{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T16:04:52Z","timestamp":1778256292036,"version":"3.51.4"},"reference-count":36,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,5,22]],"date-time":"2021-05-22T00:00:00Z","timestamp":1621641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Autonomous mobile robots are usually faced with challenging situations when driving in complex environments. Namely, they have to recognize the static and dynamic obstacles, plan the driving path and execute their motion. For addressing the issue of perception and path planning, in this paper, we introduce OctoPath, which is an encoder-decoder deep neural network, trained in a self-supervised manner to predict the local optimal trajectory for the ego-vehicle. Using the discretization provided by a 3D octree environment model, our approach reformulates trajectory prediction as a classification problem with a configurable resolution. During training, OctoPath minimizes the error between the predicted and the manually driven trajectories in a given training dataset. This allows us to avoid the pitfall of regression-based trajectory estimation, in which there is an infinite state space for the output trajectory points. Environment sensing is performed using a 40-channel mechanical LiDAR sensor, fused with an inertial measurement unit and wheels odometry for state estimation. The experiments are performed both in simulation and real-life, using our own developed GridSim simulator and RovisLab\u2019s Autonomous Mobile Test Unit platform. We evaluate the predictions of OctoPath in different driving scenarios, both indoor and outdoor, while benchmarking our system against a baseline hybrid A-Star algorithm and a regression-based supervised learning method, as well as against a CNN learning-based optimal path planning method.<\/jats:p>","DOI":"10.3390\/s21113606","type":"journal-article","created":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T00:01:20Z","timestamp":1621814480000},"page":"3606","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["OctoPath: An OcTree-Based Self-Supervised Learning Approach to Local Trajectory Planning for Mobile Robots"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6169-1181","authenticated-orcid":false,"given":"Bogdan","family":"Tr\u0103snea","sequence":"first","affiliation":[{"name":"Robotics, Vision and Control Laboratory (ROVIS), Transilvania University of Brasov, 500036 Brasov, Romania"},{"name":"Elektrobit Automotive, 500365 Brasov, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cosmin","family":"Gineric\u0103","sequence":"additional","affiliation":[{"name":"Robotics, Vision and Control Laboratory (ROVIS), Transilvania University of Brasov, 500036 Brasov, Romania"},{"name":"Elektrobit Automotive, 500365 Brasov, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mihai","family":"Zaha","sequence":"additional","affiliation":[{"name":"Robotics, Vision and Control Laboratory (ROVIS), Transilvania University of Brasov, 500036 Brasov, Romania"},{"name":"Elektrobit Automotive, 500365 Brasov, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gigel","family":"M\u0103ce\u015fanu","sequence":"additional","affiliation":[{"name":"Robotics, Vision and Control Laboratory (ROVIS), Transilvania University of Brasov, 500036 Brasov, Romania"},{"name":"Elektrobit Automotive, 500365 Brasov, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0790-9343","authenticated-orcid":false,"given":"Claudiu","family":"Pozna","sequence":"additional","affiliation":[{"name":"Robotics, Vision and Control Laboratory (ROVIS), Transilvania University of Brasov, 500036 Brasov, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4763-5540","authenticated-orcid":false,"given":"Sorin","family":"Grigorescu","sequence":"additional","affiliation":[{"name":"Robotics, Vision and Control Laboratory (ROVIS), Transilvania University of Brasov, 500036 Brasov, Romania"},{"name":"Elektrobit Automotive, 500365 Brasov, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xu, H., Gao, Y., Yu, F., and Darrell, T. (2017, January 21\u201326). End-to-end learning of driving models from large-scale video datasets. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.376"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Jaritz, M., Charette, R., Toromanoff, M., Perot, E., and Nashashibi, F. (2018, January 21\u201325). End-to-End Race Driving with Deep Reinforcement Learning. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.","DOI":"10.1109\/ICRA.2018.8460934"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Pendleton, S.D., Andersen, H., Du, X., Shen, X., Meghjani, M., Eng, Y.H., Rus, D., and Ang, M.H. (2017). Perception, planning, control, and coordination for autonomous vehicles. Machines, 5.","DOI":"10.3390\/machines5010006"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3441","DOI":"10.1109\/LRA.2019.2926224","article-title":"NeuroTrajectory: A Neuroevolutionary Approach to Local State Trajectory Learning for Autonomous Vehicles","volume":"4","author":"Grigorescu","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation learning: A review and new perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hessel, M., Modayil, J., Van Hasselt, H., Schaul, T., Ostrovski, G., Dabney, W., Horgan, D., Piot, B., Azar, M., and Silver, D. (2018, January 2\u20137). Rainbow: Combining improvements in deep reinforcement learning. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11796"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_8","first-page":"1135","article-title":"A review of motion planning techniques for automated vehicles","volume":"17","author":"Nashashibi","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1002\/rob.21918","article-title":"A survey of deep learning techniques for autonomous driving","volume":"37","author":"Grigorescu","year":"2020","journal-title":"J. Field Robot."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1109\/LRA.2020.2966414","article-title":"Learning robust control policies for end-to-end autonomous driving from data-driven simulation","volume":"5","author":"Amini","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1177\/0278364919880273","article-title":"Imitation learning for agile autonomous driving","volume":"39","author":"Pan","year":"2020","journal-title":"Int. J. Robot. Res."