12 research outputs found
Detecci贸n y recogida de muestras por veh铆culos de exploraci贸n planetaria
Las futuras misiones de exploraci贸n planetaria exigen cada vez m谩s autonom铆a, ya que estas misiones son cada vez m谩s complejas. Un ejemplo claro es la misi贸n de retorno de muestras a Marte, en la que el Sample Fetch Rover debe recoger tubos de muestras en un lugar remoto y llevarlos de vuelta a la estaci贸n base para lanzarlos a la Tierra. Esta misi贸n requiere ampliar las capacidades aut贸nomas a bordo. En primer lugar, el componente de navegaci贸n debe ser capaz de detectar y localizar los tubos de muestra, y en segundo lugar, los de guiado y control deben situar el rover cerca de los tubos de muestra y mover el manipulador para recogerlos. Estas son las principales aportaciones de este trabajo. La primera cuesti贸n se ha resuelto mediante el uso de Redes Neuronales Profundas, que permiten identificar los tubos de muestra previamente entrenados en im谩genes, y la segunda se ha resuelto ampliando el algoritmo de planificaci贸n de trayectorias dentro del componente de Guiado.
Para demostrar y validar los m茅todos propuestos, se han realizado dos experimentos. Una primera prueba de campo en el terreno experimental de B煤squeda y Rescate de la Universidad de M谩laga, y una segunda prueba de laboratorio en el Laboratorio de Rob贸tica Planetaria de la Agencia Espacial Europea. Ambos experimentos se llevaron a cabo utilizando el Rover de Pruebas ExoMars, propiedad de esta 煤ltima instituci贸n.Universidad de M谩laga. Campus de Excelencia Internacional Andaluc铆a Tech
Modelling and Control of a Hybrid Wheeled Jumping Robot
In this paper, we study a wheeled robot with a prismatic extension joint.
This allows the robot to build up momentum to perform jumps over obstacles and
to swing up to the upright position after the loss of balance. We propose a
template model for the class of such two-wheeled jumping robots. This model can
be considered as the simplest wheeled-legged system. We provide an analytical
derivation of the system dynamics which we use inside a model predictive
controller (MPC). We study the behavior of the model and demonstrate highly
dynamic motions such as swing-up and jumping. Furthermore, these motions are
discovered through optimization from first principles. We evaluate the
controller on a variety of tasks and uneven terrains in a simulator.Comment: 8 pages, 11 figures, IROS 2020, Video URL:
https://youtu.be/j2sIWL8m2p
A Family of Iterative Gauss-Newton Shooting Methods for Nonlinear Optimal Control
This paper introduces a family of iterative algorithms for unconstrained
nonlinear optimal control. We generalize the well-known iLQR algorithm to
different multiple-shooting variants, combining advantages like
straight-forward initialization and a closed-loop forward integration. All
algorithms have similar computational complexity, i.e. linear complexity in the
time horizon, and can be derived in the same computational framework. We
compare the full-step variants of our algorithms and present several simulation
examples, including a high-dimensional underactuated robot subject to contact
switches. Simulation results show that our multiple-shooting algorithms can
achieve faster convergence, better local contraction rates and much shorter
runtimes than classical iLQR, which makes them a superior choice for nonlinear
model predictive control applications.Comment: 8 page
Design and Motion Planning for a Reconfigurable Robotic Base
A robotic platform for mobile manipulation needs to satisfy two contradicting
requirements for many real-world applications: A compact base is required to
navigate through cluttered indoor environments, while the support needs to be
large enough to prevent tumbling or tip over, especially during fast
manipulation operations with heavy payloads or forceful interaction with the
environment. This paper proposes a novel robot design that fulfills both
requirements through a versatile footprint. It can reconfigure its footprint to
a narrow configuration when navigating through tight spaces and to a wide
stance when manipulating heavy objects. Furthermore, its triangular
configuration allows for high-precision tasks on uneven ground by preventing
support switches. A model predictive control strategy is presented that unifies
planning and control for simultaneous navigation, reconfiguration, and
manipulation. It converts task-space goals into whole-body motion plans for the
new robot. The proposed design has been tested extensively with a hardware
prototype. The footprint reconfiguration allows to almost completely remove
manipulation-induced vibrations. The control strategy proves effective in both
lab experiment and during a real-world construction task.Comment: 8 pages, accepted for RA-L and IROS 202
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
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