Dynamic motion of quadrupedal robots on challenging terrain: a kinodynamic optimization approach
Qi LI, Lei DING, Xin LUO
Dynamic motion of quadrupedal robots on challenging terrain: a kinodynamic optimization approach
The dynamic motion of quadrupedal robots on challenging terrain generally requires elaborate spatial–temporal kinodynamic motion planning and accurate control at higher refresh rate in comparison with regular terrain. However, conventional quadrupedal robots usually generate relatively coarse planning and employ motion replanning or reactive strategies to handle terrain irregularities. The resultant complex and computation-intensive controller may lead to nonoptimal motions or the breaking of locomotion rhythm. In this paper, a kinodynamic optimization approach is presented. To generate long-horizon optimal predictions of the kinematic and dynamic behavior of the quadruped robot on challenging terrain, we formulate motion planning as an optimization problem; jointly treat the foot’s locations, contact forces, and torso motions as decision variables; combine smooth motion and minimal energy consumption as the objective function; and explicitly represent feasible foothold region and friction constraints based on terrain information. To track the generated motions accurately and stably, we employ a whole-body controller to compute reference position and velocity commands, which are fed forward to joint controllers of the robot’s legs. We verify the effectiveness of the developed approach through simulation and on a physical quadruped robot testbed. Results show that the quadruped robot can successfully traverse a 30° slope and 43% of nominal leg length high step while maintaining the rhythm of dynamic trot gait.
quadrupedal robot / kinodynamic planning / nonlinear optimization / challenging terrain / whole-body control
[1] |
Wilshin S, Reeve M A, Spence A J. Dog galloping on rough terrain exhibits similar limb coordination patterns and gait variability to that on flat terrain. Bioinspiration & Biomimetics, 2021, 16(1): 015001
CrossRef
Google scholar
|
[2] |
Liu Q Y, Chen X D, Han B, Luo Z W, Luo X. Learning control of quadruped robot galloping. Journal of Bionic Engineering, 2018, 15(2): 329–340
CrossRef
Google scholar
|
[3] |
Zhang G T, Li Y B, Ma S G. Trotting and pacing locomotion of a position-controlled quadruped robot. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Prague: IEEE, 2021, 7456–7463
|
[4] |
Spröwitz A, Tuleu A, Vespignani M, Ajallooeian M, Badri E, Ijspeert A J. Towards dynamic trot gait locomotion: design, control, and experiments with Cheetah-cub, a compliant quadruped robot. The International Journal of Robotics Research, 2013, 32(8): 932–950
CrossRef
Google scholar
|
[5] |
Koo I M, Trong T D, Lee Y H, Moon H, Koo J, Park S, Choi H R. Biologically inspired gait transition control for a quadruped walking robot. Autonomous Robots, 2015, 39(2): 169–182
CrossRef
Google scholar
|
[6] |
Mercy T, Van Parys R, Pipeleers G. Spline-based motion planning for autonomous guided vehicles in a dynamic environment. IEEE Transactions on Control Systems Technology, 2018, 26(6): 2182–2189
CrossRef
Google scholar
|
[7] |
Fukui T, Fujisawa H, Otaka K, Fukuoka Y. Autonomous gait transition and galloping over unperceived obstacles of a quadruped robot with CPG modulated by vestibular feedback. Robotics and Autonomous Systems, 2019, 111: 1–19
CrossRef
Google scholar
|
[8] |
Roennau A, Heppner G, Nowicki M, Zoellner J M, Dillmann R. Reactive posture behaviors for stable legged locomotion over steep inclines and large obstacles. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Chicago: IEEE, 2014, 4888–4894
|
[9] |
Barasuol V, Buchli J, Semini C, Frigerio M, De Pieri E R, Caldwell D G. A reactive controller framework for quadrupedal locomotion on challenging terrain. In: Proceedings of the IEEE International Conference on Robotics and Automation. Karlsruhe: IEEE, 2013, 2554–2561
|
[10] |
Xin G Y, Lin H C, Smith J, Cebe O, Mistry M. A model-based hierarchical controller for legged systems subject to external disturbances. In: Proceedings of the IEEE International Conference on Robotics and Automation. Brisbane: IEEE, 2018, 4375–4382
|
[11] |
Li Q, Sun P, Zhao C L, Luo X. Adaptive strategies for quadruped robot to climb high-slope terrain without priori information. In: Proceedings of the IEEE International Conference on Robotics and Biomimetics. Jinghong: IEEE, 2022, 1396–1401
|
[12] |
Zico Kolter J, Ng A Y. The Stanford littledog: a learning and rapid replanning approach to quadruped locomotion. The International Journal of Robotics Research, 2011, 30(2): 150–174
CrossRef
Google scholar
|
[13] |
Zucker M, Ratliff N, Stolle M, Chestnutt J, Bagnell J A, Atkeson C G, Kuffner J. Optimization and learning for rough terrain legged locomotion. The International Journal of Robotics Research, 2011, 30(2): 175–191
CrossRef
Google scholar
