A safety navigation method for integrating global path planning and local obstacle avoidance for self-driving cars in a dynamic environment

Document Type : Research Note

Authors

1 - School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an, Shaanxi 710048, China. - College of Applied Engineering, Henan University of Science and Technology, Sanmenxia, Henan, 472000, China

2 School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an, Shaanxi 710048, China

Abstract

In this paper，a novel method for obtaining high-quality paths for self-driving cars in underground parking lots is proposed. Self-driving cars require fast and accurate planning of collisionless path. When the self-driving car arrives at the parking lot, the car downloads the layout from the intelligent system of the parking lot and is assigned a parking space, then the location of the designated parking space and the car are provided by the intelligent system. A global path is planned by the global algorithm according to the location of the parking space and the car as well as the layout. If dynamic or unknown obstacles are detected in the process of moving along the global path, the parameters of obstacles can be estimated by the obstacle-detection algorithm. According to obtained parameters, the local obstacle avoidance path can be planned by the behavioral dynamics method. After completing obstacle avoidance, then the car will return to the global path and continue to move toward the target parking space. Finally, the proposed method is simulated by MATLAB, and the results show that the car can safely park in the target parking space. This method simultaneously satisfies the smooth and the real-time requirements of path planning.

Keywords

References

Reference:
[1] Li, A.J., Li, S.M. and Li, D.R. “On the trajectory planning's key technologies for intelligent vehicle”, Mechanical Science and Technology for Aerospace Engineering,32(7), pp.1022-1026(2013).
[2] Domokos, K. and Gábor, T. “Autonomous Path Planning for Road Vehicles in Narrow Environments: An Efficient Continuous Curvature Approach”, Journal of Advanced Transportation, 2017(2), pp.1-28(2017).
[3] Kim, J.M., Lim, K.I. and Kim, J.H. “Auto Parking Path Planning System Using Modified Reeds-Shepp Curve Algorithm”, 2014 11th International Conference on Ubiquitous Robots and Ambient Intelligence, pp.311-315(2014).
[4] Wang, G.Q., Tsuneo, N. and Akira, F. “Time-Varying Shortest Path Algorithm with Transit Time Tuning for Parking Lot Navigation”, TENCON 2015-2015 IEEE Region 10 Conference, pp.1-6(2015).
[5] Du, M.B., Mei, T. and Chen, J.J. “RRT-based Motion Planning Algorithm for Intelligent Vehicle in Complex Environments”, ROBOT, 37(4), pp. 443-450(2015).
[6] Dolgov, D., Thrun, S., Montemerlo, M., et al. “Path planning for autonomous vehicles in unknown semi-structured environments”, The International Journal of Robotics Research, 29(5), pp. 485–501(2010).
[7] Elbanhawi, M. and Simic, M. “Sampling-Based Robot Motion Planning: A Review”, IEEE Access, 2(1), pp. 56-77(2014).
[8] Likhachev, M. and Ferguson, D. “Planning Long Dynamically-Feasible Maneuvers for Autonomous Vehicles”, International Journal of Robotics Research (IJRR), 2009. 
[9] Kushleyev, A. and Likhachev, M. “Time-bounded lattice for efficient planning in dynamic environments”, 2009 IEEE International Conference on Robotics and Automation, pp. 1662–1668(2009).
[10] Guo, Q., Zhang, Z. and Xu, Y. “Path-planning of automated guided vehicle based on improved Dijkstra algorithm”, 2017 29th Chinese Control and Decision Conference (CCDC),2017.
[11] Dong, Y. and Camci, E. “Faster RRT-based Nonholonomic Path Planning in 2D Building Environments Using Skeleton-constrained Path Biasing”, Journal of intelligent & Robotic Systems, 89(3-4), pp. 387-401(2018).
[12] Otte, M. and Frazzoli, E. “RRTX: Asymptotically Optimal Single-Query Sampling-Based Motion Planning with Quick Re-planning”, International Journal of Robotics Research,35(7), pp.1-35(2015).
