Whole-body motion planning with centroidal dynamics and full kinematics

By Hongkai Dai, Andres K. Valenzuela, Russ Tedrake. IEEE-RAS International Conference on Humanoid Robots, 2014

476 citations recorded by the source corpus.

To plan dynamic, whole-body motions for robots, one conventionally faces the choice between a complex, full-body dynamic model containing every link and actuator of the robot, or a highly simplified model of the robot as a point mass. In this paper we explore a powerful middle ground between these extremes. We exploit the fact that while the full dynamics of humanoid robots are complicated, their centroidal dynamics (the evolution of the angular momentum and the center of mass (COM) position) are much simpler. By treating the dynamics of the robot in centroidal form and directly optimizing the joint trajectories for the actuated degrees of freedom, we arrive at a method that enjoys simpler dynamics, while still having the expressiveness required to handle kinematic constraints such as collision avoidance or reaching to a target. We further require that the robot's COM and angular momentum as computed from the joint trajectories match those given by the centroidal dynamics. This ensures that the dynamics considered by our optimization are equivalent to the full dynamics of the robot, provided that the robot's actuators can supply sufficient torque. We demonstrate that this algorithm is capable of generating highly-dynamic motion plans with examples of a humanoid robot negotiating obstacle course elements and gait optimization for a quadrupedal robot. Additionally, we show that we can plan without pre-specifying the contact sequence by exploiting the complementarity conditions between contact forces and contact distance.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/39768dc29f81d6ed9e2fdfd36ab73c5ec2622d92

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

In Computer Vision and Pattern Recognition

In Control and Systems Engineering

Citations in the corpus

Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots (2021, methodology): “Traditional approaches for locomotion of bipedal walking robots are typically based on notions of gait stability, such as the ZMP criterion [1] and capturability [2], simplified models [7]–[9], and constrained optimization methods [10]– [12].”
Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot (2015, background): “applied to plan quadruped gaits and complex humanoid trajectories like traversing monkey bars is available (Dai et al. 2014).”
Gait and Trajectory Optimization for Legged Systems Through Phase-Based End-Effector Parameterization (2018, background): “ear Complementary Problem (LCP), which enforces that either the foot is zero distance from the contact surface (touching the environment), or the force is zero [30]–[32].”
Whole-Body Nonlinear Model Predictive Control Through Contacts for Quadrupeds (2017, background): “In this field, centroidal dynamics approaches [5]–[9] become increasingly popular as they capture the core dynamics of the problem.”