17,377 research outputs found
Automatic Differentiation of Rigid Body Dynamics for Optimal Control and Estimation
Many algorithms for control, optimization and estimation in robotics depend
on derivatives of the underlying system dynamics, e.g. to compute
linearizations, sensitivities or gradient directions. However, we show that
when dealing with Rigid Body Dynamics, these derivatives are difficult to
derive analytically and to implement efficiently. To overcome this issue, we
extend the modelling tool `RobCoGen' to be compatible with Automatic
Differentiation. Additionally, we propose how to automatically obtain the
derivatives and generate highly efficient source code. We highlight the
flexibility and performance of the approach in two application examples. First,
we show a Trajectory Optimization example for the quadrupedal robot HyQ, which
employs auto-differentiation on the dynamics including a contact model. Second,
we present a hardware experiment in which a 6 DoF robotic arm avoids a randomly
moving obstacle in a go-to task by fast, dynamic replanning
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
Contact-Implicit Trajectory Optimization Based on a Variable Smooth Contact Model and Successive Convexification
In this paper, we propose a contact-implicit trajectory optimization (CITO)
method based on a variable smooth contact model (VSCM) and successive
convexification (SCvx). The VSCM facilitates the convergence of gradient-based
optimization without compromising physical fidelity. On the other hand, the
proposed SCvx-based approach combines the advantages of direct and shooting
methods for CITO. For evaluations, we consider non-prehensile manipulation
tasks. The proposed method is compared to a version based on iterative linear
quadratic regulator (iLQR) on a planar example. The results demonstrate that
both methods can find physically-consistent motions that complete the tasks
without a meaningful initial guess owing to the VSCM. The proposed SCvx-based
method outperforms the iLQR-based method in terms of convergence, computation
time, and the quality of motions found. Finally, the proposed SCvx-based method
is tested on a standard robot platform and shown to perform efficiently for a
real-world application.Comment: Accepted for publication in ICRA 201
Angular velocity nonlinear observer from single vector measurements
The paper proposes a technique to estimate the angular velocity of a rigid
body from single vector measurements. Compared to the approaches presented in
the literature, it does not use attitude information nor rate gyros as inputs.
Instead, vector measurements are directly filtered through a nonlinear observer
estimating the angular velocity. Convergence is established using a detailed
analysis of a linear-time varying dynamics appearing in the estimation error
equation. This equation stems from the classic Euler equations and measurement
equations. As is proven, the case of free-rotation allows one to relax the
persistence of excitation assumption. Simulation results are provided to
illustrate the method.Comment: 10 pages, 8 figures. arXiv admin note: substantial text overlap with
arXiv:1503.0287
The Control Toolbox - An Open-Source C++ Library for Robotics, Optimal and Model Predictive Control
We introduce the Control Toolbox (CT), an open-source C++ library for
efficient modeling, control, estimation, trajectory optimization and Model
Predictive Control. The CT is applicable to a broad class of dynamic systems
but features interfaces to modeling tools specifically designed for robotic
applications. This paper outlines the general concept of the toolbox, its main
building blocks, and highlights selected application examples. The library
contains several tools to design and evaluate controllers, model dynamical
systems and solve optimal control problems. The CT was designed for intuitive
modeling of systems governed by ordinary differential or difference equations.
It supports rapid prototyping of cost functions and constraints and provides
standard interfaces for different optimal control solvers. To date, we support
Single Shooting, the iterative Linear-Quadratic Regulator, Gauss-Newton
Multiple Shooting and classical Direct Multiple Shooting. We provide interfaces
to general purpose NLP solvers and Riccati-based linear-quadratic optimal
control solvers. The CT was designed to solve large-scale optimal control and
estimation problems efficiently and allows for online control of dynamic
systems. Some of the key features to enable fast run-time performance are full
compatibility with Automatic Differentiation, derivative code generation, and
multi-threading. Still, the CT is designed as a modular framework whose
building blocks can also be used for other control and estimation applications
such as inverse dynamics control, extended Kalman filters or kinematic
planning
Derivative-free online learning of inverse dynamics models
This paper discusses online algorithms for inverse dynamics modelling in
robotics. Several model classes including rigid body dynamics (RBD) models,
data-driven models and semiparametric models (which are a combination of the
previous two classes) are placed in a common framework. While model classes
used in the literature typically exploit joint velocities and accelerations,
which need to be approximated resorting to numerical differentiation schemes,
in this paper a new `derivative-free' framework is proposed that does not
require this preprocessing step. An extensive experimental study with real data
from the right arm of the iCub robot is presented, comparing different model
classes and estimation procedures, showing that the proposed `derivative-free'
methods outperform existing methodologies.Comment: 14 pages, 11 figure
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