Z.: Distributed static and dynamic circumnavigation control with arbitrary spacings for a heterogeneous multi-robot system. J. Intell. Robot. Syst. 94, 883 (2019)
Lei, T., Zhang, Y., Lu, J.: The application of UAV remote sensing in mapping of damaged buildings after earthquakes. International Conference on Digital Image Processing, 10806 (2018)
Rossi, M., Brunelli, D: Autonomous gas detection and mapping with unmanned aerial vehicles. IEEE Trans. Instrum. Meas. 65(4), 765C775 (2016)
Primatesta, S., Rizzo, A., Cour-Harbo, LA: A ground risk map for unmanned aircraft in urban environments. J. Intell. Robot. Syst. (2019)
Mairaj, A., Baba, A.I., Javaid, A.Y.: Application specific drone simulators: recent advances and challenges. Simul. Model. Pract. Theory, 100–117 (2019)
Haartsen, Y., Aalmoes, R., Cheung, Y.: Simulation of unmanned aerial vehicles in the determination of accident locations. In: ICUAS 2016, International Conference on Unmanned Aircraft Systems, p 993C1002 (2016)
Wang, M., Liu, J.N.K.: Fuzzy logic based robot path planning in unknown environment. In: The IEEE International Conference on Machine Learning and Cybernetics, p 813C 818. IEEE (2005)
Lumelsky, V.J., Stepanov, A.A.: Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape. Algorithmica 2, 403C430 (1987)
Kamon, I., Rivlin, E., Rimon, E.: New range-sensor based globally convergent navigation algorithm for mobile robots. In: IEEE International Conference on Robotics and Automation, p 429C435 (1996)
Molinos, E.J., Llamazares, N., Oca?a, M.: Dynamic window based approaches for avoiding obstacles in moving. Robotics and Autonomous Systems 118, 112C1 (2019)
Fox, D., Burgard, W., Thrun, S.: Dynamic window approach to collision avoidance. IEEE Robot. Autom. Mag. 4(1), 23–33 (1997)
Ogren, P., Leonard, N.E.: A convergent dynamic window approach to obstacle avoidance. IEEE Trans. Robot. 21(2), 188–195 (2005)
Zuo, L., Guo, Q., Xu, X., Fu, H.: A hierarchical path planning approach based on a and least-squares policy iteration for mobile robots. Neurocomputing 170(c), 257C266 (2015)
Yuan, Y., Xing-she, Z., Kai-long, Z.: Dynamic trajectory planning for unmanned aerial vehicle based on sparse A* search and improved artificial potential fiel. Control Theory and Applications 27(07), 953–959 (2010)
Haddock, J., Mittenthal, J.: Simulation optimization using simulated annealing. Comput. Ind. Eng. 22(4), 387–395 (1992)
Pierreval, H., Tautou, L.: Using evolutionary algorithms and simulation for the optimization of manufacturing systems. IIE Transactions (Institute of Industrial Engineers) 29(3), 181–189 (1997)
Alireza Feyzbakhsh, S., Matsui, M.: Adam-Eve-like genetic algorithm: a methodology for optimal design of a simple flexible. Comput. Ind. Eng., 233–258 (1999)
Phung, M.D., Cong, H.Q., Dinh, T.H., Ha, Q.: Enhanced discrete particle swarm optimization path planning for UAV vision-based surface inspection. Autom. Constr. 81, 25C33 (2017)
Yang, K., Sukkarieh, S.: 3D smooth path planning for a UAV in cluttered natural environments. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 794–800. IEEE (2008)
Jayasinghe, J.A.S., Athauda, M.B.G.D.A.: Smooth trajectory generation algorithm for an unmanned aerial vehicle (UAV) under dynamic constraints: using a quadratic Bzier curve for collision avoidance. In: 2016 Manufacturing and Industrial Engineering Symposium: Innovative Applications for Industry, pp 1–6. MIES (2016)
Zhou, S., Zhu, G., Li, H., Wang, Y., Liu, X.: Real-time route planning for UAV based on weather threat. In: 2011 International Conference on Remote Sensing, Environment and Transportation Engineering, vol. 2011, pp 2342–2345. RSETE (2011)
Xia, C., Yudi, A.: Application of improved neural network in 3D path planning of UAVs. Electron. Opt. Control. 25(9), 7–11 (2018)
Xia, C., Yudi, A., Hongli, L.: Research on three-dimensional path planning of UAV based on improved ant colony algorithm. Tactical Missile Technology 02, 59–66 (2019)
Wang, H., Kearney, J., Atkinson, K.: Arc-length parameterized spline curves for real-time simulation. In: 5th International Conference on Curves and Surfaces, pp 387–396 (2002)
Maneev, V.V., Syryamkin, M.V.: Optimizing the A* search algorithm for mobile robotic devices. Materials Science and Engineering 516(1) (2019)
Qi, Z., Aqun, Z.: A multipath seeking algorithm based on a * algorithm. J. Electron. Inf. Technol. 35(04), 952–957 (2010)
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Chen, X., Zhao, M. & Yin, L. Dynamic Path Planning of the UAV Avoiding Static and Moving Obstacles. J Intell Robot Syst 99, 909–931 (2020). https://doi.org/10.1007/s10846-020-01151-x
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DOI: https://doi.org/10.1007/s10846-020-01151-x