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Sampling-based Non-Holonomic Path Generation for Self-driving Cars

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Abstract

Semi-autonomous self driving car technologies are commercially available today, but making them fully autonomous under guaranteed safety is still an open challenge. There are holonomic robot path planning algorithms for generating guaranteed collision-free paths even in very complex but completely known environments. However, it is not feasible to apply these algorithms to non-holonomic cars. In this paper we propose a novel approach to solve this problem in real-time, by generating a series of incremental collision-free path segments from the start to end configuration using an incremental sampling-based planner, such as Rapidly-exploring Random Trees (RRT). The proposed approach employs only local sensor data (point cloud data) for path planning of nonholonomic self driving cars in quasi static unknown environments. The proposed planner generates real-time paths that guarantee safety. The algorithms are extensively testedand validated in popular benchmark simulation environments.

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References

  1. Adiyatov, O., Varol, H.: Rapidly-Exploring Random Tree Based Memory Efficient Motion Planning. In: IEEE International Conference on Mechatronics and Automation (ICMA), pp. 354–359 (2013)

  2. Adiyatov, O., Varol, H.: A Novel Rrt*-Based Algorithm for Motion Planning in Dynamic Environments. In: 2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017, pp. 1416–1421. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICMA.2017.8016024 (2017)

  3. Badue, C., Guidolini, R., Carneiro, R.V., Azevedo, P., Cardoso, V.B., Forechi, A., Jesus, L.F.R., Berriel, R.F., Paixão, T.M., Mutz, F.W., Oliveira-Santos, T., de Souza, A.F.: Self-driving cars: A survey. CoRR arXiv:1901.04407 (2019)

  4. Balakirsky, S., Dimitrov, D.: Single-Query, Bi-Directional, Lazy Roadmap Planner Applied to Car-Like Robots. In: 2010 IEEE International Conference on Robotics and Automation, pp. 5015–5020. https://doi.org/10.1109/ROBOT.2010.5509583 (2010)

  5. Balakirsky, S., Dimitrov, D.: Single-Query, Bi-Directional, Lazy Roadmap Planner Applied to Car-Like Robots. In: IEEE International Conference on Robotics and Automation, pp. 5015–5020 (2010)

  6. Baruch, J.: Steer driverless cars towards full automation. Nature 536(7615), 127–127 (2016)

    Article  Google Scholar 

  7. Bellotto, N., Hu, H.: Multisensor-based human detection and tracking for mobile service robots. IEEE Trans. Syst. Man Cybern. – Part B 39(1), 167–181 (2009)

    Article  Google Scholar 

  8. Bennewitz, M., Burgard, W., Cielniak, G., Thrun, S.: Learning Motion Patterns of People for Compliant Robot Motion. Intl. J. Robot. Res. 24(1), 31–48 (2005)

    Article  Google Scholar 

  9. van den Berg, J., Ferguson, D., Kuffner, J.: Anytime Path Planning and Replanning in Dynamic Environments. In: IEEE Intl. Conf. on Robotics and Automation, pp. 2366–2371 (2006)

  10. Bradley, D., Unnikrishnan, R., Bagnell, J. A.: Vegetation Detection for Driving in Complex Environments. In: IEEE Intl. Conf. on Robotics and Automation (2007)

  11. Brockett, R. W.: Asymptotic Stability and Feedback Stabilization, pp 181–191. Birkhauser, Boston (1983)

    MATH  Google Scholar 

  12. Cai, K., Wang, C., Cheng, J., De Silva, C. W., Meng, M. Q. H.: Mobile robot path planning in dynamic environments: A survey. arXiv:2006.14195 (2020)

  13. Chang, C. C., Song, K. T.: Environment prediction for a mobile robot in a dynamic environment. IEEE Trans. Robot. Autom. 13(6), 862–872 (1997)

