Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-13T05:54:40.920Z Has data issue: false hasContentIssue false

Unmanned aerial vehicle dynamic path planning in an uncertain environment

Published online by Cambridge University Press:  05 March 2014

Min Yao*
Affiliation:
Nanjing University of Aeronautics and Astronautics, Nanjing, P. R. China
Min Zhao
Affiliation:
Nanjing University of Aeronautics and Astronautics, Nanjing, P. R. China
*
*Corresponding author. E-mail: ym_nuaa@163.com

Summary

An unmanned aerial vehicle (UAV) dynamic path planning method is proposed to avoid not only static threats but also mobile threats. The path of a UAV is planned or modified by the potential trajectory of the mobile threat, which is predicted by its current position, velocity, and direction angle, because the positions of the UAV and mobile threat are dynamically changing. In each UAV planning path, the UAV incurs some costs, including control costs to change the direction angle, route costs to bypass the threats, and threat costs to acquire some probability to be destroyed by threats. The model predictive control (MPC) algorithm is used to determine the optimal or sub-optimal path with minimum overall costs. The MPC algorithm is a rolling-optimization feedback algorithm. It is used to plan the UAV path in several steps online instead of one-time offline to avoid sudden and mobile threats dynamically. Lastly, solution implementation is described along with several simulation results that demonstrate the effectiveness of the proposed method.

Type
Articles
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Kothari, M. and Postlethwaite, I., “A probabilistically robust path planning algorithm for UAVs using rapidly-exploring random trees,” J. Intell. Robot. Syst. 71 (2), 231253 (2012).Google Scholar
2.Wei, S., Yan, G. and Yu, H., “Path planning for UAV based on mixed ant colony algorithm,” J. Syst. Simul. 23 (3), 574576 (2011).Google Scholar
3.Xingguang, P. and Demin, X., “Intelligent online path planning for UAVs in adversarial environments,” Int. J. Adv. Robot. Syst. 9 (3), 112 (2012).Google Scholar
4.Shin, H.-S., Leboucher, C. and Tsourdos, A., “Path Planning for UAVs Under Communication Constraints Using SPLAT and MILP,” 2012 UKACC International Conference on Control, Cardiff, UK (Sep. 3–5, 2012) pp. 298303.CrossRefGoogle Scholar
5.Babel, L., “Three-dimensional route planning for unmanned aerial vehicles in a risk environment,” J. Intell. Robot. Syst. 71 (2), 255269 (2013).Google Scholar
6.Lambert, A., Gruyer, D. and Mangeas, M., “Safe path planning and replanning with unmapped object detection,” IV'2002. IEEE Intelligent Vehicle Symposium. Proceedings, Versailles, France (2003) pp. 166171.Google Scholar
7.Bertsekas, D. P., Shim, D. H. and Doherty, P., “An auction algorithm for shortest paths,” SIAM J. Optim. 1 (4), 425447 (1991).Google Scholar
8.Wang, N., Gu, X., Chen, J., Shen, L. and Ren, M., “Hybrid Neural Network Method for UAV Attack Route Integrated Planning,” 6th International Symposium on Neural Networks, Wuhan, China (May 26–29, 2009) pp. 226235.Google Scholar
9.Mei, H., Tian, Y. and Zu, L., “A hybrid ant colony optimization algorithm for path planning of robot in dynamic environment,” Int. J. Inf. Technol. 12 (3), 7888 (2006).Google Scholar
10.Samar, R. and Kamal, W. A., “Optimal path computation for autonomous aerial vehicles,” Cogn. Comput. 4 (4), 515525 (2012).Google Scholar
11.Koenig, S., Likhachev, M. and Furcy, D., “Lifelong planning A*,” Artifi. Intell. 55 (3), 226235 (2004).Google Scholar
12.Xin, L., Chengping, Z. and Mingyue, D., “Efficient path planning algorithm for UAV,” J. Huazhong Univ. Sci. Technol. 39 (4), 4548 (2001).Google Scholar
13.Earl, M. G. and D'Andrea, R., “Iterative MILP methods for vehicle control problems,” IEEE Trans. Robot. 21 (6), 11581167 (2005).Google Scholar
14.Jain, V. and Grossmann, I. E., “Algorithms for hybrid MILP/CP models for a class of optimization problems,” INFORMS J. Comput. 13 (4), 258276 (2001).Google Scholar
15.Hyun, B., Kabamba, P. T. and Girard, A. R., “Optimally-informative path planning for dynamic Bayesian classification,” Optim. Lett. 6 (8), 16271642 (2012).Google Scholar
16.Pitre, R. R., Li, X. R. and Delbalzo, R., “UAV route planning for joint search and track missions-an information-value approach,” IEEE Trans. Aerosp. Electron. Syst. 48 (3), 25512565 (2012).CrossRefGoogle Scholar
17.Zhang, C. G. and Xi, Y. G., “Robot rolling path planning based on locally detected information,” Acta Autom. Sin. 29, 3844 (2003).Google Scholar
18.Chen, Y., Han, J. and Zhao, X., “Three-dimensional path planning for unmanned aerial vehicle based on linear programming,” Robotica 30 (5),773781 (2012).Google Scholar
19.Amin, J. N., Boskovic, J. D. and Mehra, R. K., “A Fast and Efficient Approach to Path Planning for Unmanned Vehicles,” Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit, Keystone, USA (Aug. 21–24, 2006) pp. 2022.Google Scholar
20.Qu, Y.-H., Pan, Q. and Yan, J.-G., “Flight Path Planning of UAV Based on Heuristically Search and Genetic Algorithms,” Proceedings of the Annual Conference of IEEE Industrial Electronics Society, Raleigh, USA (Nov. 6–10, 2005) pp. 4549.Google Scholar
21.Hsiao, Y. T., Chnang, C. L. and Chien, C. C., “Ant Colony Optimization for Best Path Planning,” Proceedings of the International Symposium on Communications and Information Technologies, Sapporo, Japan (Oct. 26–29, 2004) pp. 109113.Google Scholar
22.Wang, Z. H., Zhang, W. G., Shi, J. P. and Han, Y., “UAV Route Planning Using Multi-Objective Ant Colony System,” Proceedings of the 2008 IEEE Conference on Cybernetics and Intelligent, Chengdu, P. R. China (Sep. 21–24, 2008) pp. 797800.Google Scholar
23.Shannon, S. T., Optimal Path Planning for Single and Multiple Aircraft Using a Reduced Order Formulation Ph.D. Thesis (Atlanta, GA: Georgia Institute of Technology, 2007).Google Scholar
24.Lechevin, N., Rabbath, C. A. and Lauzon, M., “Cooperative and Deceptive Planning of Multiformations of Networked UCAVs in Adversarial Urban Environments,” Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit, Hilton Head, USA (Aug. 20–23, 2007) pp. 113.Google Scholar
25.Michalewicz, Z., Genetic Algorithm + Data Structure = Evolution Programs, 3rd rev. and extended (Springer-Verlag, Berlin, Germany, 1996).CrossRefGoogle Scholar