Hostname: page-component-89b8bd64d-n8gtw Total loading time: 0 Render date: 2026-05-09T08:19:47.636Z Has data issue: false hasContentIssue false
  • English
  • Français

Constrained coverage path planning: evolutionary and classical approaches

Published online by Cambridge University Press:  21 February 2018

S. M. Ahmadi ,
H. Kebriaei  and
H. Moradi
S. M. Ahmadi*
Affiliation:
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
H. Kebriaei*
Affiliation:
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
H. Moradi*
Affiliation:
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran Intelligent Systems Research Institute, SKKU, Suwon, South Korea
*
E-mail: s.m.ahmadi@ut.ac.ir
*Corresponding author. E-mail: kebriaei@ut.ac.ir
E-mail: moradih@ut.ac.ir
Get access Rights & Permissions [Opens in a new window]

Summary

The constrained coverage path planning addressed in this paper refers to finding an optimal path traversed by a unmanned aerial vehicle (UAV) to maximize its coverage on a designated area, considering the time limit and the feasibility of the path. The UAV starts from its current position to assess the condition of a new entry to the area. Nevertheless, the UAV needs to comply with the coverage task, simultaneously and therefore, it is likely that the optimal policy would not be the shortest path in such a condition, since a wider area can be covered through a longer path. From the other side, along with a longer path, the UAV may not reach to the target in due time. In addition, the speed of UAV is assumed to be constant and as a result, a feasible path needs to be smooth enough to support this assumption. The problem is modeled as an Epsilon-constraint optimization in which a coverage function has to be maximized, considering the constraints on the length and the smoothness of the path. For this purpose, a new genetic path planning algorithm with adaptive operator selection is proposed to solve such a complicated constrained optimization problem. The proposed approach has been compared to some classical approaches like, a modified version of the Artificial Potential Field and a modified version of Dijkstra's algorithm (a graph-based approach). All the methods are implemented and tested in different scenarios and their performances are evaluated via the simulation results.

Keywords

CoveragePath planningUnmanned aerial vehicleGenetic algorithmArtificial potential fieldDijkstra's algorithm

Information

Type
Articles
Information
Robotica , Volume 36 , Issue 6 , June 2018 , pp. 904 - 924
Copyright
Copyright © Cambridge University Press 2018 

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.)

