Abstract
This paper proposes a control framework for optimizing both ground reaction forces and new footholds in real-time through a model predictive control technique by parameterizing the foot position while constructing dynamics of a 3-dimensional single rigid body model. The method could substitute the conventional foothold controller based on linear inverted pendulum model such as capture point and be applied to balance dynamic locomotion of a robot on non-flat terrains. Furthermore, the controller is verified in various simulation environments, where foothold optimization is crucial
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References
Borrelli, F., Bemporad, A., Morari, M.: Predictive Control for Linear and Hybrid Systems. Cambridge University Press, Cambridge (2017)
Diedam, H., et al.: Online walking gait generation with adaptive foot positioning through linear model predictive control. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE (2008)
Neunert, M., et al.: Whole-body nonlinear model predictive control through contacts for quadrupeds. IEEE Robot. Autom. Lett. 3(3), 1458–1465 (2018)
Raibert, M.H.: Legged Robots that Balance. MIT Press, Cambridge (1986)
Pratt, J., Carff, J., Drakunov, S., Goswami, A.: Capture point: a step toward humanoid push recovery. In: 2006 6th IEEE-RAS International Conference on Humanoid Robots. IEEE (2006)
Kajita, S., et al.: Biped walking pattern generation by using preview control of zero-moment point. In: 2003 IEEE ICRA, vol. 2. IEEE (2003)
Diedam, H., Dimitrov, D., Wieber, P.B., Mombaur, K., Diehl, M.: Online walking gait generation with adaptive foot positioning through linear model predictive control. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE (2008)
Bledt, G., Wensing, P.M., Kim, S.: Policy-regularized model predictive control to stabilize diverse quadrupedal gaits for the MIT cheetah. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2017)
Hong, S., Kim, J.-H., Park, H.-W.: Real-time constrained nonlinear model predictive control on SO (3) for dynamic legged locomotion (2020)
Ding, Y., Pandala, A., Li, C., Shin, Y.-H., Park, H.-W.: Representation-free model predictive control for dynamic motions in quadrupeds. IEEE Trans. Robot. 37(4), 1154–1171 (2021)
Di Carlo, J., Wensing, P.M., Katz, B., Bledt, G., Kim, S.: Dynamic locomotion in the MIT cheetah 3 through convex model-predictive control. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2018)
Wu, G., Sreenath, K.: Variation-based linearization of nonlinear systems evolving on \( SO (3) \) and \(\mathbb{S}^{2} \). IEEE Access 3, 1592–1604 (2015)
Forster, C., Carlone, L., Dellaert, F., Scaramuzza, D.: On-manifold preintegration for real-time visual-inertial odometry. IEEE Trans. Robot. 33(1), 1–21 (2016)
Chignoli, M., Wensing, P.M.: Variational-based optimal control of underactuated balancing for dynamic quadrupeds. IEEE Access 8, 49785–49797 (2020)
Kim, J.-H., et al.: Legged robot state estimation with dynamic contact event information. IEEE Robot. Autom. Lett. 6(4), 6733–6740 (2021)
Pandala, A.G., Ding, Y., Park, H.: qpSWIFT: a real-time sparse quadratic program solver for robotic applications. IEEE Robot. Autom. Lett. 4(4), 3355–3362 (2019)
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Kim, MG., Hong, S., Kim, JH., Park, HW. (2022). Design of Foothold Decision Model in Convex Model Predictive Control for Legged Robots. In: Kim, J., et al. Robot Intelligence Technology and Applications 6. RiTA 2021. Lecture Notes in Networks and Systems, vol 429. Springer, Cham. https://doi.org/10.1007/978-3-030-97672-9_3
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DOI: https://doi.org/10.1007/978-3-030-97672-9_3
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