10,982 research outputs found
SMC framework in motion control systems
Design of a motion control system should take into account both the unconstrained motion performed without interaction with environment or other system, and the constrained motion where system is in contact with environment or has certain functional interaction with another system. In this paper control systems design approach, based on siding mode methods, that allows selection of control for generic tasks as trajectory and/or force tracking as well as for systems that require maintain some functional relation like bilateral or multilateral systems, establisment of virtual relation among mobile robots or control of haptic systems is presented. It is shown that all basic motion control problems - trajectory tracking, force control, hybrid position/force control scheme and the impedance control - can be treated in the same way while avoiding the structural change of the controller and guarantying stable behavior of the system In order to show applicability of the proposed techniques simulation and experimental results for high precision systems in microsystems assembly tasks and bilateral control systems are presente
Aerial-Ground collaborative sensing: Third-Person view for teleoperation
Rapid deployment and operation are key requirements in time critical
application, such as Search and Rescue (SaR). Efficiently teleoperated ground
robots can support first-responders in such situations. However, first-person
view teleoperation is sub-optimal in difficult terrains, while a third-person
perspective can drastically increase teleoperation performance. Here, we
propose a Micro Aerial Vehicle (MAV)-based system that can autonomously provide
third-person perspective to ground robots. While our approach is based on local
visual servoing, it further leverages the global localization of several ground
robots to seamlessly transfer between these ground robots in GPS-denied
environments. Therewith one MAV can support multiple ground robots on a demand
basis. Furthermore, our system enables different visual detection regimes, and
enhanced operability, and return-home functionality. We evaluate our system in
real-world SaR scenarios.Comment: Accepted for publication in 2018 IEEE International Symposium on
Safety, Security and Rescue Robotics (SSRR
Folding Assembly by Means of Dual-Arm Robotic Manipulation
In this paper, we consider folding assembly as an assembly primitive suitable
for dual-arm robotic assembly, that can be integrated in a higher level
assembly strategy. The system composed by two pieces in contact is modelled as
an articulated object, connected by a prismatic-revolute joint. Different
grasping scenarios were considered in order to model the system, and a simple
controller based on feedback linearisation is proposed, using force torque
measurements to compute the contact point kinematics. The folding assembly
controller has been experimentally tested with two sample parts, in order to
showcase folding assembly as a viable assembly primitive.Comment: 7 pages, accepted for ICRA 201
Torque-Controlled Stepping-Strategy Push Recovery: Design and Implementation on the iCub Humanoid Robot
One of the challenges for the robotics community is to deploy robots which
can reliably operate in real world scenarios together with humans. A crucial
requirement for legged robots is the capability to properly balance on their
feet, rejecting external disturbances. iCub is a state-of-the-art humanoid
robot which has only recently started to balance on its feet. While the current
balancing controller has proved successful in various scenarios, it still
misses the capability to properly react to strong pushes by taking steps. This
paper goes in this direction. It proposes and implements a control strategy
based on the Capture Point concept [1]. Instead of relying on position control,
like most of Capture Point related approaches, the proposed strategy generates
references for the momentum-based torque controller already implemented on the
iCub, thus extending its capabilities to react to external disturbances, while
retaining the advantages of torque control when interacting with the
environment. Experiments in the Gazebo simulator and on the iCub humanoid robot
validate the proposed strategy
A Real-Time Solver For Time-Optimal Control Of Omnidirectional Robots with Bounded Acceleration
We are interested in the problem of time-optimal control of omnidirectional
robots with bounded acceleration (TOC-ORBA). While there exist approximate
solutions for such robots, and exact solutions with unbounded acceleration,
exact solvers to the TOC-ORBA problem have remained elusive until now. In this
paper, we present a real-time solver for true time-optimal control of
omnidirectional robots with bounded acceleration. We first derive the general
parameterized form of the solution to the TOC-ORBA problem by application of
Pontryagin's maximum principle. We then frame the boundary value problem of
TOC-ORBA as an optimization problem over the parametrized control space. To
overcome local minima and poor initial guesses to the optimization problem, we
introduce a two-stage optimal control solver (TSOCS): The first stage computes
an upper bound to the total time for the TOC-ORBA problem and holds the time
constant while optimizing the parameters of the trajectory to approach the
boundary value conditions. The second stage uses the parameters found by the
first stage, and relaxes the constraint on the total time to solve for the
parameters of the complete TOC-ORBA problem. We further implement TSOCS as a
closed loop controller to overcome actuation errors on real robots in
real-time. We empirically demonstrate the effectiveness of TSOCS in simulation
and on real robots, showing that 1) it runs in real time, generating solutions
in less than 0.5ms on average; 2) it generates faster trajectories compared to
an approximate solver; and 3) it is able to solve TOC-ORBA problems with
non-zero final velocities that were previously unsolvable in real-time
Momentum Control with Hierarchical Inverse Dynamics on a Torque-Controlled Humanoid
Hierarchical inverse dynamics based on cascades of quadratic programs have
been proposed for the control of legged robots. They have important benefits
but to the best of our knowledge have never been implemented on a torque
controlled humanoid where model inaccuracies, sensor noise and real-time
computation requirements can be problematic. Using a reformulation of existing
algorithms, we propose a simplification of the problem that allows to achieve
real-time control. Momentum-based control is integrated in the task hierarchy
and a LQR design approach is used to compute the desired associated closed-loop
behavior and improve performance. Extensive experiments on various balancing
and tracking tasks show very robust performance in the face of unknown
disturbances, even when the humanoid is standing on one foot. Our results
demonstrate that hierarchical inverse dynamics together with momentum control
can be efficiently used for feedback control under real robot conditions.Comment: 21 pages, 11 figures, 4 tables in Autonomous Robots (2015
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