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A unified modeling and trajectory planning method based on system manipulability for the operation process of the legged locomotion manipulation system

Published online by Cambridge University Press:  17 April 2023

Peng Kang Open the ORCID record for Peng Kang [Opens in a new window] ,
Haibin Meng  and
Wenfu Xu
Peng Kang
Affiliation:
School of Mechanical Engineering and Automation, Harbin Institute of Technology (Shenzhen), Shenzhen, China
Haibin Meng
Affiliation:
School of Mechanical Engineering and Automation, Harbin Institute of Technology (Shenzhen), Shenzhen, China
Wenfu Xu*
Affiliation:
School of Mechanical Engineering and Automation, Harbin Institute of Technology (Shenzhen), Shenzhen, China Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics, Shenzhen, China
*
*Corresponding author. E-mail: wfxu@hit.edu.cn
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Abstract

Flexibility is one of the most significant advantages of legged robots in unstructured environments. However, quadruped robots cannot interact with environments to complete some manipulation tasks. One effective way is to load a manipulation arm. In this paper, we exhibit a quadruped locomotion manipulation system (LMS) named HITPhanT. This system comprises a quadruped locomotion platform and a six-degree-of-freedom manipulation arm. Besides, when the LMS moves to a designated position for operation, it is necessary to constrain the foot contact points to avoid sliding. Therefore, the foot contact point is regarded as a spherical hinge. So the locomotion platform can be considered as a parallel mechanism. A hybrid kinematics model is established by considering the serial robotic arms connecting this parallel mechanism. Besides, the trajectory planning method, which improves the system’s manipulability in evaluating the system balance, is also proposed. Finally, corresponding experiments verify the overall system’s stabilization and algorithm’s effectiveness.

Keywords

trajectory planninglocomotion manipulation systemstabilitymanipulability
Type
Research Article
Information
Robotica , Volume 41 , Issue 7 , July 2023 , pp. 2139 - 2154
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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References

