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RimJump: Edge-based Shortest Path Planning for a 2D Map

Published online by Cambridge University Press:  29 November 2018

Zhuo Yao Open the ORCID record for Zhuo Yao [Opens in a new window] ,
Weimin Zhang ,
Yongliang Shi ,
Mingzhu Li ,
Zhenshuo Liang ,
Fangxing Li  and
Qiang Huang
Zhuo Yao
Affiliation:
School of Mechatronics, Beijing Institute of Technology, Beijing Advanced Innovation Center for Intelligent Robots and Systems, Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, 100081 Beijing, China E-mails: joe_yao@foxmail.com; ylshi@bit.edu.cn, limingzhu@bit.edu.cn, liangzs_bit@foxmail.com, wonk2000@bit.edu.cn, qhuang@bit.edu.cn
Weimin Zhang*
Affiliation:
School of Mechatronics, Beijing Institute of Technology, Beijing Advanced Innovation Center for Intelligent Robots and Systems, Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, 100081 Beijing, China E-mails: joe_yao@foxmail.com; ylshi@bit.edu.cn, limingzhu@bit.edu.cn, liangzs_bit@foxmail.com, wonk2000@bit.edu.cn, qhuang@bit.edu.cn
Yongliang Shi
Affiliation:
School of Mechatronics, Beijing Institute of Technology, Beijing Advanced Innovation Center for Intelligent Robots and Systems, Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, 100081 Beijing, China E-mails: joe_yao@foxmail.com; ylshi@bit.edu.cn, limingzhu@bit.edu.cn, liangzs_bit@foxmail.com, wonk2000@bit.edu.cn, qhuang@bit.edu.cn
Mingzhu Li
Affiliation:
School of Mechatronics, Beijing Institute of Technology, Beijing Advanced Innovation Center for Intelligent Robots and Systems, Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, 100081 Beijing, China E-mails: joe_yao@foxmail.com; ylshi@bit.edu.cn, limingzhu@bit.edu.cn, liangzs_bit@foxmail.com, wonk2000@bit.edu.cn, qhuang@bit.edu.cn
Zhenshuo Liang
Affiliation:
School of Mechatronics, Beijing Institute of Technology, Beijing Advanced Innovation Center for Intelligent Robots and Systems, Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, 100081 Beijing, China E-mails: joe_yao@foxmail.com; ylshi@bit.edu.cn, limingzhu@bit.edu.cn, liangzs_bit@foxmail.com, wonk2000@bit.edu.cn, qhuang@bit.edu.cn
Fangxing Li
Affiliation:
School of Mechatronics, Beijing Institute of Technology, Beijing Advanced Innovation Center for Intelligent Robots and Systems, Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, 100081 Beijing, China E-mails: joe_yao@foxmail.com; ylshi@bit.edu.cn, limingzhu@bit.edu.cn, liangzs_bit@foxmail.com, wonk2000@bit.edu.cn, qhuang@bit.edu.cn
Qiang Huang
Affiliation:
School of Mechatronics, Beijing Institute of Technology, Beijing Advanced Innovation Center for Intelligent Robots and Systems, Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, 100081 Beijing, China E-mails: joe_yao@foxmail.com; ylshi@bit.edu.cn, limingzhu@bit.edu.cn, liangzs_bit@foxmail.com, wonk2000@bit.edu.cn, qhuang@bit.edu.cn
*
*Corresponding author. E-mail: zhwm@bit.edu.cn
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Summary

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Path planning under 2D map is a key issue in robot applications. However, most related algorithms rely on point-by-point traversal. This causes them usually cannot find the strict shortest path, and their time cost increases dramatically as the map scale increases. So we proposed RimJump to solve the above problem, and it is a new path planning method that generates the strict shortest path for a 2D map. RimJump selects points on the edge of barriers to form the strict shortest path. Simulation and experimentation prove that RimJump meets the expected requirements.

Keywords

Path planningShortest pathRimJump
Type
Articles
Information
Robotica , Volume 37 , Issue 4 , April 2019 , pp. 641 - 655
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © Cambridge University Press 2018