    Article  Google Scholar 

  14. Chen, Z., Ngai, D. C. K., Yung, N. H. C.: Behavior prediction based on obstacle motion patterns in dynamically changing environments. In: Proceedings of the 2008 IEEE/WIC/ACM Intl. Conf. on Intelligent Agent Technology, pp. 132–135 (2008)

  15. Dixon, W., Walker, I., Dawson, D.: Fault Detection for Wheeled Mobile Robots with Parametric Uncertainty. In: 2001. Proceedings. 2001 IEEE/ASME International Conference On Advanced Intelligent Mechatronics, vol. 2, pp. 1245–1250 (2001)

  16. Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A.: Koltun, V.: CARLA: An open urban driving simulator. In: Proceedings of the 1st Annual Conference on Robot Learning, pp. 1–16 (2017)

  17. Du Toit, N., Burdick, J.: Robot motion planning in dynamic, uncertain environments. IEEE Trans. Robot. 28(1), 101–115 (2012)

  18. Elnagar, A., Gupta, K.: Motion prediction of moving objects based on autoregressive model. IEEE Trans. Syst. Man Cybern. Part A 28(6), 803–810 (1998)

    Article  Google Scholar 

  19. Elnagar, A., Hussein, A.: An Adaptive Motion Prediction Model for Trajectory Planner Systems. In: Intl. Conf. on Robotics and Automation, pp. 2442–2447 (2003)

  20. Ess, A., Leibe, B., Schindler, K., Gool, L. V.: Moving Obstacle Detection in Highly Dynamic Scenes. In: IEEE Intl. Conf. on Robotics and Automation, pp. 56–63 (2009)

  21. Esteban, C. H., Hernandez, C., Schmitt, E. F.: Multi-Stereo 3D Object Reconstruction. In: 3D Data Processing Visualization and Transmission, pp. 159–166 (2002)

  22. Fiorini, P., Shiller, Z.: Motion Planning in Dynamic Environments Using Velocity Obstacles. In: Intl. J. of Robotics Research, vol. 17, pp. 760–772 (1998)

  23. Foka, A. F., Trahanias, P. E.: Predictive Autonomous Robot Navigation. In: Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), pp 490–495 (2002)

  24. Fraichard, T., Mermond, R.: Path Planning with Uncertainty for Car-Like Robots. In: 1998. Proceedings. 1998 IEEE International Conference On Robotics and Automation, vol. 1, pp. 27 –32 Vol.1 (1998)

  25. Fulgenzi, C., Tay, C., Spalanzani, A., Laugier, C.: Probabilistic Navigation in Dynamic Environment Using Rapidly-Exploring Random Trees and Gaussian Processes. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 1056–1062 (2008). https://doi.org/10.1109/IROS.2008.4650959

  26. Gallagher, G., Srinivasa, S. S., Bagnell, J. A., Ferguson, D.: Gatmo: a Generalized Approach to Tracking Movable Objects. In: IEEE Intl. Conf. on Robotics and Automation, pp 2043–2048 (2009)

  27. Garrido, S., Blanco, D., Moreno, L., Martin, F.: Improving Rrt Motion Trajectories Using Vfm. In: 2009 IEEE International Conference on Mechatronics, pp 1–6 (2009). https://doi.org/10.1109/ICMECH.2009.4957120

  28. Garrido, S., Moreno, L., Blanco, D., Martin, F.: Smooth Path Planning for Non-Holonomic Robots Using Fast Marching. In: 2009 IEEE International Conference on Mechatronics, pp 1–6 (2009). https://doi.org/10.1109/ICMECH.2009.4957121

  29. Gavrila, D.: The visual analysis of human movement: a survey. Comput. Vis. Image Underst. 73, 82–98 (1999)

    Article  Google Scholar 

  30. Govea, V., Alejandro, D., Large, F., Fraichard, T., Laugier, C.: High-Speed Autonomous Navigation with Motion Prediction for Unknown Moving Obstacles. In: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp 82–87 (2004)

