1,801 research outputs found
Whole-Body MPC for a Dynamically Stable Mobile Manipulator
Autonomous mobile manipulation offers a dual advantage of mobility provided
by a mobile platform and dexterity afforded by the manipulator. In this paper,
we present a whole-body optimal control framework to jointly solve the problems
of manipulation, balancing and interaction as one optimization problem for an
inherently unstable robot. The optimization is performed using a Model
Predictive Control (MPC) approach; the optimal control problem is transcribed
at the end-effector space, treating the position and orientation tasks in the
MPC planner, and skillfully planning for end-effector contact forces. The
proposed formulation evaluates how the control decisions aimed at end-effector
tracking and environment interaction will affect the balance of the system in
the future. We showcase the advantages of the proposed MPC approach on the
example of a ball-balancing robot with a robotic manipulator and validate our
controller in hardware experiments for tasks such as end-effector pose tracking
and door opening
Automatic Gain Tuning of a Momentum Based Balancing Controller for Humanoid Robots
This paper proposes a technique for automatic gain tuning of a momentum based
balancing controller for humanoid robots. The controller ensures the
stabilization of the centroidal dynamics and the associated zero dynamics.
Then, the closed-loop, constrained joint space dynamics is linearized and the
controller's gains are chosen so as to obtain desired properties of the
linearized system. Symmetry and positive definiteness constraints of gain
matrices are enforced by proposing a tracker for symmetric positive definite
matrices. Simulation results are carried out on the humanoid robot iCub.Comment: Accepted at IEEE-RAS International Conference on Humanoid Robots
(HUMANOIDS). 201
An Efficiently Solvable Quadratic Program for Stabilizing Dynamic Locomotion
We describe a whole-body dynamic walking controller implemented as a convex
quadratic program. The controller solves an optimal control problem using an
approximate value function derived from a simple walking model while respecting
the dynamic, input, and contact constraints of the full robot dynamics. By
exploiting sparsity and temporal structure in the optimization with a custom
active-set algorithm, we surpass the performance of the best available
off-the-shelf solvers and achieve 1kHz control rates for a 34-DOF humanoid. We
describe applications to balancing and walking tasks using the simulated Atlas
robot in the DARPA Virtual Robotics Challenge.Comment: 6 pages, published at ICRA 201
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