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Optimal Path Planning Satisfying Complex Task Requirement in Uncertain Environment

Published online by Cambridge University Press:  08 April 2019

Xin-Yi Yu ,
Zhen-Yong Fan ,
Lin-Lin Ou ,
Feng Zhu  and
Yong-Kui Guo
Xin-Yi Yu
Affiliation:
Department of Automation, College of Information Engineering, Zhejiang University of Technology, Zhejiang Province, Hangzhou, China E-mails: [email protected], [email protected], [email protected], [email protected]
Zhen-Yong Fan
Affiliation:
Department of Automation, College of Information Engineering, Zhejiang University of Technology, Zhejiang Province, Hangzhou, China E-mails: [email protected], [email protected], [email protected], [email protected]
Lin-Lin Ou*
Affiliation:
Department of Automation, College of Information Engineering, Zhejiang University of Technology, Zhejiang Province, Hangzhou, China E-mails: [email protected], [email protected], [email protected], [email protected]
Feng Zhu
Affiliation:
Department of Automation, College of Information Engineering, Zhejiang University of Technology, Zhejiang Province, Hangzhou, China E-mails: [email protected], [email protected], [email protected], [email protected]
Yong-Kui Guo
Affiliation:
Department of Automation, College of Information Engineering, Zhejiang University of Technology, Zhejiang Province, Hangzhou, China E-mails: [email protected], [email protected], [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

Robots often need to accomplish some complex tasks such as surveillance, response and obstacle avoidance. In this paper, a dynamic search method is proposed to generate optimal robot trajectories satisfying complex task requirement in uncertain environment. The LTL-A* algorithm is presented to generate a global optimal path and the A* algorithm is provided to modify the global optimal path. The task is specified by a linear temporal logic (LTL) formula, and a weighted transition system according to the known information in uncertain environment is modeled to describe the robot motion. Subsequently, a product automaton is constructed by combining the transition system with the task requirement. Based on the product automaton, the LTL-A* algorithm is proposed to generate a global optimal path. The local path planning based on the A* algorithm is employed to deal with the environment change during the process of tracking the global optimal path for the robot. The results of the simulation and experiments show that the proposed method can not only meet the complex task requirement in uncertain environment but also improve the search efficiency.

Keywords

Path planningComplex tasksLTLA* algorithmUncertain environment
Type
Articles
Information
Robotica , Volume 37 , Issue 11 , November 2019 , pp. 1956 - 1970
Copyright
© Cambridge University Press 2019 