Article purchase

Temporarily unavailable

References

1. Jia, D., Wermelinger, M., Diethelm, R., Krüsi, P. and Hutter, M., “Coverage Path Planning for Legged Robots in Unknown Environments,” Proceedings of the 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), Lausanne (2016) pp. 68–73.Google Scholar
2. Bircher, A. et al., “Three-dimensional coverage path planning via viewpoint resampling and tour optimization for aerial robots,” Auton. Robots 40 (6), 1059–1078 (2016).Google Scholar
3. Barraquand, J. and Latombe, J. C., “Robot motion planning: A distributed representation approach,” Int. J. Rob. Res. 10, 628–649 (1991).Google Scholar
4. Chen, C., “Path planning in distorted configuration space,” Robotica 35 (7), 1585–1597 (2017).Google Scholar
5. Khatib, O., “Real-time obstacle avoidance for manipulators and mobile robots,” Int. J. Rob. Res. 5, 90–98 (1986).Google Scholar
6. Koren, Y. and Borenstein, J., “Potential Field Methods and Their Inherent Limitations for Mobile Robot Navigation,” Proceedings of the IEEE International Conference on Robotics and Automation (1991) pp. 1398–1404.Google Scholar
7. Ren, J., McIsaac, K. and Patel, R. V., “Modified Newton's method applied to potential field-based navigation for mobile robots,” IEEE Trans. Robot. 22, 384–391 (2006).Google Scholar
8. Xiao, B., Yu, L., Li, S. and Chen, R., “Research of escaping local minima strategy for artificial potential field,” J. Syst. Simul. 19, 4495–4503 (2007).Google Scholar
9. Al-Sultan, K. S. and Aliyu, M. D. S., “A new potential field-based algorithm for path planning,” J. Intell. Robot. Syst. 17, 265–282 (1996).Google Scholar
10. Lee, J., Nam, Y., Hong, S. and Cho, W., “New potential functions with random force algorithms using potential field method,” J. Intell. Robot. Syst. 66, 303–319 (2012).Google Scholar
11. Zhu, Y., Zhang, T. and Song, J., “An Improved Wall Following Method for Escaping from Local Minimum in Artificial Potential Field Based Path Planning,” Proceedings of the 48th IEEE Conference on Decision and Control. Held Jointly with 28th Chinese Control Conference CDC/CCC 2009 (2009) pp. 6017–6022.Google Scholar
12. Raja, R., Dutta, A. and Venkatesh, K. S., “New potential field method for rough terrain path planning using genetic algorithm for a 6-wheel rover,” Robot. Auton. Syst. 72, 295–306 (2015).Google Scholar
13. Simon, J. and Martinovic, G., “Navigation of mobile robots using WSN's RSSI parameter and potential field method,” Acta Polytech. Hung. 10, 107–118 (2013).Google Scholar
14. Yin, L., Yin, Y. and Lin, C., “A new potential field method for mobile robot path planning in the dynamic environments,” Asian J. Control 11, 214–225 (2009).CrossRefGoogle Scholar
15. Vadakkepat, P., Chen Tan, K. and Ming-Liang, W., “Evolutionary Artificial Potential Fields and Their Application in Real Time Robot Path Planning,” Proceedings of the 2000 IEEE Congress on Evolutionary Computation (2000) pp. 256–263.Google Scholar
16. Dijkstra, E. W., “A note on two problems in connexion with graphs,” Numer. Math. 1, 269–271 (1959).Google Scholar
17. Zhang, Z. and Zhao, Z., “A multiple mobile robots path planning algorithm based on A-star and Dijkstra algorithm,” Int. J. Smart Home 8, 75–86 (2014).Google Scholar
18. Wang, C., Wang, L., Qin, J., Wu, Z., Duan, L., Li, Z., Cao, M., Ou, X., Su, X. and Li, W., “Path Planning of Automated Guided Vehicles Based on Improved A-Star Algorithm,” Proceedings of the IEEE International Conference on Information and Automation (2015) pp. 2071–2076.Google Scholar
19. Pamosoaji, A. K. and Hong, K. S., “A path-planning algorithm using vector potential functions in triangular regions,” IEEE Trans. Syst. Man, Cybern. 43, 832–842 (2013).Google Scholar
20. Xiao, J., Michalewicz, Z., Zhang, L. and Trojanowski, K., “Adaptive evolutionary planner/navigator for mobile robots,” IEEE Trans. Evol. Comput. 1, 18–28 (1997).CrossRefGoogle Scholar
21. Roberge, V., Tarbouchi, M. and Labonté, G., “Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning,” IEEE Trans. Ind. Inform. 9, 132–141 (2013).Google Scholar
22. Yen, J., “A fuzzy logic based extension to Payton and Rosenblatt's command fusion method for mobile robot navigation,” IEEE Trans. Syst. Man, Cybern. 25, 971–978 (1995).Google Scholar