  31. Govea, V., Alejandro, D., Large, F., Fraichard, T., Laugier, C.: Moving Obstacles’ Motion Prediction for Autonomous Navigation. In: Int. Conf. on Control, Automation, Robotics and Vision (2004)

  32. Han, M., Kanade, T.: Creating 3D Models with Uncalibrated Cameras. In: Proceeding of IEEE Computer Society Workshop on the Application of Computer Vision (WACV2000) (2000)

  33. Hassler, S.: Self-driving cars and trucks are on the move [spectral lines]. IEEE Spectr. 54(1), 6–6 (2017)

    Article  Google Scholar 

  34. Huazhong, L., Yongsheng, L., Meini, W., Tangren, D.: Design and implementation of improved rrt algorithm for collision free motion planning of high-dimensional robot in complex environment. In: Proceedings of 2012 2nd International Conference on Computer Science and Network Technology, pp 1391–1397 (2012). https://doi.org/10.1109/ICCSNT.2012.6526180

  35. Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Rob. Res. 30(7), 846–894. https://doi.org/10.1177/0278364911406761 (2011)

  36. Karaman, S., Frazzoli, E.: Sampling-Based Optimal Motion Planning for Non-Holonomic Dynamical Systems. In: IEEE International Conference on Robotics and Automation (ICRA), pp 5041–5047 (2013)

  37. Kavraki, L., Svestka, P., Latombe, J., Overmars, M.: Probabilistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces. In: IEEE Trans. on Robotics and Automation, vol. 12, pp 566–580 (1996)

  38. Kodratoff, Y., Moscatelli, S.: Machine learning for object recognition and scene analysis. Int. J. Pattern Recogn. AI 8, 259–304 (1994)

    Article  Google Scholar 

  39. Kurniawati, H., Yanzhu, D., Hsu, D., Wee, S. L.: Motion planning under uncertainty for robotic tasks with long time horizons. Int. J. Robot. Res. 30(3), 308–323 (2011)

    Article  Google Scholar 

  40. Kushleyev, A., Likhachev, M.: Time-Bounded Lattice for Efficient Planning in Dynamic Environments. In: IEEE Intl. Conf. on Robotics and Automation, pp 1662–1668 (2009)

  41. Kuwata, Y., Fiore, G., Teo, J., Frazzoli, E., How, J.: Motion Planning for Urban Driving Using Rrt. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 1681–1686 (2008)

  42. Kuwata, Y., Fiore, G. A., Teo, J., Frazzoli, E., How, J. P.: Motion Planning for Urban Driving Using Rrt. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 1681–1686 (2008). https://doi.org/10.1109/IROS.2008.4651075

  43. Large, F., Sckhavat, S., Shiller, Z., Laugier, C.: Using Non-Linear Velocity Obstacles to Plan Motions in a Dynamic Environment. In: IEEE Intl. Conf. on Control, Automation, Robotics and Vision (ICARCV), pp 734–739 (2002)

  44. Latombe, J.: Robot motion planning. Kluwer Academic Publishers (1991)

  45. LaValle, S., Sharma, R.: Robot motion planning in a changing, partially predictable environment. In: 1994., Proceedings of the 1994 IEEE International Symposium on Intelligent Control, pp 261–266 (1994)

  46. LaValle, S. M.: Planning algorithms. Cambridge University Press (2006)

  47. LaValle, S. M.: Planning algorithms. Cambridge University Press, New York (2006)

  48. LaValle, Jr., S. M., Kuffner, J.J.: Randomized Kinodynamic Planning. In: IEEE International Conference on Robotics and Automation, pp 473–479 (1999)

  49. Leven, P., Hutchinson, S.: A framework for real-time path planning in changing environments. Intl. J. Robot. Res. 21, 999–1030 (2002)

    Article  Google Scholar 

  50. Lozano-Pérez, T.: Spatial Planning: a Configuration Space Approach. In: IEEE Trans. on Computers, vol. C-32, pp 108–120 (1983)