|
[14] |
Kalakrishnan M, Buchli J, Pastor P, Mistry M, Schaal S. Learning, planning, and control for quadruped locomotion over challenging terrain. The International Journal of Robotics Research, 2011, 30(2): 236–258
CrossRef
Google scholar
|
[15] |
Jenelten F, Miki T, Vijayan A E, Bjelonic M, Hutter M. Perceptive locomotion in rough terrain––online foothold optimization. IEEE Robotics and Automation Letters, 2020, 5(4): 5370–5376
CrossRef
Google scholar
|
[16] |
Chen L, Ye S S, Sun C M, Zhang A D, Deng G Y, Liao T J. Optimized foothold planning and posture searching for energy-efficient quadruped locomotion over challenging terrains. In: Proceedings of the IEEE International Conference on Robotics and Automation. Paris: IEEE, 2020, 399–405
|
[17] |
Bellicoso C D, Jenelten F, Gehring C, Hutter M. Dynamic locomotion through online nonlinear motion optimization for quadrupedal robots. IEEE Robotics and Automation Letters, 2018, 3(3): 2261–2268
CrossRef
Google scholar
|
[18] |
Fankhauser P, Bjelonic M, Bellicoso C D, Miki T, Hutter M. Robust rough-terrain locomotion with a quadrupedal robot. In: Proceedings of the IEEE International Conference on Robotics and Automation. Brisbane: IEEE, 2018, 5761–5768
|
[19] |
Aceituno-Cabezas B, Mastalli C, Dai H K, Focchi M, Radulescu A, Caldwell D G, Cappelletto J, Grieco J C, Fernández-López G, Semini C. Simultaneous contact, gait, and motion planning for robust multilegged locomotion via mixed-integer convex optimization. IEEE Robotics and Automation Letters, 2018, 3(3): 2531–2538
CrossRef
Google scholar
|
[20] |
Bjelonic M, Sankar P K, Bellicoso C D, Vallery H, Hutter M. Rolling in the deep––hybrid locomotion for wheeled-legged robots using online trajectory optimization. IEEE Robotics and Automation Letters, 2020, 5(2): 3626–3633
CrossRef
Google scholar
|
[21] |
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: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Madrid: IEEE, 2018, 1–9
|
[22] |
Bledt G, Kim S. Implementing regularized predictive control for simultaneous real-time footstep and ground reaction force optimization. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Macau: IEEE, 2019, 6316–6323
|
[23] |
Grandia R, Farshidian F, Ranftl R, Hutter M. Feedback MPC for torque-controlled legged robots. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Macau: IEEE, 2019, 4730–4737
|
[24] |
Neunert M, Stäuble M, Giftthaler M, Bellicoso C D, Carius J, Gehring C, Hutter M, Buchli J. Whole-body nonlinear model predictive control through contacts for quadrupeds. IEEE Robotics and Automation Letters, 2018, 3(3): 1458–1465
CrossRef
Google scholar
|
[25] |
Jenelten F, Grandia R, Farshidian F, Hutter M. TAMOLS: terrain-aware motion optimization for legged systems. IEEE Transactions on Robotics, 2022, 38(6): 3395–3413
CrossRef
Google scholar
|
[26] |
Winkler A W, Bellicoso C D, Hutter M, Buchli J. Gait and trajectory optimization for legged systems through phase-based end-effector parameterization. IEEE Robotics and Automation Letters, 2018, 3(3): 1560–1567
CrossRef
Google scholar
|
[27] |
Bjelonic M, Grandia R, Geilinger M, Harley O, Medeiros V S, Pajovic V, Jelavic E, Coros S, Hutter M. Offline motion libraries and online MPC for advanced mobility skills. The International Journal of Robotics Research, 2022, 41(9–10): 903–924
CrossRef
Google scholar
|
[28] |
Li Q, Qian L T, Sun P, Luo X. Energy-efficient dynamic motion planning of quadruped robots via whole-body nonlinear trajectory optimization. In: Proceedings of the IEEE International Conference on Mechatronics and Automation. Guilin: IEEE, 2022, 1610–1615
|
[29] |
Li Q, Qian L T, Wang S H, Sun P, Luo X. Towards generation and transition of diverse gaits for quadrupedal robots based on trajectory optimization and whole-body impedance control. IEEE Robotics and Automation Letters, 2023, 8(4): 2389–2396
CrossRef
Google scholar
|
[30] |
Siciliano B, Khatib O. Springer Handbook of Robotics. 2nd ed. Cham: Springer, 2016, 11–36
|
[31] |
Andersson J A E, Gillis J, Horn G, Rawlings J B, Diehl M. CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 2019, 11(1): 1–36
CrossRef
Google scholar
|
[32] |
Wächter A, Biegler L T. On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming, 2006, 106(1): 25–57
CrossRef
Google scholar
|
[33] |
Felis M L. RBDL: an efficient rigid-body dynamics library using recursive algorithms. Autonomous Robots, 2017, 41(2): 495–511
CrossRef
Google scholar
|
[34] |
DiehlMBock H GDiedamHWieberP B. Fast direct multiple shooting algorithms for optimal robot control. In: Diehl M, Mombaur K, eds. Fast Motions in Biomechanics and Robotics: Optimization and Feedback Control. Heidelberg: Springer, 2006, 65–93
|
[35] |
Norby J, Yang Y, Tajbakhsh A, Ren J, Yim J K, Stutt A, Yu Q, Flowers N, Johnson A M. Quad-SDK: full stack software framework for agile quadrupedal locomotion. In ICRA Workshop on Legged Robots, 2022
|
/
〈 | 〉 |