[13] Song, X.L., Zhou, N., Huang, Z.Y., et al. “An Improved RRT Algorithm of Local Path Planning for Vehicle Collision Avoidance”, Journal of Hunan University (Natural Science), 44(4), pp.30-37(2017).
[14] Jeon, J. H., Cowlagi, R.V., Peters, S.C., et al. “Optimal motion planning with the half-car dynamical model for autonomous high-speed driving”, 2013 American Control Conference, pp. 188–193(2013).
[15] Kuffner, J.J. and LaValle. S.M. “RRT-connect: An efficient approach to single-query path planning”, IEEE International Conference on Robotics & Automation, pp. 995-1001(2002).
[16] Fraichard, T. and Scheuer, A. “From Reeds and Shepp’s to continuous curvature paths”, IEEE Transactions on Robotics, 20(6), pp. 1025-1035(2004).
[17] Lau, B., Sprunk, C. and Burgard, W. “Kinodynamic motion planning for mobile robots using splines”, IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2427-2433(2009).
[18] Amiryan, J. and Jamzad, M. “Adaptive Motion Planning with Artificial Potential Fields Using a Prior Path”, The 3rd RSI International Conference on Robotics and Mechatronics, pp. 731-736(2015).
[19] Qureshi, A.H., Iqbal, K.F., Qamar, S.M., et al. “Potential Guided Directional-RRT* for Accelerated Motion Planning in Cluttered Environments”, 2013 IEEE International Conference on Mechatronics and Automation, pp. 519-524(2013).
[20] Qureshi, A.H., Mumtaz, S. and Iqbal, K.F. “Adaptive Potential Guided Directional-RRT*”, The IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1887-1892(2013).
[21] Jaillet, L., Hoffman, J. and Berg, J.V.D. “EG-RRT: Environment-Guided Random Trees for Kinodynamic Motion Planning with Uncertainty and Obstacles”, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2646-2652(2011).
[22] Sun, L.B., Liu, Y., Sun, J.Z., et al. “Path Planning Model Based on Mixed Perception Information”, Computer Engineering, 36(10), pp. 32-35(2010).
[23] Lv, W.X., Zhao, L.J., Wang, K., et al. “Efficient exploration of unknown environments with RRT-boundary constraint”, Hua Zhong Univ. of Sci. &Tech. (Natura Science Edition), 39, pp. 366-369(2011).
[24] Y, S.Q., Fu, W.P. and Li, D.X. “Application of Dynamic System Theory to Mobile Robot Navigation”, Mechanical Science and Technology for Aerospace Engineering, 29, pp. 100-104(2010).
[25] Y, S.Q., Fu, W.P., Li, D.X., et al. “Research on Application of Genetic Algorithm for Intelligent Mobile Robot Navigation Based on Dynamic Approach”, IEEE International Conference on Automation and Logistics, pp. 898-902(2007).
[26] Bicho, E., Mallet, P. and Schoner, G. “Using attractor dynamics to control autonomous vehicle motion”, Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Societh, pp. 1176-1181(1998).
[27] Fu, W.P., Zhang, P.F. and Yang, S.Q. “Behavioral dynamics of mobile robot and rolling windows algorithm to path planning”, Computer Engineering and Applications, 45, pp. 212-214(2009).
[28] Wang, W.Y., Fu, W.P., Wei, M.M., et al. “Behavior dynamics method for the motion planning of the end-effector of autonomous manipulator”, Journal of Xi’an University of Technology, 32(4), pp. 468-474(2016).
[29] Fu, W.P., Hao, D.P., Yang, S.Q., et al. “Study on the navigation method of behavior dynamics in mobile robot”, Mechanical Science and Technology for Aerospace Engineering, 32(10), pp. 1488-1491(2013).
[30] Han, G.N., Fu, W.P., Hao, D.P., et al. “Study on the Motion Planning Method of Intelligent Vehicle Based on the Behavior Dynamics”, Mechanical Science and Technology for Aerospace Engineering, 34(2), pp. 301-306(2015).
[31] Han, G.N., Fu, W.P. and Wang, W. “The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm”, Computational Intelligence and Neuroscience, pp.1-10(2016).
Scientia Iranica
Volume 28, Issue 3 - Serial Number 3
Transactions on Mechanical Engineering (B)
May and June 2021
Pages 1318-1328

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