Event Location: GHC 8102

Abstract: This proposal 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 proposal.

1. How to handle modelling error using state estimation?
2. How to use force sensor and IMU information in state estimation?
3. How to use the full-body dynamics to estimate generalized velocity?

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.

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 two 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 using forward kinematics and joint variables using forward dynamics. The second method estimates the generalized velocity using an quadratic program-based optimization approach. Force sensor information is also taken into account as an optimization variable in this formulation.

Committee:Christopher G. Atkeson, Chair

Hartmut Geyer

Alonzo Kelly

Hannah Michalska, McGill University