Dynamic Locomotion Through Online Nonlinear Motion Optimization for Quadrupedal Robots

By Dario Bellicoso, Fabian Jenelten, Christian Gehring, Marco Hutter. 2018

216 citations recorded by the source corpus.

This letter presents a realtime motion planning and control method that enables a quadrupedal robot to execute dynamic gaits including trot, pace, and dynamic lateral walk, as well as gaits with full flight phases such as jumping, pronking, and running trot. The proposed method also enables smooth transitions between these gaits. Our approach relies on an online zero-moment point based motion planner which continuously updates the reference motion trajectory as a function of the contact schedule and the state of the robot. The reference footholds for each leg are obtained by solving a separate optimization problem. The resulting optimized motion plans are tracked by a hierarchical whole-body controller. Our framework has been tested in simulation and on ANYmal, a fully torque-controllable quadrupedal robot, both in simulation and on the actual robot.

Subfields: Aerospace Engineering, Biomedical Engineering, Control and Systems Engineering

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Citations in the corpus

Learning quadrupedal locomotion over challenging terrain (2020, neutral): “We have compared the presented controller to a state-of-the-art baseline (1, 27) in the forest environment.”
Learning agile and dynamic motor skills for legged robots (2019, methodology): “obots are designed to run as fast as possible whereas ANYmal is designed to be robust, reliable, and versatile. The current speed record on ANYmal is 1.2m/s and has been set using the ﬂying trot gait [12]. Although this may not seem high, it is 50% faster than the previous speed record on the platfo”
Robust Recovery Controller for a Quadrupedal Robot using Deep Reinforcement Learning (2019, background): “Optimization-based methods [17] have a hard time to solve such a task as they usually require analytic dynamic models and often predeﬁned contact sequences [1], which are both hard to ﬁnd as the system can interact at multiple uncertain contact points or patches.”
Dynamic Locomotion on Slippery Ground (2019, methodology): “We use a ZMP-based motion planner to compute the desired torso trajectory as described in [12].”