{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T02:18:08Z","timestamp":1768443488014,"version":"3.49.0"},"reference-count":25,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,30]],"date-time":"2023-12-30T00:00:00Z","timestamp":1703894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Central Military Commission Science and Technology Commission Project"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Unmanned transportation in construction scenarios presents a significant challenge due to the presence of complex dynamic on-ground obstacles and potential airborne falling objects. Consequently, the typical methodology for composite air\u2013ground risk avoidance in construction scenarios holds enormous importance. In this paper, an integrated potential-field-based risk assessment approach is proposed to evaluate the threat severity of the environmental obstacles. Meanwhile, the self-adaptive dynamic window approach is suggested to manage the real-time motion planning solution for air\u2013ground risks. By designing the multi-objective velocity sample window, we constrain the vehicle\u2019s speed planning instructions within reasonable limits. Combined with a hierarchical decision-making mechanism, this approach achieves effective obstacle avoidance with multiple drive modes. Simulation results demonstrate that, in comparison with the traditional dynamic window approach, the proposed method offers enhanced stability and efficiency in risk avoidance, underlining its notable safety and effectiveness.<\/jats:p>","DOI":"10.3390\/s24010221","type":"journal-article","created":{"date-parts":[[2023,12,31]],"date-time":"2023-12-31T06:00:21Z","timestamp":1704002421000},"page":"221","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["An Integrated Autonomous Dynamic Navigation Approach toward a Composite Air\u2013Ground Risk Construction Scenario"],"prefix":"10.3390","volume":"24","author":[{"given":"Da","family":"Jiang","sequence":"first","affiliation":[{"name":"National Engineering Laboratory for Wheeled Vehicle, China North Vehicle Research Institute, Beijing 100072, China"}]},{"given":"Meijing","family":"Wang","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Wheeled Vehicle, China North Vehicle Research Institute, Beijing 100072, China"}]},{"given":"Xiaole","family":"Chen","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Wheeled Vehicle, China North Vehicle Research Institute, Beijing 100072, China"}]},{"given":"Hongchao","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Wheeled Vehicle, China North Vehicle Research Institute, Beijing 100072, China"}]},{"given":"Kang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"given":"Chengchi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"given":"Shuhui","family":"Li","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Wheeled Vehicle, China North Vehicle Research Institute, Beijing 100072, China"}]},{"given":"Ling","family":"Du","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Wheeled Vehicle, China North Vehicle Research Institute, Beijing 100072, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1038\/s41586-023-05732-2","article-title":"Dense reinforcement learning for safety validation of autonomous vehicles","volume":"615","author":"Feng","year":"2023","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.1109\/LRA.2021.3056028","article-title":"Path planning for UGVs based on traversability hybrid A","volume":"6","author":"Thoresen","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"116605","DOI":"10.1016\/j.eswa.2022.116605","article-title":"Modified continuous ant colony optimisation for multiple unmanned ground vehicle path planning","volume":"196","author":"Liu","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.1109\/TASE.2020.3010887","article-title":"Trajectory planning and tracking strategy applied to an unmanned ground vehicle in the presence of obstacles","volume":"18","author":"Zhou","year":"2020","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chen, D., Wang, Z., Zhou, G., and Li, S. (2022). Path planning and energy efficiency of heterogeneous mobile robots using Cuckoo\u2013beetle swarm search algorithms with applications in UGV obstacle avoidance. Sustainability, 14.","DOI":"10.3390\/su142215137"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Baras, N., and Dasygenis, M. (2023). UGV Coverage Path Planning: An Energy-Efficient Approach through Turn Reduction. Electronics, 12.","DOI":"10.3390\/electronics12132959"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103068","DOI":"10.1016\/j.autcon.2019.103068","article-title":"An integrated UGV-UAV system for construction site data collection","volume":"112","author":"Asadi","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Pan, L., and Zhang, Y. (2010, January 24\u201327). Scene image classifying via the partially connected neural network. Proceedings of the 2010 5th International Conference