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References

Xu, B. G., Yi, Y. X. and Zhou, M. J., “Current status and prospects of intelligent mobile robot technology,” Rob. Tech. Appl. 2(2), 29–34 (2007).Google Scholar
Chabini, I. and Lan, S., “Adaptations of the A* algorithm for the computation of fastest paths in deterministic discrete-time dynamic networks,” Trans. Intell. Transp. Syst. 3(1), 60–74 (2002).CrossRefGoogle Scholar
Xu, Y. and Liu, R., “Path planning for mobile articulated robots based on the improved A* algorithm,” Int. J. Adv. Rob. Syst. 14(4), 1–10 (2017).Google Scholar
Tu, J. and Yang, S. X., “Genetic algorithm based path planning for a mobile robot,” International Conference on Robotics and Automation, Taipei, Taiwan (2003), pp. 1221–1226.Google Scholar
Shi, Y., “Particle swarm optimization: Developments, applications and resources,” Int. Conf. Evol. Comput. 1, 81–86 (2001).Google Scholar
Kennedy, J., Particle Swarm Optimization. Encyclopedia of Machine Learning (Springer, Perth, Australia, 2011), pp. 760–766.Google Scholar
Fan, X., Luo, X., Yi, S., Yang, S. and Zhang, H., “Optimal path planning for mobile robots based on intensified ant colony optimization algorithm,” International Conference on Robotics, Intelligent Systems and Signal, Changsha, China (2003) pp. 131–136.Google Scholar
Xu, W. L., Tso, S. K. and Fung, Y. H., “Sensor-based reactive navigation of a mobile robot through local target switching,” International Conference on Advanced Robotics, Monterey, CA, USA (1997), pp. 361–366.Google Scholar
Stentz, A., “The focussed D* algorithm for real-time replanning,” International Joint Conference on Artificial Intelligence, Montreal, Quebec, Canada (1995) pp. 1652–1659.Google Scholar
Qureshi, A. H. and Ayaz, Y., “Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments,” Rob. Autonom. Syst. 68, 1–11 (2015).CrossRefGoogle Scholar
Zhang, B. T., Liu, Y., Lu, Q. and Wang, J., “A path planning strategy for searching the most reliable path in uncertain environments,” Int. J. Adv. Rob. Syst. 13(5), 1–9 (2016).Google Scholar
Wei, C. Y. and Ni, F. S., “Tabu temporal difference learning for robot path planning in uncertain environments,” Lect. Notes Comput. Sci. 123–134 (Springer, Cham, 2018).Google Scholar
Deepak, N. S., Wei, Q. T. and Pierre, F. J., “Stochastic time-optimal path-planning in uncertain, strong, and dynamic flows,” Comput. Methods Appl. Mech. Eng. 333, 218–237 (2018).Google Scholar
Shlomi, H., Shraga, S. and Nir, S., “Applying probability navigation function in dynamic uncertain environments,” Rob. Autonom. Syst. 87, 237–246 (2017).Google Scholar
Bacchus, F. and Kabanza, F., “Planning for temporally extended goals,” Ann. Math. Artif. Intell. 22, 5–27 (1998).CrossRefGoogle Scholar
Xinyi, Y., Hao, C., Yongkui, G., Cheng, C., Linlin, O. and Li, Y., “Path planing of warehouse robots based on linear temporal logic,” Chin Hig. Technol. Lett. 26(1), 16–23 (2016).Google Scholar
Xiao, Y. T., Lin-Lin, O. and Li, Y., “Optimal patrolling path planning via linear temporal logic,” Acta Autom. Sin. 40(10), 2126–2133 (2014).Google Scholar
Guo, M., Johansson, K. H. and Dimarogonas, D. V., “Motion and action planning under LTL specifications using navigation functions and action description language,” International Conference on Intelligent Robots and Systems, Tokyo, Japan (2013) pp. 240–245.Google Scholar
Leahy, K. J., Aksaray, D. and Belta, C., “Informative path planning under temporal logic constraints with performance guarantees,” American Control Conference, Seattle, WA, USA (2017) pp. 1859–1865.Google Scholar
Kloetzer, M. and Mahulea, C., “LTL planning in dynamic environments,” Int. Fed. Accountants 45, 294–300 (2012).Google Scholar
Ayala, A. M., Andersson, S. B. and Belta, C., “Temporal logic motion planning in unknown environments,” International Conference on Intelligent Robots and Systems, Tokyo, Japan (2013) pp. 5279–5284.Google Scholar
Xu, N., Li, J., Niu, Y. and Shen, L., “An LTL-based motion and action dynamic planning method for autonomous robot,” Int. Fed. Accountants. Ifac PapersOnLine. 49, 91–96 (2016).CrossRefGoogle Scholar
Wang, H., Yu, Y. and Yuan, Q., “Application of Dijkstra algorithm in robot path-planning,” Second International Conference on Mechanic Automation and Control Engineering, Inner Mongolia, China (2011) pp. 1067–1069.CrossRefGoogle Scholar
Sheng-qun, C. and Lin-fei, D., “Applied analysis of Dijkstra algorithm and A-star algorithm in the intelligent guiding,” J. Chongqing Univ. Sci. Technol. (Natural Sciences Edition) (2010), pp. 1673–1980.Google Scholar
Nannicini, G., Delling, D., Liberti, L. and Schultes, D., “Bidirectional A Search for time-dependent fast paths,” J. Am. Chem. Soc. 136(24), 8626–8641 (2014).Google Scholar
Soltani, R., Tawfik, H., Goulermas, J. Y. and Fernando, T., “Path planning in construction sites: Performance evaluation of the Dijkstra, A *, and GA search algorithms,” Adv. Eng. Inf. 16, 291–303 (2002).CrossRefGoogle Scholar
Yao, J., Lin, C., Xie, X. and Wang, A. J., “Path planning for virtual human motion using improved A* star algorithm,” Seventh International Conference on Information Technology, Las Vegas, NV, USA (2010), pp. 53–58.Google Scholar
Smith, S. L., Tůmová, J., Belta, C. and Rus, D., “Optimal path planning under temporal logic constraints,” International Conference on Intelligent Robots and Systems, Taipei, Taiwan (2010) pp. 3288–3293.Google Scholar
Emerson, E. A., “Temporal and modal logic,” Handb. Theor. Comput. Sci. 995–1072 (Elsevier, Amsterdam, Netherlands, 1990).Google Scholar
Gastin, P. and Oddoux, D., “Fast LTL to Büchi automata translation,” International Conference on Computer Aided Verification, Paris, France (2001) pp. 53–65.CrossRefGoogle Scholar
Smith, S. L., Tůmová, J., Belta, C. and Rus, D., “Optimal path planning for surveillance with temporal-logic constraints,” Int. J. Rob. Res. 30(14), 1695–1708 (2011).CrossRefGoogle Scholar
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