{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:49:13Z","timestamp":1772909353752,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T00:00:00Z","timestamp":1657584000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62003217"],"award-info":[{"award-number":["62003217"]}]},{"name":"National Natural Science Foundation of China","award":["62173234"],"award-info":[{"award-number":["62173234"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Path planning for wheeled mobile robots on partially known uneven terrain is an open challenge since robot motions can be strongly influenced by terrain with incomplete environmental information such as locally detected obstacles and impassable terrain areas. This paper proposes a hierarchical path planning approach for a wheeled robot to move in a partially known uneven terrain. We first model the partially known uneven terrain environment respecting the terrain features, including the slope, step, and unevenness. Second, facilitated by the terrain model, we use A\u22c6 algorithm to plan a global path for the robot based on the partially known map. Finally, the Q-learning method is employed for local path planning to avoid locally detected obstacles in close range as well as impassable terrain areas when the robot tracks the global path. The simulation and experimental results show that the designed path planning approach provides satisfying paths that avoid locally detected obstacles and impassable areas in a partially known uneven terrain compared with the classical A\u22c6 algorithm and the artificial potential field method.<\/jats:p>","DOI":"10.3390\/s22145217","type":"journal-article","created":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T23:02:01Z","timestamp":1657666921000},"page":"5217","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Path Planning for Wheeled Mobile Robot in Partially Known Uneven Terrain"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1998-2377","authenticated-orcid":false,"given":"Bo","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China"},{"name":"Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guobin","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China"},{"name":"Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qixin","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China"},{"name":"Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6782-5571","authenticated-orcid":false,"given":"Xiaoshan","family":"Bai","sequence":"additional","affiliation":[{"name":"College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China"},{"name":"Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Ding","sequence":"additional","affiliation":[{"name":"College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China"},{"name":"Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4914-5959","authenticated-orcid":false,"given":"Awais","family":"Khan","sequence":"additional","affiliation":[{"name":"College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China"},{"name":"Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1117\/12.553242","article-title":"High speed hazard avoidance for mobile robots in rough terrain","volume":"5422","author":"Spenko","year":"2004","journal-title":"Unmanned Ground Veh. Technol. VI"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wan, W., Gou, S., Peng, M., Liu, Z., Di, K., Li, L., Yu, T., Wang, J., and Cheng, X. (2020). Vision-based decision support for rover path planning in the Chang\u2019e-4 Mission. Remote Sens., 12.","DOI":"10.3390\/rs12040624"},{"key":"ref_3","unstructured":"(2021, November 18). [EB\/OL], Available online: http:\/\/mars.jpl.nasa.gov\/MPF\/index1.html."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liang, H., Bai, H., Sun, R., Sun, R., and Li, C. (2017, January 26\u201328). Three-dimensional path planning based on DEM. Proceedings of the 2017 36th Chinese Control Conference (CCC), Dalian, China.","DOI":"10.23919\/ChiCC.2017.8028307"},{"key":"ref_5","unstructured":"Dupuis, E., Allard, P., Bakambu, J., Lamarche, T., Zhu, W.H., and Rekleitis, I. (2005, January 5\u20138). Towards autonomous long-range navigation. Proceedings of the 8th International Symposium on Artificial Intelligence, Robotics and Automation in Space, Munich, Germany."},{"key":"ref_6","unstructured":"Vandapel, N., Huber, D.F., Kapuria, A., and Hebert, M. (May, January 26). Natural terrain classification using 3-d ladar data. Proceedings of the IEEE International Conference on Robotics and Automation, ICRA\u201904, New Orleans, LA, USA."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Agrawal, M., Konolige, K., and Bolles, R.C. (2007, January 20\u201321). Localization and Mapping for Autonomous Navigation in Outdoor Terrains: A Stereo Vision Approach. Proceedings of the 2007 IEEE Workshop on Applications of Computer Vision (WACV\u201907), Austin, TX, USA.","DOI":"10.1109\/WACV.2007.40"},{"key":"ref_8","unstructured":"Huber, D., Carmichael, O., and Hebert, M. (2000, January 24\u201328). 3D map reconstruction from range data. Proceedings of the 2000 ICRA, Millennium Conference, IEEE International Conference on Robotics and Automation, Symposia Proceedings (Cat. No.00CH37065), San Francisco, CA, USA."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Stentz, A. (1997). Optimal and efficient path planning for partially known environments. Intelligent Unmanned Ground Vehicles, Springer.","DOI":"10.1007\/978-1-4615-6325-9_11"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/TSSC.1968.300136","article-title":"Formal Basis for the Heuristic Determination of Minimum Cost Paths","volume":"4","author":"Hart","year":"1968","journal-title":"IEEE Trans. Syst. Sci. Cybern."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1177\/02783640122067453","article-title":"Randomized Kinodynamic Planning","volume":"20","author":"LaValle","year":"2001","journal-title":"Int. J. Robot. Res."