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Path planning in distorted configuration space

Published online by Cambridge University Press:  30 May 2016

Chao Chen
Chao Chen*
Affiliation:
Department of Mechanical and Aerospace Engineering, Monash University, Melbourne, Victoria, Australia
*
*Corresponding author. E-mail: chao.chen@monash.edu
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Summary

An effective algorithm for path planning is introduced based on a novel concept, the distorted configuration space (DC-space), where all obstacles deform into dimensionless geometric objects. Path planning in this space can be conducted by simply connecting the starting position and ending position with a straight line. This linear path in the DC-space is then mapped back into a feasible in the original C-space. The advantage of this approach is that no trial-and-error or iteration is needed while a feasible path can be found directly if it exists. An algorithm with general formulas is derived. Examples in 2D and 3D are provided to validate this concept and algorithm.

Keywords

Path planningAutomationMotion planningNavigationMobile robots

Information

Type
Articles
Information
Robotica , Volume 35 , Issue 7 , July 2017 , pp. 1585 - 1597
Copyright
Copyright © Cambridge University Press 2016 

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References

1. Ellekilde, L. P. and Petersen, H. G., “Motion planning efficient trajectories for industrial bin-picking,” Int. J. Robot. Res. 32 (9–10), 991–1004 (2013).CrossRefGoogle Scholar
2. Srinivasa, S. S., Berenson, D., Cakmak, M., Collet, A., Dogar, M. R., Dragan, A. D., Knepper, R. A., Niemueller, T., Strabala, K., Weghe, J. M. Vande and Ziegler, J., “Herb 2.0: Lessons learned from developing a mobile manipulator for the home,” Proc. Ieee 100 (8), 2410–2428 (2012).CrossRefGoogle Scholar
3. Yang, K., Gan, S. K. and Sukkarieh, S., “A gaussian process-based rrt planner for the exploration of an unknown and cluttered environment with a uav,” Adv. Robot. 27 (6), 431–443 (2013).CrossRefGoogle Scholar
4. Yoon, J. and Crane, C. D., “Path planning for unmanned ground vehicle in urban parking area,” Proceedings of the 11th International Conference on Control, Automation and Systems (Iccas), Gyeonggi-do, Republic of Korea (2011) pp. 887–892.Google Scholar
5. Kuwata, Y., Teo, J., Fiore, G., Karaman, S., Frazzoli, E. and How, J. P., “Real-time motion planning with applications to autonomous urban driving,” Ieee Trans. Control Syst. Technol. 17 (5), 1105–1118 (2009).CrossRefGoogle Scholar
6. Latombe, J. C., “Motion planning: A journey of robots, molecules, digital actors, and other artifacts,” Int. J. Robot. Res. 18 (11), 1119–1128 (1999).CrossRefGoogle Scholar
7. Elbanhawi, M. and Simic, M., “Sampling-based robot motion planning: A review,” IEEE Access 2, 56–77 (2014).CrossRefGoogle Scholar
8. Maekawa, T., Noda, T., Tamura, S., Ozaki, T. and Machida, K., “Curvature continuous path generation for autonomous vehicle using b-spline curves,” Comput.-Aided Des. 42 (4), 350–359 (2010).CrossRefGoogle Scholar
9. Takahashi, O. and Schilling, R. J., “Motion planning in a plane using generalized voronoi diagrams,” Ieee Trans. Robot. Autom. 5 (2), 143–150 (1989).CrossRefGoogle Scholar
10. Jan, G. E., Sun, C. C., Tsai, W. C. and Lin, T. H., “An o(n log n) shortest path algorithm based on delaunay triangulation,” Ieee-Asme Trans. Mechatronics 19 (2), 660–666 (2014).CrossRefGoogle Scholar
11. Brooks, R. A. and Lozanoperez, T., “A subdivision algorithm in configuration space for findpath with rotation,” Ieee Trans. Syst. Man and Cybern. 15 (2), 224–233 (1985).CrossRefGoogle Scholar
12. Dijkstra, E. W., “A note on two problems in connexion with graphs,” Numerische Mathematik 1, 269–271 (1959).CrossRefGoogle Scholar
13. Hart, P. E., Nilsson, N. J. and Raphael, B., “A formal basis for the heuristic determination of minimum cost paths,” IEEE Trans. Syst. Sci. Cybern. 4, 100–107 (1968).CrossRefGoogle Scholar
14. Seraji, H. and Howard, A., “Behavior-based robot navigation on challenging terrain: A fuzzy logic approach,” Ieee Trans. Robot. Autom. 18 (3), 308–321 (2002).CrossRefGoogle Scholar
15. Beom, H. R. and Cho, H. S., “A sensor-based navigation for a mobile robot using fuzzy-logic and reinforcement learning,” Ieee Trans. Syst. Man Cybern. 25 (3), 464–477 (1995).CrossRefGoogle Scholar