  51. Missiuro, P., Roy, N.: Adapting probabilistic roadmaps to handle uncertain maps. In: 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on Robotics and Automation, pp 1261–1267 (2006)

  52. Murarka, A., Sridharan, M., Kuipers, B.: Detecting Obstacles and Drop-Offs Using Stereo and Motion Cues for Safe Local Motion. In: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp 702–708 (2008)

  53. Nam, Y. S., Lee, B. H., Kim, M. S.: View-Time Based Moving Obstacle Avoidance Using Stochastic Prediction of Obstacle Motion. In: IEEE Intl. Conf. on Robotics and Automation, pp 1081–1086 (1996)

  54. Noreen, I., Khan, A., Habib, Z.: Optimal path planning using rrt* based approaches: A survey and future directions. Int. J. Adv. Comput Sci. Appl. 7(11). https://doi.org/10.14569/IJACSA.2016.071114 (2016)

  55. Oriolo, G., Vendittelli, M., Freda, L., Troso, G.: The Srt Method: Randomized Strategies for Exploration. In: Robotics and Automation, 2004. Proceedings. ICRA’04. 2004 IEEE International Conference On, vol. 5, pp 4688–4694. IEEE (2004)

  56. Otte, M., Frazzoli, E.: Rrtx. Int. J. Rob. Res. 35(7), 797–822. https://doi.org/10.1177/0278364915594679 (2016)

  57. Page, L., Sanderson, A.: Robot Motion Planning for Sensor-Based Control with Uncertainties. In: 1995. Proceedings., 1995 IEEE International Conference On Robotics and Automation, vol. 2, pp 1333–1340 (1995)

  58. Parkinson, C., Bertozzi, A.L., Osher, S.J.: A Hamilton-Jacobi Formulation for Time-Optimal Paths of Rectangular Nonholonomic Vehicles. In: 2020 59Th IEEE Conference on Decision and Control (CDC), pp 4073–4078 (2020). https://doi.org/10.1109/CDC42340.2020.9303861

  59. Pepy, R., Lambert, A., Mounier, H.: Path Planning Using a Dynamic Vehicle Model. In: Information and Communication Technologies, 2006. ICTTA ’06. 2Nd, vol. 1, pp 781–786 (2006)

  60. Pettersson, O.: Execution monitoring in robotics: a survey. Robot. Auton. Syst. 53(2), 73–88 (2005)

    Article  Google Scholar 

  61. Petti, S., Fraichard, T.: Safe navigation of a car-like robot in a dynamic environment. In: Proceedings of the European Conference on Mobile Robots (2005)

  62. Petti, S., Fraichard, T.: Safe Navigation of a Car-Like Robot in a Dynamic Environment. In: Proc. of the European Conf. on Mobile Robots. Ancona (IT), France. https://hal.inria.fr/inria-00182047. Voir basilic: http://emotion.inrialpes.fr/bibemotion/2005/PF05a/ address: Ancona (IT) (2005)

  63. Pham, T., Smeulders, A.: Object recognition with uncertain geometry and uncertain part detection. Comput. Vis. Image Underst. 99, 258 (2005)

    Article  Google Scholar 

  64. Phillips, J. M., Bedrossian, N., Kavraki, L. E.: Guided Expansive Spaces Trees: a Search Strategy for Motion- and Cost-Constrained State Spaces. In: 2004. Proceedings. ICRA ’04. 2004 IEEE International Conference On Robotics and Automation, vol. 4, pp 3968–3973 (2004). https://doi.org/10.1109/ROBOT.2004.1308890

  65. Rodriguez, S., Tang, X., Lien, J. M., Amato, N.: An obstacle-based rapidly-exploring random tree. In: Proceedings IEEE International Conference on Robotics and Automation, pp 895–900 (2006)

  66. Roy, N., Burgard, W., Fox, D., Thrun, S.: Coastal Navigation-Mobile Robot Navigation with Uncertainty in Dynamic Environments. In: 1999. Proceedings. 1999 IEEE International Conference On Robotics and Automation, vol. 1, pp 35–40 (1999)

