In *2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)*, pages 1–9, October, 2018. ISSN: 2153-0866

doi abstract bibtex

doi abstract bibtex

This paper presents an implementation of model predictive control (MPC) to determine ground reaction forces for a torque-controlled quadruped robot. The robot dynamics are simplified to formulate the problem as convex optimization while still capturing the full 3D nature of the system. With the simplified model, ground reaction force planning problems are formulated for prediction horizons of up to 0.5 seconds, and are solved to optimality in under 1 ms at a rate of 20-30 Hz. Despite using a simplified model, the robot is capable of robust locomotion at a variety of speeds. Experimental results demonstrate control of gaits including stand, trot, flying-trot, pronk, bound, pace, a 3-legged gait, and a full 3D gallop. The robot achieved forward speeds of up to 3 m/s, lateral speeds up to 1 m/s, and angular speeds up to 180 deg/sec. Our approach is general enough to perform all these behaviors with the same set of gains and weights.

@inproceedings{carlo_dynamic_2018, title = {Dynamic {Locomotion} in the {MIT} {Cheetah} 3 {Through} {Convex} {Model}-{Predictive} {Control}}, doi = {10.1109/IROS.2018.8594448}, abstract = {This paper presents an implementation of model predictive control (MPC) to determine ground reaction forces for a torque-controlled quadruped robot. The robot dynamics are simplified to formulate the problem as convex optimization while still capturing the full 3D nature of the system. With the simplified model, ground reaction force planning problems are formulated for prediction horizons of up to 0.5 seconds, and are solved to optimality in under 1 ms at a rate of 20-30 Hz. Despite using a simplified model, the robot is capable of robust locomotion at a variety of speeds. Experimental results demonstrate control of gaits including stand, trot, flying-trot, pronk, bound, pace, a 3-legged gait, and a full 3D gallop. The robot achieved forward speeds of up to 3 m/s, lateral speeds up to 1 m/s, and angular speeds up to 180 deg/sec. Our approach is general enough to perform all these behaviors with the same set of gains and weights.}, booktitle = {2018 {IEEE}/{RSJ} {International} {Conference} on {Intelligent} {Robots} and {Systems} ({IROS})}, author = {Carlo, J. Di and Wensing, P. M. and Katz, B. and Bledt, G. and Kim, S.}, month = oct, year = {2018}, note = {ISSN: 2153-0866}, keywords = {Convex functions, Dynamics, Legged locomotion, MIT cheetah 3, Predictive control, Predictive models, Robot kinematics, convex model-predictive control, convex optimization, convex programming, dynamic locomotion, ground reaction force planning problems, legged locomotion, predictive control, robot dynamics, torque control, torque-controlled quadruped robot}, pages = {1--9}, }

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