Wheeled-legged robots for multi-terrain locomotion in plateau environments

Abstract

Wheeled-legged robots integrate the mobility efficiency of wheeled platforms with the terrain adaptability of legged robots, making them ideal for complex, unstructured environments. However, balancing high payload capacity with agile multimodal locomotion remains a major challenge. This paper presents a field study conducted in the high-altitude region of Golmud, Qinghai, with elevations ranging from 2800 m to 4000 m. We evaluate three wheeled-legged robot platforms of different scales on diverse terrains including Gobi, desert, grassland, and wetlands. Our experiments demonstrate the robot’s robust locomotion performance across multimodal tasks such as obstacle crossing, slope climbing, and terrain classification. Moreover, we validate the performance of autonomous perception systems, including real-time localization and 3D mapping, under harsh plateau conditions. The results provide valuable insights into the deployment of wheeled-legged robots in extreme natural environments and lay a solid foundation for future applications in inspection, rescue, and transport missions in high-altitude regions.

Keywords

Wheeled-legged robots / Reinforcement learning control / Terrain classification / Perception and mapping

CRediT authorship contribution statement

Kang Wang: Conceptualization. Jinmian Hou: Writing - review & editing, Writing - original draft, Software, Resources, Methodology. Shichao Zhou: Writing - original draft, Methodology. Dachuang Wei: Writing - original draft. Wei Xu: Project administration. Yulin Wang: Project administration. Hui Chai: Project administration. Lingkun Chen: Project administration. Qiuguo Zhu: Project administration. Liang Gao: Project administration. Min Guo: Conceptualization. Guoteng Zhang: Conceptualization. Zhongqu Xie: Conceptualization. Tuo Liu: Conceptualization. Mingyue Zhu: Writing - original draft. Yueming Wang: Conceptualization. Tong Yan: Conceptualization. Jingsong Gao: Writing - original draft. Meng Hong: Writing - original draft. Weikai Ding: Writing - original draft.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported in part by the National Key R&D Program of china (2022YFB4701500 and 2024YFB4708705), in part by the National Natural Science Foundation of China (52475021, 52305024 and 52205012), in part by the Natural Science Foundation of Jiangsu Province, China (BK20230928), in part by the China Postdoctoral Science Foundation, China (2023M731690), in part by the Fundamental Research Funds for the Central Universities, China (30923011029).

Appendix A. Supplementary data

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.birob.2025.100256.

References

[1] Unitree Robotics,Go2-W high-performance wheeled-legged robot, 2025, Available online: https://www.unitree.com/cn/go2-w. (Accessed on June 24, 2025).

[2] DEEP Robotics,Lynx M20 medium-sized wheeled-legged robot, 2025, Avail-able online: https://www.deeprobotics.cn/robot/wap/lynx.html. (Accessed on June 24, 2025).

[3] M. Bjelonic, Swiss-mile: Transforming mobility with wheeled-legged robots, 2025, Available online: https://www.swiss-mile.com/. (Accessed on June 24, 2025).

[4] M. Bjelonic, R. Grandia, M. Geilinger, O. Harley, V.S. Medeiros, V. Pajovic, E. Jelavic, S. Coros, M. Hutter, Offline motion libraries and online MPC for advanced mobility skills, Int. J. Robot. Res. 41 (9-10) (2022) 903-924.

[5] J. Lee, M. Bjelonic, M. Hutter, Control of wheeled-legged quadrupeds using deep reinforcement learning, in: Climbing and Walking Robots Conference, Springer, 2022, pp. 119-127.

[6] J. Lee, M. Bjelonic, A. Reske, L. Wellhausen, T. Miki, M. Hutter, Learning robust autonomous navigation and locomotion for wheeled-legged robots, Sci. Robot. 9 (89) (2024) eadi9641.

[7] W. Reid, B. Emanuel, B. Chamberlain-Simon, S. Karumanchi, G. Meirion-Griffith, Mobility mode evaluation of a wheel-on-limb rover on glacial ice analogous to europa terrain, in: 2020 IEEE Aerospace Conference, IEEE, 2020, pp. 1-9.

[8] M. Bjelonic, R. Grandia, O. Harley, C. Galliard, S. Zimmermann, M. Hutter, Whole-body mpc and online gait sequence generation for wheeled-legged robots, in: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, IEEE, 2021, pp. 8388-8395.

[9] S. Zhou, B. Xiang, Z. Xie, L. Chen, C. Gu, P. Ma, Y. Wang, Proprioceptive-based multimode state estimation for wheeled-quadruped robot, IEEE Sensors J. 25 (14) (2025) 27587-27595, http://dx.doi.org/10.1109/JSEN.2025.3576923.