Abstract
Obstacle avoidance is a core module for autonomous vehicle working in open environment. A lot of research is concentrated on obstacle avoidance and path planning of vehicle moving in two or three dimensions. However, when it comes to one degree of freedom, the topic is still little explored. In this paper, an imitation learning approach for autonomous vehicles longitudinal obstacle avoidance is introduced. This work aims to show the interest of using a supervised learning approach to imitate human’s behaviour when driving in environments with unpredictable obstacles. Two machine learning methods, K-nearest neighbours and XGBoost, were integrated into our learning architecture and were tested on two applications in logistics and transportation. The results show the ability of our solution to cope with different types of vehicle dynamics. Our solution is for each application able to reproduce the same decision of an expert driver and to make a trip without collision and with acceptable time travel.
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References
Andreasson, H., Bouguerra, A., Cirillo, M., Dimitrov, D.N., Driankov, D., Karlsson, L., Lilienthal, A.J., Pecora, F., Saarinen, J.P., Sherikov, A., Stoyanov, T.: Autonomous transport vehicles: where we are and what is missing. IEEE Robot. Autom. Magaz. 22(1), 64–75 (2015)
Aradi, S.: Survey of deep reinforcement learning for motion planning of autonomous vehicles. IEEE Trans. Intell. Transp. Syst. 1–20 (2020)
Cardarelli, E., Digani, V., Sabattini, L., Secchi, C., Fantuzzi, C.: Cooperative cloud robotics architecture for the coordination of multi-AGV systems in industrial warehouses. Mechatronics 45, 1–13 (2017)
Chen, T., Guestrin, C.: XGBoost: a scalable tree Boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)
De Ryck, M., Versteyhe, M., Debrouwere, F.: Automated guided vehicle systems, state-of-the-art control algorithms and techniques. J. Manufact. Syst. 54, 152–173 (2020)
Gonzalez, D., Perez, J., Milanes, V., Nashashibi, F.: A review of motion planning techniques for automated vehicles. IEEE Trans. Intell. Transp. Syst. 17(4), 1135–1145 (2016)
Hamandi, M., D’Arcy, M., Fazli, P.: DeepMoTIon: learning to navigate like humans. arXiv:1803.03719 [cs, stat] (2019)
Huh, J., Lee, D.D.: Efficient sampling With Q-learning to guide rapidly exploring random trees. IEEE Robot. Autom. Lett. 3(4), 3868–3875 (2018)
Hussein, A., Gaber, M.M., Elyan, E., Jayne, C.: Imitation learning: a survey of learning methods. ACM Comput. Surveys 50(2), 1–35 (2017)
Ichter, B., Harrison, J., Pavone, M.: Learning sampling distributions for robot motion planning. arXiv:1709.05448 [cs] (2019)
Kunchev, V., Jain, L., Ivancevic, V., Finn, A.: Path planning and obstacle avoidance for autonomous mobile robots: a review. In: Knowledge-Based Intelligent Information and Engineering Systems, vol. 4252, pp. 537–544 (2006)
Kuwata, Y., Karaman, S., Teo, J., Frazzoli, E., How, J., Fiore, G.: Real-time motion planning with applications to autonomous Urban driving. IEEE Trans. Cont. Syst. Technol. 17(5), 1105–1118 (2009)
Liu, Y., Li, S., Li, J., Shen, J.: Operation policy research of double rail-guided vehicle based on simulation. In: 2010 International Conference on E-Product E-Service and E-Entertainment, pp. 1–4 (2010)
Macek, K., Vasquez, D., Fraichard, T., Siegwart, R.: Safe vehicle navigation in dynamic urban scenarios. In: 2008 11th International IEEE Conference on Intelligent Transportation Systems, pp. 482–489 (2008)
Meyes, R., Tercan, H., Roggendorf, S., Thiele, T., Büscher, C., Obdenbusch, M., Brecher, C., Jeschke, S., Meisen, T.: Motion planning for industrial robots using reinforcement learning. Proced. CIRP 63, 107–112 (2017)
Pfeiffer, M., Schaeuble, M., Nieto, J., Siegwart, R., Cadena, C.: From perception to decision: a data-driven approach to end-to-end motion planning for autonomous ground robots. In: 2017 IEEE International Conference on Robotics and Automation (ICRA) pp. 1527–1533 (2017)
Plissonneau, A., Trentesaux, D., Ben-Messaoud, W., Bekrar, A.: Ai-based speed control models for the autonomous train: a literature review. In: 2021 Third International Conference on Transportation and Smart Technologies (TST), pp. 9–15 (2021)
Sallez, Y., Berger, T., Bonte, T.: The concept of safety bubble for reconfigurable assembly systems. Manufact. Lett. 24, 77–81 (2020)
Schwarting, W., Alonso-Mora, J., Rus, D.: Planning and decision-making for autonomous vehicles. Ann. Rev. Cont. Robot. Autonom. Syst. 1(1), 187–210 (2018)
Stentz, A.: Optimal and efficient path planning for partially-known environments. In: Proceedings of the 1994 IEEE International Conference on Robotics and Automation, vol. 4, pp. 3310–3317 (1994)
Takahashi, T., Sun, H., Tian, D., Wang, Y.: Learning heuristic functions for mobile robot path planning using deep neural networks. In: Proceedings of the International Conference on Automated Planning and Scheduling, vol. 29, pp. 764–772 (2021)
Touzani, S., Granderson, J., Fernandes, S.: Gradient boosting machine for modeling the energy consumption of commercial buildings. Energy Buildings 158, 1533–1543 (2018)
Wang, J., Zhang, T., Ma, N., Li, Z., Ma, H., Meng, F., Meng, M.Q.: A survey of learning-based robot motion planning. IET Cyber-Syst, Robot (2021)
Acknowledgements
This research work contributes to the French collaborative project TFA (autonomous freight train), with SNCF, Alstom Transport, Hitachi Rail STS, Capgemini Engineering and Apsys. It was carried out in the framework of IRT Railenium, Valenciennes, France, and therefore was granted public funds within the scope of the French Program “Investissements d’Avenir”.
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Plissonneau, A., Trentesaux, D., Ben-Messaoud, W., Bekrar, A. (2022). An Imitation Learning Approach for Vehicles Longitudinal Obstacle Avoidance in Logistics and Transportation. In: Borangiu, T., Trentesaux, D., Leitão, P., Cardin, O., Joblot, L. (eds) Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future. SOHOMA 2021. Studies in Computational Intelligence, vol 1034. Springer, Cham. https://doi.org/10.1007/978-3-030-99108-1_38
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