Optimization based full body control for the atlas robot

This paper is about improving joint and actuator velocity estimates on a human-sized hydraulic humanoid robot by adding a network of inexpensive microelectromechanical systems (MEMS) gyroscopes. Due to the lack of joint velocity sensors on the majority of humanoid robots, the joint velocity estimates often become a limiting factor on the controller performance. The distributed gyroscopes serve as indirect sensors of the joint velocities that can be estimated by a Kalman filter. Using this framework, we achieve higher velocity gains at the center of mass level on an Atlas hydraulic humanoid robot, which translates into better control performance.

show abstract

“…Details about our controller can be found in [11] [12]. Essentially, we track the desired Center of Mass (CoM) trajectory in the following waÿ…”

Section: Controllermentioning

confidence: 99%

A distributed MEMS gyro network for joint velocity estimation

Xinjilefu

Feng

Atkeson

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

Self Cite

View full text Add to dashboard Cite

show abstract

“…1). In order to track the CoM and footstep trajectory, we use an optimization-based whole-body inverse dynamics controller formulated as a QP, similar to [17], [18], [19], to resolve for joint torques at 1kHz. To track the desired forces, we implemented a simple force feedback policy that was added to the resulting joint torques from the whole-body QP solver (τ qp ):…”

Section: Maximum Push Experimentsmentioning

confidence: 99%

An MPC Walking Framework with External Contact Forces

Mason¹,

Rotella²,

Schaal³

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

In this work, we present an extension to a linear Model Predictive Control (MPC) scheme that plans external contact forces for the robot when given multiple contact locations and their corresponding friction cone. To this end, we set up a two-step optimization problem. In the first optimization, we compute the Center of Mass (CoM) trajectory, foot step locations, and introduce slack variables to account for violating the imposed constraints on the Zero Moment Point (ZMP). We then use the slack variables to trigger the second optimization, in which we calculate the optimal external force that compensates for the ZMP tracking error. This optimization considers multiple contacts positions within the environment by formulating the problem as a Mixed Integer Quadratic Program (MIQP) that can be solved at a speed between 100-300 Hz. Once contact is created, the MIQP reduces to a single Quadratic Program (QP) that can be solved in realtime (< 1kHz). Simulations show that the presented walking control scheme can withstand disturbances 2-3x larger with the additional force provided by a hand contact.

show abstract

“…The reference motion is usually generated with an MPC scheme independently from the actual robot motion [11]. In that case, since the set Z is robust invariant, condition (30) is recursively satisfied and robust recursive feasibility is ensured for all v ∈ W (see prop.2 in [16]).…”

Section: Mpc Scheme For Biped Walkingmentioning

confidence: 99%

“…The reference motion is usually generated with an MPC scheme independently from the actual robot motion [11]. We will refer to this approach as Open Loop MPC (OL-MPC).…”

Section: Introductionmentioning

confidence: 99%

Model predictive control of biped walking with bounded uncertainties

Villa

Wieber

2017

2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)

View full text Add to dashboard Cite

Abstract-A biped walking controller for humanoid robots has to handle together hard constraints, dynamic environments, and uncertainties. Model Predictive Control (MPC) is a suitable and widely used control method to handle the first two issues. Uncertainties on the robot imply a non-zero tracking error when trying to follow a reference motion. A standard solution for this issue is to use tighter constraints by introducing some hand tuned safety margins, for the reference motion generation to ensure that the actual robot motion will satisfy all constraints even in presence of the tracking error. In this article, we find bounds for the tracking error and we show how such safety margins can be precisely computed from the tracking error bounds. Also, a tracking control gain is proposed to reduce the restrictiveness introduced with the safety margins. MPC with these considerations ensure the correct operation of the biped robot under a given degree of uncertainties when it is implemented in open-loop. Nevertheless, the straightforward way to implement an MPC closed-loop scheme fails. We discuss the reasons for this failure and propose a robust closed-loop MPC scheme.

show abstract

Optimization based full body control for the atlas robot

Cited by 144 publications

References 22 publications

A distributed MEMS gyro network for joint velocity estimation

A distributed MEMS gyro network for joint velocity estimation

An MPC Walking Framework with External Contact Forces

Model predictive control of biped walking with bounded uncertainties

Contact Info

Product

Resources

About