16. Hu, Y. R., Yang, S. X., Xu, L. Z. and Meng, M. Q. H., “A knowledge based genetic algorithm for path planning in unstructured mobile robot environments,” Proceedings of the IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO, Shenyang, China (2004) pp. 767–772.Google Scholar
17. Garcia, M. A. P., Montiel, O., Castillo, O., Sepulveda, R. and Melin, P., “Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation,” Appl. Soft Comput. 9 (3), 1102–1110 (2009).CrossRefGoogle Scholar
18. LaValle, S. M., Planning Algorithms (Cambridge Univ. Press, Cambridge, 2006).CrossRefGoogle Scholar
19. Khatib, O., “Real-time obstacle avoidance for manipulators and mobile robots,” Int. J. Robot. Res. 5 (1), 90–98 (1986).Google Scholar
20. Koren, Y. and Borenstein, J., “Potential-field methods and their inherent limitations for mobile robot navigation,” Ieee International Conference on Robotics and Automation, Sacramento, CA (vols. 1–3, 1991) pp. 1398–1404.Google Scholar
21. Chen, Z. J. Dong, Z. N., Zhang, R. L. and Zhou, R., “Study on uav path planning approach based on fuzzy virtual force,” Chin. J. Aeronautics 23, 341–350 (2010).Google Scholar
22. Sun, L. R. et al., “Mobile robot real-time path planning based on virtual targets method,” 3rd International Conference on Measuring Technology and Mechatronics Automation, Shangshai, China (2011) pp. 568–572.Google Scholar
23. Kavraki, L. E., Svestka, P., Latombe, J. C. and Overmars, M. H., “Probabilistic roadmaps for path planning in high-dimensional configuration spaces,” Ieee Trans. Robot. Autom. 12 (4), 566–580 (1996).CrossRefGoogle Scholar
24. Kavraki, L. and Latombe, J. C., “Randomized preprocessing of configuration space for path planning - articulated robots,” Iros '94 - Intelligent Robots and Systems: Advanced Robotic Systems and the Real World, Munich, Germany (vols. 1–3, 1994) pp. 1764–1771.Google Scholar
25. LaValle, S. M. and Kuffner, J. J., “Rapidly-exploring random trees: Progress and prospects,” Algorithmic and Computational Robotics: New Directions, Natick, Massachusetts (2001) pp. 293–308.Google Scholar
26. Hsu, D., Kindel, R., Latombe, J. C. and Rock, S., “Randomized kinodynamic motion planning with moving obstacles,” Int. J. Robot. Res. 21 (3), 233–255 (2002).CrossRefGoogle Scholar
27. Barraquand, J. and Latombe, J. C., “Robot motion planning-a distributed representation approach,” Int. J. Robot. Res. 10 (6), 628–649 (1991).CrossRefGoogle Scholar
28. Schwartz, J. T. and Sharir, M., “A survey of motion planning and related geometric algorithms,” Artificial Intell. 37 (1–3), 157–169 (1988).CrossRefGoogle Scholar
29. Hsu, D., Latombe, J. C. and Kurniawati, H., “On the probabilistic foundations of probabilistic roadmap planning,” Int. J. Robot. Res. 25, 627–643 (Jul. 2006).CrossRefGoogle Scholar
30. Amato, N. M. and Wu, Y., “A randomized roadmap method for path and manipulation planning,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Minneapolis, MN (vol. 1, Apr. 1996) pp. 113–120.Google Scholar
31. Boor, V., Overmars, M. H. and van der Stappen, A. F., “The gaussian sampling strategy for probabilistic roadmap planners,” Proceedings of the 1996, IEEE International Conference on Robotics and Automation (ICRA), Detroit, MI (vol. 2, May, 1999) pp. 1018–1023.Google Scholar
32. Sun, Z., Hsu, D., Jiang, T. T., Kurniawati, H. and Reif, J. H., “Narrow passage sampling for probabilistic roadmap planning,” IEEE Trans. Robot. 21, 1105–1115 (Dec. 2005).Google Scholar
33. Thomas, S., Morales, M., Tang, X. Y. and Amato, N. M., “Biasing samplers to improve motion planning performance,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Roma, Italy (Apr. 2007) pp. 1625–1630.Google Scholar
34. Simeon, T., Laumond, J. P. and Nissoux, C., “Visibility-based probabilistic roadmaps for motion planning,” Adv. Robot. 14, 477–493 (2000).CrossRefGoogle Scholar
35. Mohamed, E. and Milan, S., “Sampling-based robot motion planning: A review,” IEEE Access 2, 56–77 (Jan. 2014).Google Scholar
36. Hauser, K., Bretl, T. and Latombe, J. C., “Learning-assisted multi-step planning,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Barcelona, Spain (Apr. 2005) pp. 4575–4580.Google Scholar
37. Edge, W. L., The Theory of Ruled Surfaces (Cambridge Univ. Press, London, 1931).Google Scholar