References

Gong, W., Xie, X. and Liu, Y.-J., “Human experience-inspired path planning for robots,” Int. J. Adv. Robot. Syst. 15 (2018). doi: 10.1177/1729881418757046.CrossRefGoogle Scholar
Pereira, G. A. S., Pimenta, L. C. A., Chaimowicz, L., Fonseca, A. R., de Almeida, D. S. C., Corrêa, L. D. Q., Mesquita, R. C., Chaimowicz, L., de Almeida, D. S. C. and Campos, M. F. M., “Robot navigation in multi-terrain outdoor environments,” Int. J. Robot. Res. 28(6), 685–700 (2009).10.1177/0278364908097578CrossRefGoogle Scholar
Lavalle, S., Planning Algorithms (Cambridge University Press, New York, 2006).10.1017/CBO9780511546877CrossRefGoogle Scholar
Farsi, M., Ratcliff, K., Johnson, P. J., Allen, C. R., Karam, K. Z. and Pawson, R., “Robot control system for window cleaning,” Autom. Robot. Constr. XI 1, 617–623 (1994).Google Scholar
Sahar, G. and Hollerbach, J. M., “Planning a minimum-time trajectories for robot arms,” Int. J. Robot. Res. 5(3), 90–100 (1984).10.1177/027836498600500305CrossRefGoogle Scholar
Yap, P., Burch, N., Holte, R. C. and Schaeffer, J., “Any-angle path planning for computer games,” AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, Stanford, California, USA (AAAI Press 2011) pp. 201–207.Google Scholar
Nash, A., Koenig, S. and Tovey, C. A., “Lazy Theta*: Any-angle path planning and path length analysis in 3D,” Symposium on Combinatorial Search, SOCS, Stone Mountain, Atlanta, Georgia, USA, July DBLP (2010) pp. 299–307.Google Scholar
Rowe, N. C. and Richbourg, R. F., “An efficient Snell’s law method for optimal-path planning across multiple two-dimensional, irregular, homogeneous-cost regions,” Int. J. Robot. Res. 9(6), 48–66 (1990).10.1177/027836499000900605CrossRefGoogle Scholar
Likhachev, M., Ferguson, D., Gordon, G., Stentz, A. and Thrun, S., “Anytime dynamic A*: An anytime, replanning algorithm,” Fifteenth International Conference on International Conference on Automated Planning and Scheduling, Monterey, CA, USA (AAAI Press, 2005) pp. 262–271.Google Scholar
Stentz, A., “Optimal and efficient path planning for partially-known environments,” Proceedings of the IEEE International Conference on Robotics and Automation, San Diego, CA, USA, 2002, vol. 4 (IEEE, 1994) pp. 3310–3317.Google Scholar
Stentz, A., “The focussed D* algorithm for real-time replanning,” International Joint Conference on Artificial Intelligence, Montreal, Quebec, Canada (Morgan Kaufmann Publishers Inc., San Francisco, CA, 1995) pp. 1652–1659.Google Scholar
Nash, A., Daniel, K., Koenig, S. and Felner, A., “Theta*: Any-angle path planning on grids,” J. Artif. Intell. Res. 39(1), 533–579 (2014).Google Scholar
Jaillet, L., Cortés, J. and Siméon, T., “Sampling-based path planning on configuration-space costmaps,” IEEE Trans. Robot. 26(4), 635–646 (2010).10.1109/TRO.2010.2049527CrossRefGoogle Scholar
Thomas, S., Coleman, P. and Amato, N. M., “Reachable distance space: Efficient sampling-based planning for spatially constrained systems,” Int. J. Robot. Res. 29(7), 916–934 (2010).Google Scholar
Persson, S. M. and Sharf, I., “Sampling-based A* algorithm for robot path-planning,” Int. J. Robot. Res. 33(13), 1683–1708 (2014).10.1177/0278364914547786CrossRefGoogle Scholar
Sun, Z., Hsu, D., Jiang, T., Kurniawati, H. and Reif, J. H., “Narrow passage sampling for probabilistic roadmap planning,” IEEE Trans. Robot. 21(6), 1105–1115 (2005).Google Scholar
Choset, H., Kantor, G. A., Thrun, S., Principles of Robot Motion: Theory, Algorithms, and Implementations (MIT Press, Cambridge, MA, 2005).Google Scholar
Gambardella, L. M. and Dorigo, M., “Ant-Q: A reinforcement learning approach to the traveling Salesman problem,” Machine Learning, Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, California, USA, July DBLP (1995) pp. 252–260.Google Scholar
Liu, G., Peng, Y. and Hou, X., “The ant algorithm for solving robot path planning problem,” International Conference on Information Technology and Applications, Sydney, NSW, Australia (IEEE Computer Society, 2005) pp. 25–27.Google Scholar
Zhu, Q. B. and Zhang, Y., “An ant colony algorithm based on grid method for mobile robot path planning,” Robot 27(2), 132–136 (2005).Google Scholar
Vien, N. A., Viet, N. H., Lee, S. G. and Chung, T. C., “Obstacle avoidance path planning for mobile robot based on ant-Q reinforcement learning algorithm,” International Symposium on Neural Networks, Nanjing, China, vol. 4491 (Springer, Berlin, Heidelberg, 2007) Vol. 4491, pp. 704–713.Google Scholar
Zhou, J., Dai, G., He, D-Q., Ma, J. and Cai, X-Y., “Swarm intelligence: Ant-based robot path planning,” Fifth International Conference on Information Assurance and Security IEEE Computer Society, Xi’an, China (2009), pp. 459–463.Google Scholar
