doctoral dissertation, tech. report CMU-RI-TR-15-20, Robotics Institute, Carnegie Mellon University, August, 2015
|This thesis focuses on dynamic model based state estimation for hydraulic humanoid
robots. The goal is to produce state estimates that are robust and achieve good performance
when combined with the controller.
Three issues are addressed in this thesis.
Hydraulic humanoid robots are force-controlled. It is natural for a controller to produce
force commands to the robot using inverse dynamics. Model based control and state
estimation relies on the accuracy of the model. We address the issue: “To what complexity
do we have to model the dynamics of the robot for state estimation?”. We discuss the
impact of modeling error on the robustness of the state estimators, and introduce a state
estimator based on a simple dynamics model, it is used in the DARPA Robotics Challenge
Finals for fall detection and prevention.
Hydraulic humanoids usually have force sensors on the joints and end effectors, but not
joint velocity sensors because there is no high velocity portion of the transmission as there
are no gears. A simple approach to estimate joint velocity is to differentiate measured
joint position over time and low pass filter the signal to remove noise, but it is difficult to
balance between the signal to noise ratio and delay. To address this issue, we will discuss
three ways to use the full-body dynamics model and force sensor information to estimate
joint velocities. The first method efficiently estimates the full state through decoupling.
It estimates the base variables by fusing inertial sensing with forward kinematics, and
joint variables using forward dynamics. The second method estimates the generalized
velocity using quadratic program. Force sensor information is also taken into account
as an optimization variable in this formulation. The third method uses low cost MEMS
IMUs to measure link angular velocities, and integrate that information into joint velocity
Some of these state estimators were used on the Atlas robot for full body control,
odometry and fall detection and prevention. In the DARPA Robotics Challenge Finals,
we achieved 12/14 points and had no fall or human intervention.
- How to use force sensor and IMU information in state estimation?
- How to use the full-body dynamics to estimate generalized velocity?
- How to use state estimation to handle modeling error and detect humanoid falling?
|humanoid, state estimation, control, Kalman filter, robot dynamics, sensor fusion, DRC
Ben Xinjilefu, "State Estimation for Humanoid Robots," doctoral dissertation, tech. report CMU-RI-TR-15-20, Robotics Institute, Carnegie Mellon University, August, 2015
author = "X Xinjilefu",
title = "State Estimation for Humanoid Robots",
booktitle = "",
school = "Robotics Institute, Carnegie Mellon University",
month = "August",
year = "2015",
address= "Pittsburgh, PA",