JACIII Vol.24 No.7 pp. 820-828
doi: 10.20965/jaciii.2020.p0820


Three-Dimensional Unmanned Aerial Vehicle Route Planning Using Hybrid Differential Evolution

Hao Zhang*1, Lihua Dou*1,*2,*3, Chunxiao Cai*4,†, and Bin Xin*1,*2,*3

*1School of Automation, Beijing Institute of Technology
No.5 Zhongguancun South Street, Haidian, Beijing 100081, China

*2State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology
No.5 Zhongguancun South Street, Haidian, Beijing 100081, China

*3Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology
No.5 Zhongguancun South Street, Haidian, Beijing 100081, China

*4Center for Assessment and Demonstration Research Academy of Military Sciences
Courtyard No.1, Xianghongmen East Gate, Haidian, Beijing 100091, China

Corresponding author

October 2, 2020
October 27, 2020
December 20, 2020
unmanned aerial vehicle, route planning, differential evolution, route smoothing

Unmanned aerial vehicles (UAVs) have been investigated proactively owing to their promising applications. A route planner is key to UAV autonomous task execution. Herein, a hybrid differential evolution (HDE) algorithm is proposed to generate a high-quality and feasible route for fixed-wing UAVs in complex three-dimensional environments. A multiobjective function is designed, and both the route length and risk are optimized. Multiple constraints based on actual situations are considered, including UAV mobility, terrain, forbidden flying areas, and interference area constraints. Inspired by the wolf pack search algorithm, the proposed HDE algorithm combines differential evolution (DE) with an approaching strategy to improve the search capability. Moreover, considering the dynamic properties of fixed-wing UAVs, the quadratic B-spline curve is used for route smoothing. The HDE algorithm is compared with a state-of-the-art UAV route planning algorithm, i.e., the modified wolf pack search algorithm, and the traditional DE algorithm. Several numerical experiments are performed, and the performance comparison of algorithms shows that the HDE algorithm demonstrates better performances in terms of solution quality and constraint-handling ability in complex three-dimensional environments.

Cite this article as:
H. Zhang, L. Dou, C. Cai, and B. Xin, “Three-Dimensional Unmanned Aerial Vehicle Route Planning Using Hybrid Differential Evolution,” J. Adv. Comput. Intell. Intell. Inform., Vol.24 No.7, pp. 820-828, 2020.
Data files:
  1. [1] P. Yao, H. Wang, and Z. Su, “Real-time path planning of unmanned aerial vehicle for target tracking and obstacle avoidance in complex dynamic environment,” Aerospace Science and Technology, Vol.47, pp. 269-279, 2015.
  2. [2] L. Yu and K. Zhou, “A dynamic local path planning method for outdoor robot based on characteristics extraction of laser rangefinder and extended support vector machine,” Int. J. of Pattern Recognition and Artificial Intelligence, Vol.30, No.2, pp. 1-23, 2016.
  3. [3] P. Yang, K. Tang, J. A. Lozano, and X. Cao, “Path planning for single unmanned aerial vehicle by separately evolving waypoints,” IEEE Trans. on Robotics, Vol.31, No.5, pp. 1130-1146, 2015.
  4. [4] G.-G. Wang, H. C. E. Chu, and S. Mirjalili, “Three-dimensional path planning for UCAV using an improved bat algorithm,” Aerospace Science and Technology, Vol.49, pp. 231-238, 2016.
  5. [5] C. Huang and J. Y. Fei, “UAV Path Planning Based on Particle Swarm Optimization with Global Best Path Competition,” Int. J. of Pattern Recognition and Artificial Intelligence, Vol.32, No.6, Article No.1859008, 2018.
  6. [6] X. Wang, Y. Liang, S. Liu, and L. Xu, “Bearing-Only Obstacle Avoidance Based on Unknown Input Observer and Angle-Dependent Artificial Potential Field,” Sensors, Vol.19, No.1, Article No.31, 2019.
  7. [7] R. A. Martin, I. Rojas, K. Franke, and J. D. Hedengren, “Evolutionary View Planning for Optimized UAV Terrain Modeling in a Simulated Environment,” Remote Sensing, Vol.8, No.1, Article No.26, 2016.
  8. [8] V. Roberge, M. Tarbouchi, and G. Labonté, “Fast Genetic Algorithm Path Planner for Fixed-Wing Military UAV Using GPU,” IEEE Trans. on Aerospace and Electronic Systems, Vol.54, No.5, pp. 2105-2117, 2018.
  9. [9] S. A. Gautam and N. Verma, “Path Planning for Unmanned Aerial Vehicle Based on Genetic Algorithm and Artificial Neural Network in 3D,” Proc. of the 2014 Int. Conf. on Data Mining and Computing (ICDMIC 2014), pp. 1-5, 2014.
  10. [10] H. Duan, X. Zhang, J. Wu, and G. Ma, “Max-min adaptive ant colony optimization approach to multi-UAVs coordinated trajectory replanning in dynamic and uncertain environments,” J. of Bionic Engineering, Vol.6, No.2, pp. 161-173, 2009.
  11. [11] X. Zhang and H. Duan, “An improved constrained differential evolution algorithm for unmanned aerial vehicle global route planning,” Applied Soft Computing, Vol.26, pp. 270-284, 2015.
  12. [12] Y. Chen et al., “Three-dimensional unmanned aerial vehicle path planning using modified wolf pack search algorithm,” Neurocomputing, Vol.266, pp. 445-457, 2017.
  13. [13] C. Yang, X. Tu, and J. Chen, “Algorithm of marriage in honey bees optimization based on the wolf pack search,” Proc. of the 2007 Int. Conf. on Intelligent Pervasive Computing, pp. 462-467, 2007.
  14. [14] S. Das and P. N. Suganthan, “Differential evolution: A survey of the state of the art,” IEEE Trans. on Evolutionary Computation, Vol.15, No.1, pp. 4-31, 2011.
  15. [15] K. Deb, “An efficient constraint handling method for genetic algorithms,” Computer Methods in Applied Mechanics and Engineering, Vol.186, Nos.2-4, pp. 311-338, 2000.

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