on Computer Science & Education, Hefei, China.","DOI":"10.1109\/ICCSE.2010.5593440"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, P., Gao, S., Li, L., Sun, B., and Cheng, S. (2019). Obstacle avoidance path planning design for autonomous driving vehicles based on an improved artificial potential field algorithm. Energies, 12.","DOI":"10.3390\/en12122342"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1049\/iet-its.2020.0048","article-title":"Vehicle collision avoidance motion planning strategy using artificial potential field with adaptive multi-speed scheduler","volume":"14","author":"Wahid","year":"2020","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1376","DOI":"10.1109\/TIE.2019.2898599","article-title":"A motion planning and tracking framework for autonomous vehicles based on artificial potential field elaborated resistance network approach","volume":"67","author":"Huang","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1186\/s13638-019-1396-2","article-title":"Obstacle avoidance of mobile robots using modified artificial potential field algorithm","volume":"2019","author":"Rostami","year":"2019","journal-title":"EURASIP J. Wirel. Commun. Netw."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1007\/s10846-020-01151-x","article-title":"Dynamic path planning of the UAV avoiding static and moving obstacles","volume":"99","author":"Chen","year":"2020","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"154679","DOI":"10.1109\/ACCESS.2021.3128295","article-title":"UAV dynamic path planning based on obstacle position prediction in an unknown environment","volume":"9","author":"Feng","year":"2021","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.cja.2020.12.018","article-title":"A newly bio-inspired path planning algorithm for autonomous obstacle avoidance of UAV","volume":"34","author":"Zhou","year":"2021","journal-title":"Chin. J. Aeronaut."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isatra.2022.03.027","article-title":"Disturbance observer-based adaptive reinforcement learning for perturbed uncertain surface vessels","volume":"130","author":"Pham","year":"2022","journal-title":"ISA Trans."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, L., Dai, S., and Dong, C. (IEEE Trans. Neural Netw. Learn. Syst., 2023). Adaptive optimal tracking control of an underactuated surface vessel using Actor\u2013Critic reinforcement learning, IEEE Trans. Neural Netw. Learn. Syst., early access.","DOI":"10.1109\/TNNLS.2022.3214681"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1109\/TITS.2019.2954952","article-title":"Memory-based deep reinforcement learning for obstacle avoidance in UAV with limited environment knowledge","volume":"22","author":"Singla","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.procs.2022.10.217","article-title":"A reinforcement learning based path planning approach in 3D environment","volume":"212","author":"Kulathunga","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Nguyen, K., Dang, V., Pham, D., and Dao, P. (Int. J. Robust Nonlinear Control, 2023). Formation control scheme with reinforcement learning strategy for a group of multiple surface vehicles, Int. J. Robust Nonlinear Control, early access.","DOI":"10.1002\/rnc.7083"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1016\/j.isatra.2022.02.034","article-title":"A reinforcement learning-based near-optimal hierarchical approach for motion control: Design and experiment","volume":"129","author":"Qin","year":"2022","journal-title":"ISA Trans."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1109\/100.580977","article-title":"The dynamic window approach to collision avoidance","volume":"4","author":"Fox","year":"1997","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jiang, F., Dong, M., Fan, Y., and Wang, Q. (2022). Research on motor speed control method based on the prevention of vehicle rollover. Energies, 15.","DOI":"10.3390\/en15103609"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1080\/13588265.2019.1593290","article-title":"Rollover crashworthiness analyses\u2013an overview and state of the art","volume":"25","author":"Seyedi","year":"2020","journal-title":"Int. J. Crashworthiness"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1687814018821218","DOI":"10.1177\/1687814018821218","article-title":"Research method of vehicle rollover mechanism under critical instability condition","volume":"11","author":"Li","year":"2019","journal-title":"Adv. Mech. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/1\/221\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:44:48Z","timestamp":1760132688000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/1\/221"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,30]]},"references-count":25,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["s24010221"],"URL":"https:\/\/doi.org\/10.3390\/s24010221","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,30]]}}}