Bellicoso, D., Krmer, K., Stuble, M., Sako, D. and Hutter, M., “ALMA-Articulated Locomotion and Manipulation for a Torque-Controllable Robot,” In: IEEE International Conference on Robotics and Automation (ICRA) (2019).Google Scholar
Zhou, Z., Lee, D. J., Yoshinaga, Y., Balakirsky, S., Guo, D. and Zhao, Y., “Reactive Task Allocation and Planning for Quadrupedal and Wheeled Robots Teaming,” In: 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) (2022).Google Scholar
Yang, C., Yuan, K., Zhu, Q., Yu, W. and Li, Z., “Multi-expert learning of adaptive legged locomotion,” Sci. Robot. 5(49), eabb2174 (2020).10.1126/scirobotics.abb2174CrossRefGoogle ScholarPubMed
Bouman, A., Ginting, M. F., Alatur, N., Palieri, M. and Agha-Mohammadi, A. A., “Autonomous Spot: Long-Range Autonomous Exploration of Extreme Environments with Legged Locomotion,” In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2020).CrossRefGoogle Scholar
Raibert, M., Blankespoor, K., Nelson, G. and Playter, R., “BigDog, the rough-terrain quadruped robot,” IFAC Proc. Vol. 41(2), 10822–10825 (2008).CrossRefGoogle Scholar
Wolfslag, W., Mcgreavy, C., Xin, G., Tiseo, C., Vijayakumar, S. and Li, Z., “Optimisation of Body-ground Contact for Augmenting Whole-Body Loco-manipulation of Quadruped Robots,” In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2020).Google Scholar
Shi, F., Homberger, T., Lee, J., Miki, T., Zhao, M., Farshidian, F., Okada, K., Inaba, M. and Hutter, M., “Circus ANYmal: A Quadruped Learning Dexterous Manipulation with Its Limbs,” In: 2021 IEEE International Conference on Robotics and Automation (ICRA) (2021).Google Scholar
Hooks, J., Ahn, M. S., Yu, J., Zhang, X., Zhu, T., Chae, H. and Hong, D., “ALPHRED: A multi-modal operations quadruped robot for package delivery applications,” IEEE Robot. Autom. Lett. 5(4), 5409–5416 (2020).10.1109/LRA.2020.3007482CrossRefGoogle Scholar
Kuindersma, S., Deits, R., Fallon, M., Valenzuela, A., Dai, H., Permenter, F., Koolen, T., Marion, P. and Tedrake, R., “Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot,” Auton. Robot. 40(3), 429–455 (2016).CrossRefGoogle Scholar
Gong, Y., Hartley, R., Da, X., Hereid, A., Harib, O., Huang, J. K. and Grizzle, J., “Feedback Control of a Cassie Bipedal Robot: Walking, Standing, and Riding a Segway,” In: 2019 American Control Conference (ACC) (2018).Google Scholar
Kashiri, N., Baccelliere, L., Muratore, L., Laurenzi, A., Ren, Z., Hoffman, E. M., Kamedula, M., Rigano, G. F., Malzahn, J., Cordasco, F., Guria, P., Margan, A. and Tsagarakis, N. G., “CENTAURO: A hybrid locomotion and high power resilient manipulation platform,” IEEE Robot. Autom. Lett. 4(2), 1595–1602 (2019).CrossRefGoogle Scholar
De Luca, A., Muratore, L., Raghavan, V. S., Antonucci, D. and Tsagarakis, N. G., “Autonomous obstacle crossing strategies for the hybrid wheeled-legged robot centauro,” Front. Robot. AI 8, 721001 (2021).CrossRefGoogle ScholarPubMed
Kameduła, M. and Tsagarakis, N. G., “Reactive support polygon adaptation for the hybrid legged-wheeled CENTAURO robot,” IEEE Robot. Autom. Lett. 5(2), 1734–1741 (2020).CrossRefGoogle Scholar
Sleiman, J. P., Farshidian, F., Minniti, M. V. and Hutter, M., “A unified MPC framework for whole-body dynamic locomotion and manipulation,” IEEE Robot. Autom. Lett. 6(3), 4688–4695 (2021).CrossRefGoogle Scholar
Ma, Y., Farshidian, F., Miki, T., Lee, J. and Hutter, M., “Combining learning-based locomotion policy with model-based manipulation for legged mobile manipulators,” IEEE Robot. Autom. Lett. 7(2), 2377–2384 (2022).10.1109/LRA.2022.3143567CrossRefGoogle Scholar
Yao, Q., Wang, J., Yang, S., Wang, C., Meng, L., Zhang, Q. and Wang, D., “A Transferable Legged Mobile Manipulation Framework Based on Disturbance Predictive Control,” In: 2022 IEEE International Conference on Robotics and Biomimetics (ROBIO) (2022).Google Scholar
Murphy, M. P., Stephens, B., Abe, Y. and Rizzi, A. A., “High degree-of-freedom dynamic manipulation,” Proc. SPIE - Int. Soc. Opt. Eng. 8387, 30 (2012).Google Scholar
Kuindersma, S., Deits, R., Fallon, M., Valenzuela, A., Dai, H., Permenter, F. and Tedrake, R., “Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot,” Auton. Robot. 40(3), 429–455 (2016).CrossRefGoogle Scholar
Ferrolho, H., Merkt, W., Ivan, V., Wolfslag, W. and Vijayakumar, S., “Optimizing Dynamic Trajectories for Robustness to Disturbances Using Polytopic Projections,” In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2020).Google Scholar
Xin, G., Zeng, F. and Qin, K., “Loco-manipulation control for arm-mounted quadruped robots: Dynamic and kinematic strategies,” Machines 10(8), 719 (2022).CrossRefGoogle Scholar
Fu, Z., Cheng, X. and Pathak, D., “Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotion,” arXiv preprint arXiv:221010044 (2022).Google Scholar
Zimmermann, S., Poranne, R. and Coros, S., “Go Fetch! - Dynamic Grasps using Boston Dynamics Spot with External Robotic Arm,” In: 2021 IEEE International Conference on Robotics and Automation (ICRA) (2021).Google Scholar
Zhang, G., Ma, S., Shen, Y. and Li, Y., “A motion planning approach for nonprehensile manipulation and locomotion tasks of a legged robot,” IEEE Trans. Robot. 36(3), 855–874 (2020).CrossRefGoogle Scholar
Sombolestan, M. and Nguyen, Q., “Hierarchical Adaptive Loco-manipulation Control for Quadruped Robots,” arXiv preprint arXiv:220913145 (2022).Google Scholar
Wu, Z., Chen, Y. and Xu, W., “A light space manipulator with high load-to-weight ratio: System development and compliance control,” Space Sci. Technol. 2021(1), 12 (2021).CrossRefGoogle Scholar
Peng, K., Haibin, M., Lei, Y. and Wenfu, X., “Whole Body Collaborative Planning Method for Legged Locomotion Manipulation System in Operation Process,” In: 2022 IEEE International Conference on Robotics and Biomimetics (ROBIO), Jinghong, China (2022) pp. 2098–2103.Google Scholar