  67. Schneiderman, H., Kanade, T.: Object detection using the statistics of parts. Intl. J. Comput. Vis. 56, 151–177 (2004)

    Article  Google Scholar 

  68. Spanogianopoulos, S., Sirlantzis, K.: Non-Holonomic Path Planning of Car-Like Robot Using Rrt*Fn. In: 2015 12Th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp 53–57 (2015)

  69. Tahirovic, A., Magnani, G.: A Roughness-Based Rrt for Mobile Robot Navigation Planning. In: In IFAC World Congress, vol. 18, pp 5944–5949 (2001)

  70. Udupa, S. M.: Collision detection and avoidance in computer controlled manipulators. Ph.D. thesis, Pasadena (1977)

  71. Vannoy, J., Xiao, J.: Real-Time Motion Planning of Multiple Mobile Manipulators with a Common Task Objective in Shared Work Environments. In: IEEE Intl. Conf. on Robotics and Automation, pp 20–26 (2007)

  72. Vasile, C. I., Li, X., Belta, C.: Reactive sampling-based path planning with temporal logic specifications. Int. J. Robot. Res. 39(8), 1002–1028 (2020)

    Article  Google Scholar 

  73. Ward, J., Katupitiya, J.: Free Space Mapping and Motion Planning in Configuration Space for Mobile Manipulators. In: IEEE Intl. Conf. on Robotics and Automation, pp 4981–4986 (2007)

  74. Widyotriatmo, A., Pamosoaji, A., Hong, K. S.: Robust Configuration Control of a Mobile Robot with Uncertainties. In: 2011 8Th Asian Control Conference (ASCC), pp 1036 –1041 (2011)

  75. Withagen, P., Schutte, K., Groen, F.: Object Detection and Tracking Using a Likelihood Based Approach. In: Proc. IEEE Int. Conf. on Image Processing (2003)

  76. Zhu, X., Goldberg, A. B.: Introduction to semi-supervised learning. Synth. Lect. Artif. Intell. Mach. Learn. 3(1), 1–130 (2009)

    MATH  Google Scholar 

  77. Ziegler, J., Werling, M.: Navigating Car-Like Robots in Unstructured Environments Using an Obstacle Sensitive Cost Function. In: 2008 IEEE Intelligent Vehicles Symposium, pp 787–791 (2008). https://doi.org/10.1109/IVS.2008.4621302

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Acknowledgements

This publication is based upon research work supported by the Khalifa University of Science and Technology under Award No. RC1-2018-KUCARS

Funding

This publication is based upon research work supported by the Khalifa University of Science and Technology under Award No. RC1-2018-KUCARS.

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Authors and Affiliations

  1. Center for Autonomous Robotic Systems (KUCARS), Khalifa University, Abu Dhabi, UAE

    Sotirios Spanogiannopoulos, Yahya Zweiri & Lakmal Seneviratne

  2. Faculty of Science, Engineering and computing, Kingston University, London, UK

    Yahya Zweiri

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  1. Sotirios Spanogiannopoulos
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Contributions

Author: Sotirios Spanogiannopoulos Facilitating real-time planner for non-holonomic path generation process will need further advancements [68]:

1. Computational simplicity in non-holonomic path generation.

2. acilitation of incremental collision-free paths.

3. Ability to modify the existing non-holonomic path generated based on updated sensor data from the environment.

Co-authors: Yahya Zweiri and Lakmal Seneviratne Help on reviewing paper from different angles several times before the final submission. Special thanks to Lakmal Seneviratne for his suggestions on paper’s proofreading.

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Correspondence to Sotirios Spanogiannopoulos.

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Spanogiannopoulos, S., Zweiri, Y. & Seneviratne, L. Sampling-based Non-Holonomic Path Generation for Self-driving Cars. J Intell Robot Syst 104, 14 (2022). https://doi.org/10.1007/s10846-021-01440-z

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