23. Qu, H., Yang, S. X., Willms, A. R. and Yi, Z., “Real-time robot path planning based on a modified pulse-coupled neural network model,” IEEE Trans. Neural Netw. 20, 1724–1739 (2009).Google Scholar
24. Moharam, R. and Morsy, E., “Genetic algorithms to balanced tree structures in graphs,” Swarm Evol. Comput. 32, 132–136 (2016).Google Scholar
25. Hu, Y. and Yang, S. X., “A Knowledge Based Genetic Algorithm for Path Planning of a Mobile Robot,” Proceedings of the IEEE International Conference on Robotics and Automation ICRA2004. (2004) pp. 4350–4355.Google Scholar
26. Rathbun, D., Kragelund, S., Pongpunwattana, A. and Capozzi, B., “An Evolution Based Path Planning Algorithm for Autonomous Motion of a UAV Through Uncertain Environments,” Proceedings of the IEEE 21st Digital Avionics Systems Conference (2002) vol. 2, pp. 8D2-1-8D2-12.Google Scholar
27. Li, Q., Zhang, W., Yin, Y., Wang, Z. and Liu, G., “An Improved Genetic Algorithm of Optimum Path Planning for Mobile Robots,” Proceedings of the 6th International Conference on IEEE Intelligent Systems Design and Applications ISDA2006 (2006) pp. 637–642.Google Scholar
28. Hasircioglu, I., Topcuoglu, H. R. and Ermis, M., “3-D Path Planning for the Navigation of Unmanned Aerial Vehicles by Using Evolutionary Algorithms,” Proceedings of the 10th Annual Conference Genetic and Evolutionary Computation (2008) pp. 1499–1506.Google Scholar
29. Ataei, M. and Yousefi-Koma, A., “Three-dimensional optimal path planning for waypoint guidance of an autonomous underwater vehicle,” Robot. Auton. Syst. 67, 23–32 (2015). doi:10.1016/j.robot.2014.10.007.Google Scholar
30. Ahmed, F. and Deb, K., “Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms,” Soft Comput. 17, 1283–1299 (2013).Google Scholar
31. Choset, H., Acar, E., Rizzi, A. and Luntz, J., “Exact Cellular Decompositions in Terms of Critical Points of Morse Functions,” Proceedings of the IEEE International Conference on Robotics and Automation, ICRA2000 (2000) pp. 2270–2277.Google Scholar
32. Semsch, E., Jakob, M., Pavlíček, D. and Pěchouček, M., “Autonomous UAV Surveillance in Complex Urban Environments,” Proceedings of the IEEE/WIC/ACM International Jt. Conference, IET, Web Intelligence and Intelligent Agent Technologies WI-IAT2009 (2009) pp. 82–85.Google Scholar
33. Huang, W. H., “Optimal Line-Sweep-Based Decompositions for Coverage Algorithms,” Proceedings of the IEEE International Conference on Robotics and Automation ICRA2001 (2001) pp. 27–32.Google Scholar
34. DeLima, P. and Pack, D., “Maximizing Search Coverage Using Future Path Projection for Cooperative Multiple UAVs with Limited Communication Ranges,” In: Optimization and Cooperative Control Strategies (Hirsch, M., Commander, C. W., Pardalos, P. M. and Murphey, R., eds.) (Springer, Berlin, Heidelberg, 2009) pp. 103–117.Google Scholar
35. Robin, C. and Lacroix, S., “Multi-robot target detection and tracking: Taxonomy and survey,” Auton. Robots 1–32 (2015).Google Scholar
36. Holland, J. H., Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence (MIT Press, Cambridge, MA, 1992).Google Scholar
37. Davis, L., “Adapting Operator Probabilities in Genetic Algorithms,” Proceedings of the 3rd International Conference on Genetics, Algorithms (1989) pp. 61–69.Google Scholar
38. Deb, K., Multi-Objective Optimization Using Evolutionary Algorithms, vol. 16 (John Wiley & Sons, Hoboken, New Jersey, 2001).Google Scholar
39. Zhou, A., Qu, B. Y., Li, H., Zhao, S. Z., Suganthan, P. N. and Zhang, Q., “Multiobjective evolutionary algorithms: A survey of the state of the art,” Swarm Evol. Comput. 1, 32–49 (2011).Google Scholar
40. Ahmadi, S. M., Kebriaei, H. and Moradi, H., “Adaptive Operator Selection for Path Planning in Static Environments,” Proceedings of the IEEE SAI Intelligent Systems Conference IntelliSys2015 (2015) pp. 285–289.Google Scholar
41. Zhang, R., Zheng, C. and Yan, P., “Route Planning for Unmanned Air Vehicles with Multiple Missions Using an Evolutionary Algorithm,” Proceedings of the 3rd International Conference on Natural Computation IEEE ICNC2007 (2007) pp. 23–28.Google Scholar