Brand, M., Masuda, M., Wehner, N. and Yu, X. H., “Ant colony optimization algorithm for robot path planning,” International Conference on Computer Design and Applications, Qinhuangdao, China, vol. 3 (IEEE, 2010) pp. V3-436–V3-440.Google Scholar
Nearchou, A. and Aspragathos, N. A., “A genetic path planning algorithm for redundant articulated robots,” Robotica 15(2), 213–224 (1997).10.1017/S0263574797000234CrossRefGoogle Scholar
Nearchou, A. C., “Path planning of a mobile robot using genetic heuristics,” Robotica 16(5), 575–588 (1998).10.1017/S0263574798000289CrossRefGoogle Scholar
Bohlin, R. and Kavraki, L. E., “Path planning using lazy PRM,” Proceedings of ICRA IEEE International Conference on Robotics and Automation, San Francisco, CA, USA, vol. 1 (IEEE, 2000) pp. 521–528.Google Scholar
Tu, J. and Yang, S. X., “Genetic algorithm based path planning for a mobile robot,” IEEE International Conference on Robotics and Automation, Taipei, Taiwan, vol. 1 (2003) pp. 1221–1226.Google Scholar
Sedighi, K. H., Ashenayi, K., Manikas, T. W., Wainwright, R. L. and Tai, H-M., “Autonomous local path planning for a mobile robot using a genetic algorithm,” IEEE Congress on Evolutionary Computation, Portland, OR, USA (2004).Google Scholar
Ahuactzin, J. M., Talbi, E. G., Bessière, P. and Mazer, E., “Using genetic algorithms for robot motion planning,” Selected Papers from the Workshop on Geometric Reasoning for Perception and Action, vol. 708 (Springer-Verlag, Berlin, Heidelberg, 2006) pp. 84–93.10.1007/3-540-57132-9_6CrossRefGoogle Scholar
Al-Taharwa, I., Sheta, A., and Weshah, M. A., “A mobile robot path planning using genetic algorithm in static environment,” J. Comput. Sci. 4(4), 341–344 (2008).10.3844/jcssp.2008.341.344CrossRefGoogle Scholar
Tsai, C.-C., Huang, H.-C. and Chan, C.-K., “Parallel elite genetic algorithm and its application to global path planning for autonomous robot navigation,” IEEE. T. Ind. Electron. 58(10), 4813–4821 (2011).10.1109/TIE.2011.2109332CrossRefGoogle Scholar
Khatib, O., “Real-time obstacle avoidance for manipulators and mobile robots,” Int. J. Robot. Res. 5(1), 90–98 (1986).10.1177/027836498600500106CrossRefGoogle Scholar
Francis, G., Ott, L. and Ramos, F., Stochastic functional gradient path planning in occupancy maps, Preprint arXiv:1705.05987 (2017).Google Scholar
Zhu, Q., Yan, Y. and Xing, Z., “Robot path planning based on artificial potential field approach with simulated annealing,” International Conference on Intelligent Systems Design and Applications, Jinan, China (IEEE, 2006), pp. 622–627.10.1109/ISDA.2006.253908CrossRefGoogle Scholar
Park, M. G. and Lee, M. C., “Experimental evaluation of robot path planning by artificial potential field approach with simulated annealing,” Proceedings of the, IEEE SICE Conference, Osaka, Japan, vol. 4 (2003) pp. 2190–2195.Google Scholar
Lee, M. C. and Park, M. G., “Artificial potential field based path planning for mobile robots using a virtual obstacle concept,” Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003), Kobe, Japan, Japan, Vol. 2 (IEEE, 2003) pp. 735–740.Google Scholar
Arribas, T., Gómez, M. and Sánchez, S., “Optimal motion planning based on CACM-RL using SLAM,” 44(8), 75–80 (2012).Google Scholar
Meziat, D., “Optimal motion planning by reinforcement learning in autonomous mobile vehicles,” Robotica 30(2), 159–170 (2012).Google Scholar
Lozano-Pérez, T. and Wesley, M. A., “An algorithm for planning collision-free paths among polyhedral obstacles,” Commun. ACM 22(10), 560–570 (1979).10.1145/359156.359164CrossRefGoogle Scholar
de Berg, M., Computational Geometry (Springer, Berlin, 2013).Google Scholar
Leven, P. and Hutchinson, S., “Toward real-rime path planning in changing environments,” In: Algorithmic and Computational Robotics: New Directions: The Fourth International Workshop on the Algorithmic Foundations of Robotics (Donald, B. R. et al., eds.) (A. K. Peters, Wellesley, MA, 2018) pp. 363–376.Google Scholar
De Berg, M., Van Kreveld, M., Overmars, M. and Schwarzkopf, O., “Computational geometry: Algorithms and applications,” Math. Gaz. 19(3), 333–334 (2008).Google Scholar
Lyle, N. and Tomasz, P., “Geometry for robot path planning,” Robotica 25(6), 691–701 (2007).Google Scholar
Michael, H., Matveev, A. S. and Savkin, A. V., “Algorithms for collision-free navigation of mobile robots in complex cluttered environments: A survey,” Robotica 33(3), 463–497 (2015).Google Scholar

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