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Khatib, O. (1986). Real-time obstacle avoidance for manipulators and mobile robots. Autonomous Robot Vehicles, Springer.","DOI":"10.1007\/978-1-4613-8997-2_29"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"91","DOI":"10.5772\/63484","article-title":"Dynamic path planning algorithm for a mobile robot based on visible space and an improved genetic algorithm","volume":"13","author":"Zhang","year":"2016","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5829","DOI":"10.1007\/s00500-016-2161-7","article-title":"An improved ant colony algorithm for robot path planning","volume":"21","author":"Liu","year":"2017","journal-title":"Soft Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"052204","DOI":"10.1007\/s11432-016-9115-2","article-title":"Path planning for mobile robot using self-adaptive learning particle swarm optimization","volume":"61","author":"Li","year":"2018","journal-title":"Sci. China Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rcim.2010.06.019","article-title":"Reinforcement based mobile robot navigation in dynamic environment","volume":"27","author":"Jaradat","year":"2011","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"ref_17","first-page":"84","article-title":"2D and 3D Robot path planning based on the A\u22c6 algorithm","volume":"36","author":"Pan","year":"2015","journal-title":"J. Jinggangshan Univ. (Natural Sci.)"},{"key":"ref_18","first-page":"1758","article-title":"Obstacle avoidance planning based on artificial potential field optimized by point of tangency in three-dimensional space","volume":"26","author":"Peng","year":"2014","journal-title":"J. Syst. Simul."},{"key":"ref_19","first-page":"45","article-title":"Global path planning based on genetic-ant hybrid algorithm for AUV","volume":"45","author":"Pan","year":"2017","journal-title":"J. Huazhong Univ. Sci. Technol. (Nat. Sci. Ed.)"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6748","DOI":"10.1109\/LRA.2020.3011912","article-title":"Deep Reinforcement Learning for Safe Local Planning of a Ground Vehicle in Unknown Rough Terrain","volume":"5","author":"Josef","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"102450","DOI":"10.1109\/ACCESS.2021.3097945","article-title":"Autonomous quadrotor navigation with vision based obstacle avoidance and path planning","volume":"9","author":"Lin","year":"2021","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Luan, P.G., and Thinh, N.T. (2020). Real-time hybrid navigation system-based path planning and obstacle avoidance for mobile robots. Appl. Sci., 10.","DOI":"10.3390\/app10103355"},{"key":"ref_23","unstructured":"Wang, Y., and Dou, W. (2018, January 19\u201323). A parallel algorithm of path planning for DEM terrain data. Proceedings of the 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), Wuxi, China."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mokrane, A., Braham, A.C., and Cherki, B. (2020, January 23\u201324). UAV Path Planning Based on Dynamic Programming Algorithm On Photogrammetric DEMs. Proceedings of the 2020 International Conference on Electrical Engineering (ICEE), Bandung, Indonesia.","DOI":"10.1109\/ICEE49691.2020.9249903"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wermelinger, M., Fankhauser, P., Diethelm, R., Kr\u00fcsi, P., Siegwart, R., and Hutter, M. (2016, January 9\u201314). Navigation planning for legged robots in challenging terrain. Proceedings of the 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea.","DOI":"10.1109\/IROS.2016.7759199"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"R\u00f6smann, C., Hoffmann, F., and Bertram, T. (2017, January 24\u201328). Kinodynamic trajectory optimization and control for car-like robots. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8206458"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xie, W., Fang, X., and Wu, S. (2020, January 4\u20137). 2.5 D Navigation Graph and Improved A-Star Algorithm for Path Planning in Ship inside Virtual Environment. Proceedings of the 2020 Prognostics and Health Management Conference (PHM-Besan\u00e7on), Besancon, France.","DOI":"10.1109\/PHM-Besancon49106.2020.00057"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sharma, A., Gupta, K., Kumar, A., Sharma, A., and Kumar, R. (2017, January 22\u201325). Model based path planning using Q-Learning. Proceedings of the 2017 IEEE International Conference on Industrial Technology (ICIT), Toronto, ON, Canada.","DOI":"10.1109\/ICIT.2017.7915468"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Song, L., Li, C., Wang, X., Zhang, N., and Fu, H. (2018, January 25\u201327). On Local Path Planning for the Mobile Robot based on QL Algorithm. Proceedings of the 2018 37th Chinese Control Conference (CCC), Wuhan, China.","DOI":"10.23919\/ChiCC.2018.8484186"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Xin, J., Zhao, H., Liu, D., and Li, M. (2017, January 20\u201322). Application of deep reinforcement learning in mobile robot path planning. Proceedings of the 2017 Chinese Automation Congress (CAC), Jinan, China.","DOI":"10.1109\/CAC.2017.8244061"},{"key":"ref_31","unstructured":"Liu, X., Yao, Z., Wu, B., Ling, H., Zhu, L., and Zhang, J. (2020, January 10\u201312). Research On Path Planning Of Hull Decontamination Robot Based On Q-Learning. Proceedings of the 2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL), Chongqing, China."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gautam, U., Malmathanraj, R., and Srivastav, C. (2015, January 3\u20134). Simulation for path planning of autonomous underwater vehicle using flower pollination algorithm, genetic algorithm and Q-learning. Proceedings of the 2015 International Conference on Cognitive Computing and Information Processing (CCIP), Noida, India.","DOI":"10.1109\/CCIP.2015.7100710"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"410","DOI":"10.18517\/ijaseit.6.4.832","article-title":"An integrated artificial potential field path planning with kinematic control for nonholonomic mobile robot","volume":"6","author":"Triharminto","year":"2016","journal-title":"Int. J. Adv. Sci. Eng. Inf. Technol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5217\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:49:04Z","timestamp":1760140144000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5217"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,12]]},"references-count":33,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22145217"],"URL":"https:\/\/doi.org\/10.3390\/s22145217","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,12]]}}}