Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-28T23:45:19.192Z Has data issue: false hasContentIssue false
  • English
  • Français

Three-dimensional path planning for unmanned aerial vehicle based on linear programming

Published online by Cambridge University Press:  15 September 2011

Yang Chen ,
Jianda Han  and
Xingang Zhao
Yang Chen
Affiliation:
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, P.R. China School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, P.R. China Graduate School, Chinese Academy of Sciences, Beijing 100039, P.R. China
Jianda Han*
Affiliation:
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, P.R. China
Xingang Zhao
Affiliation:
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, P.R. China
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

In this paper, an approach based on linear programming (LP) is proposed for path planning in three-dimensional space, in which an aerial vehicle is requested to pursue a target while avoiding static or dynamic obstacles. This problem is very meaningful for many aerial robots, such as unmanned aerial vehicles. First, the tasks of target-pursuit and obstacle-avoidance are modelled with linear constraints in relative coordination according to LP formulation. Then, two weighted cost functions, representing the optimal velocity resolution, are integrated into the final objective function. This resolution, defined to achieve the optimal velocity, deals with the optimization of a pair of orthogonal vectors. Some constraints, such as boundaries of the vehicle velocity, acceleration, sensor range, and flying height, are considered in this method. A number of simulations, under static and dynamic environments, are carried out to validate the performance of generating optimal trajectory in real time. Compared with ant colony optimization algorithm and genetic algorithm, our method has less parameters to tune and can achieve better performance in real-time application.

Keywords

Path planning in three dimensionsObstacle avoidanceTarget pursuitUncertain environment
Type
Articles
Information
Robotica , Volume 30 , Issue 5 , September 2012 , pp. 773 - 781
Copyright
Copyright © Cambridge University Press 2011

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.Shih, C. L., Lee, T. T. and Gruver, W. A., “A unified approach for robot motion planning with moving polyhedral obstacles,” IEEE Trans. Syst. Man Cybern. 20 (4), 903–915 (1990).CrossRefGoogle Scholar
2.Kitamura, Y., Tanaka, T., Kishino, F. and Yachida, M., “3-D path planning in a dynamic environment using an octree and an artificial potential field,” In: Proceeding of IEEE/RSJ International Conference on Intelligent Robots & Systems (IROS), (Oct. 11–15, 1995) pp. 474–481.Google Scholar
3.Zhang, Y. and Valavanis, K. P., “A 3-D potential panel method for robot motion planning,” Robotica 15 (4), 421–434 (1997).CrossRefGoogle Scholar
4.Ge, S. S. and Cui, Y. J., “New potential functions for mobile robot path planning,” IEEE Trans. Robot. Autom. 16 (5), 615–620 (2000).CrossRefGoogle Scholar
5.Fahimi, F., Autonomous Robots Modeling, Path Planning, and Control (Springer Science + Business Media, Dordrecht, Netherlands, 2009).CrossRefGoogle Scholar
6.Mou, C., Qing-xian, W. and Chang-sheng, J., “A modified ant optimization algorithm for path planning of UCAV,” Appl. Soft Comput. 8 (4), 1712–1718 (2008).Google Scholar
7.Hao, Y., Zu, W. and Zhao, Y., “Real-time obstacle avoidance method based on polar coordination particle swarm optimization in dynamic environment,” In: 2nd IEEE Conference on Industrial Electronics and Applications (ICIEA 2007), Harbin, China (May 23–25, 2007) pp. 1612–1617.Google Scholar
8.Foo, J., Knutzon, J., Oliver, J. and Winer, E., “Three-dimensional path planning of unmanned aerial vehicles using particle swarm optimization,” Proceedings of the 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference (AIAA 2006–6995) (Sep. 6–8, 2006).CrossRefGoogle Scholar
9.Mittal, S. and Deb, K., “Three-dimensional offline path planning for UAVs using multiobjective evolutionary algorithms,” In: Proceedings of the IEEE Congress on Evolutionary Computation, Singapore (Sep. 25–28, 2007) pp. 3195–3202.Google Scholar
10.Zhao, L. and Murthy, V. R., “Optimal flight path planner for an unmanned helicopter by evolutionary algorithms,” AIAA Guidance, Navigation and Control Conference and Exhibit, AIAA 2007–6741, South Carolina, USA (Aug. 20–23, 2007).Google Scholar
11.Allaire, F. C. J., Tarbouchi, M., Labonté, G. and Fusina, G., “FPGA implementation of genetic algorithm for UAV real-time path planning,” J. Intell. Robot. Syst. 54, 495–510 (2009).Google Scholar
12.Krenzke, T., “Ant Colony Optimization for Agile Motion Planning,” Master's Thesis (MIT, Cambridge, MA, 2006).Google Scholar
13.Fiorini, P. and Shiller, Z., “Motion planning in dynamic environments using velocity obstacles,” Int. J. Robot. Res. 17 (7), 760–772 (1998).CrossRefGoogle Scholar
14.Wang, Y., Lane, D. M. and Falconer, G. J., “Two novel approaches for unmanned underwater vehicle path planning: constrained optimisation and semi-infinite constrained optimization,” Robotica 18, 123–142 (2000).CrossRefGoogle Scholar
15.Zu, D., Han, J. and Tan, D., “Acceleration space LP for the path planning of dynamic target pursuit and obstacle avoidance,” In: Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, China (Jun. 21–23, 2006) pp. 9084–9088.Google Scholar
16.Richards, A. and How, J. P., “Aircraft trajectory planning with collision avoidance using mixed integer linear programming,” In: Proceedings of the American Control Conference, Alaska, USA (May 8–10, 2002) pp. 1936–1941.Google Scholar
17.Schouwenaars, T., How, J. and Feron, E., “Receding horizon path planning with implicit safety guarantees,” In: Proceedings of the American Control Conference, Boston, MA, USA (Jun 30–Jul 2, 2004) pp. 5576–5581.Google Scholar
18.Applegate, D., Cook, W., Dash, S. and Mevenkamp, M., QSopt Callable Library [online]. Available at: http://www2.isye.gatech.edu/~wcook/qsopt/ Accessed May 4, 2011.Google Scholar
19.Boyd, S. and Vandenberghe, L., Convex Optimization (Cambridge University Press, Cambridge, UK, 2004).CrossRefGoogle Scholar
20.MultiGen-Paradigm. Vega Prime Getting Started Guide, Version 2.0 (MultiGen-Paradigm, Inc, San Jose, CA, 2005).Google Scholar