},{"key":"ref_12","unstructured":"Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L.D., Monfort, M., Muller, U., and Zhang, J. (2016). End to End Learning for Self-Driving Cars. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kahn, G., Abbeel, P., and Levine, S. (2020). BADGR: An autonomous self-supervised learning-based navigation system. arXiv.","DOI":"10.1109\/LRA.2021.3057023"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.procs.2018.01.054","article-title":"Grid path planning with deep reinforcement learning: Preliminary results","volume":"123","author":"Panov","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, B., Liu, Z., Li, Q., and Prorok, A. (2020). Mobile Robot Path Planning in Dynamic Environments through Globally Guided Reinforcement Learning. arXiv.","DOI":"10.1109\/LRA.2020.3026638"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Salay, R., Queiroz, R., and Czarnecki, K. (2018). An Analysis of ISO 26262: Machine Learning and Safety in Automotive Software, SAE. Technical Report, SAE Technical Paper.","DOI":"10.4271\/2018-01-1075"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Steffi, D.D., Mehta, S., Venkatesh, K., and Dasari, S.K. (2021). Robot Path Planning\u2014Prediction: A Multidisciplinary Platform: A Survey. Data Science and Security, Springer.","DOI":"10.1007\/978-981-15-5309-7_22"},{"key":"ref_19","unstructured":"Cai, K., Wang, C., Cheng, J., De Silva, C.W., and Meng, M.Q.H. (2020). Mobile Robot Path Planning in Dynamic Environments: A Survey. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, J., Yang, S.X., and Xu, Z. (2019, January 6\u20138). A Survey on Robot Path Planning using Bio-inspired Algorithms. Proceedings of the 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), Dali, China.","DOI":"10.1109\/ROBIO49542.2019.8961498"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1748","DOI":"10.1109\/TASE.2020.2976560","article-title":"Neural RRT*: Learning-based optimal path planning","volume":"17","author":"Wang","year":"2020","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wu, H., Chen, Z., Sun, W., Zheng, B., and Wang, W. (2017, January 19\u201325). Modeling trajectories with recurrent neural networks. Proceedings of the 26th International Joint Conference on Artificial Intelligence IJCAI-17, Melbourne, Australia.","DOI":"10.24963\/ijcai.2017\/430"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Park, S.H., Kim, B., Kang, C.M., Chung, C.C., and Choi, J.W. (2018, January 26\u201330). Sequence-to-sequence prediction of vehicle trajectory via LSTM encoder-decoder architecture. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China.","DOI":"10.1109\/IVS.2018.8500658"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ma, Y., Zhu, X., Zhang, S., Yang, R., Wang, W., and Manocha, D. (February, January 27). Trafficpredict: Trajectory prediction for heterogeneous traffic-agents. Proceedings of the AAAI Conference on Artificial Intelligence 2019, Honolulu, HI, USA.","DOI":"10.1609\/aaai.v33i01.33016120"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Altch\u00e9, F., and de La Fortelle, A. (2017, January 16\u201319). An LSTM network for highway trajectory prediction. Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan.","DOI":"10.1109\/ITSC.2017.8317913"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Deo, N., and Trivedi, M.M. (2018, January 26\u201330). Multi-modal trajectory prediction of surrounding vehicles with maneuver based lstms. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China.","DOI":"10.1109\/IVS.2018.8500493"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kim, B., Kang, C.M., Kim, J., Lee, S.H., Chung, C.C., and Choi, J.W. (2017, January 16\u201319). Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network. Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan.","DOI":"10.1109\/ITSC.2017.8317943"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s10514-012-9321-0","article-title":"OctoMap: An efficient probabilistic 3D mapping framework based on octrees","volume":"34","author":"Hornung","year":"2013","journal-title":"Auton. Robot."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Han, S. (2018). Towards efficient implementation of an octree for a large 3D point cloud. Sensors, 18.","DOI":"10.3390\/s18124398"},{"key":"ref_30","unstructured":"Vanneste, S., Bellekens, B., and Weyn, M. (2014, January 21). 3DVFH+: Real-time three-dimensional obstacle avoidance using an Octomap. Proceedings of the MORSE 2014\u2014Model-Driven Robot Software Engineering, York, UK."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"26765","DOI":"10.1007\/s11042-020-09302-w","article-title":"Multi-granularity environment perception based on octree occupancy grid","volume":"79","author":"Zhang","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"9681","DOI":"10.3390\/s150509681","article-title":"Analysis and experimental kinematics of a skid-steering wheeled robot based on a laser scanner sensor","volume":"15","author":"Wang","year":"2015","journal-title":"Sensors"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1243\/0954407011525683","article-title":"A general theory for skid steering of tracked vehicles on firm ground","volume":"215","author":"Wong","year":"2001","journal-title":"Proc. Inst. Mech. Eng. Part D J. Automob. Eng."},{"key":"ref_34","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_35","first-page":"18","article-title":"Practical search techniques in path planning for autonomous driving","volume":"1001","author":"Dolgov","year":"2008","journal-title":"Ann Arbor"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Trasnea, B., Marina, L., Vasilcoi, A., Pozna, C., and Grigorescu, S. (2019, January 25\u201327). GridSim: A Simulated Vehicle Kinematics Engine for Deep Neuroevolutionary Control in Autonomous Driving. Proceedings of the 2019 Third IEEE International Conference on Robotic Computing (IRC), Naples, Italy.","DOI":"10.1109\/IRC.2019.00091"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/11\/3606\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:05:53Z","timestamp":1760162753000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/11\/3606"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,22]]},"references-count":36,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["s21113606"],"URL":"https:\/\/doi.org\/10.3390\/s21113606","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,22]]}}}