181 research outputs found
Impedence Control for Variable Stiffness Mechanisms with Nonlinear Joint Coupling
The current discussion on physical human robot
interaction and the related safety aspects, but also the interest
of neuro-scientists to validate their hypotheses on human motor
skills with bio-mimetic robots, led to a recent revival of tendondriven
robots. In this paper, the modeling of tendon-driven
elastic systems with nonlinear couplings is recapitulated. A
control law is developed that takes the desired joint position
and stiffness as input. Therefore, desired motor positions are
determined that are commanded to an impedance controller.
We give a physical interpretation of the controller. More importantly,
a static decoupling of the joint motion and the stiffness
variation is given. The combination of active (controller) and
passive (mechanical) stiffness is investigated. The controller
stiffness is designed according to the desired overall stiffness.
A damping design of the impedance controller is included in
these considerations. The controller performance is evaluated
in simulation
Sampling-Based Trajectory (re)planning for Differentially Flat Systems: Application to a 3D Gantry Crane
In this paper, a sampling-based trajectory planning algorithm for a
laboratory-scale 3D gantry crane in an environment with static obstacles and
subject to bounds on the velocity and acceleration of the gantry crane system
is presented. The focus is on developing a fast motion planning algorithm for
differentially flat systems, where intermediate results can be stored and
reused for further tasks, such as replanning. The proposed approach is based on
the informed optimal rapidly exploring random tree algorithm (informed RRT*),
which is utilized to build trajectory trees that are reused for replanning when
the start and/or target states change. In contrast to state-of-the-art
approaches, the proposed motion planning algorithm incorporates a linear
quadratic minimum time (LQTM) local planner. Thus, dynamic properties such as
time optimality and the smoothness of the trajectory are directly considered in
the proposed algorithm. Moreover, by integrating the branch-and-bound method to
perform the pruning process on the trajectory tree, the proposed algorithm can
eliminate points in the tree that do not contribute to finding better
solutions. This helps to curb memory consumption and reduce the computational
complexity during motion (re)planning. Simulation results for a validated
mathematical model of a 3D gantry crane show the feasibility of the proposed
approach.Comment: Published at IFAC-PapersOnLine (13th IFAC Symposium on Robot Control
Model Predictive Trajectory Optimization With Dynamically Changing Waypoints for Serial Manipulators
Systematically including dynamically changing waypoints as desired discrete
actions, for instance, resulting from superordinate task planning, has been
challenging for online model predictive trajectory optimization with short
planning horizons. This paper presents a novel waypoint model predictive
control (wMPC) concept for online replanning tasks. The main idea is to split
the planning horizon at the waypoint when it becomes reachable within the
current planning horizon and reduce the horizon length towards the waypoints
and goal points. This approach keeps the computational load low and provides
flexibility in adapting to changing conditions in real time. The presented
approach achieves competitive path lengths and trajectory durations compared to
(global) offline RRT-type planners in a multi-waypoint scenario. Moreover, the
ability of wMPC to dynamically replan tasks online is experimentally
demonstrated on a KUKA LBR iiwa 14 R820 robot in a dynamic pick-and-place
scenario.Comment: 8 pages, 6 figure
Singularity Avoidance with Application to Online Trajectory Optimization for Serial Manipulators
This work proposes a novel singularity avoidance approach for real-time
trajectory optimization based on known singular configurations. The focus of
this work lies on analyzing kinematically singular configurations for three
robots with different kinematic structures, i.e., the Comau Racer 7-1.4, the
KUKA LBR iiwa R820, and the Franka Emika Panda, and exploiting these
configurations in form of tailored potential functions for singularity
avoidance. Monte Carlo simulations of the proposed method and the commonly used
manipulability maximization approach are performed for comparison. The
numerical results show that the average computing time can be reduced and
shorter trajectories in both time and path length are obtained with the
proposed approachComment: 8 pages, 2 figures, Accepted for publication at IFAC World Congress
202
Machine Learning-based Framework for Optimally Solving the Analytical Inverse Kinematics for Redundant Manipulators
Solving the analytical inverse kinematics (IK) of redundant manipulators in
real time is a difficult problem in robotics since its solution for a given
target pose is not unique. Moreover, choosing the optimal IK solution with
respect to application-specific demands helps to improve the robustness and to
increase the success rate when driving the manipulator from its current
configuration towards a desired pose. This is necessary, especially in
high-dynamic tasks like catching objects in mid-flights. To compute a suitable
target configuration in the joint space for a given target pose in the
trajectory planning context, various factors such as travel time or
manipulability must be considered. However, these factors increase the
complexity of the overall problem which impedes real-time implementation. In
this paper, a real-time framework to compute the analytical inverse kinematics
of a redundant robot is presented. To this end, the analytical IK of the
redundant manipulator is parameterized by so-called redundancy parameters,
which are combined with a target pose to yield a unique IK solution. Most
existing works in the literature either try to approximate the direct mapping
from the desired pose of the manipulator to the solution of the IK or cluster
the entire workspace to find IK solutions. In contrast, the proposed framework
directly learns these redundancy parameters by using a neural network (NN) that
provides the optimal IK solution with respect to the manipulability and the
closeness to the current robot configuration. Monte Carlo simulations show the
effectiveness of the proposed approach which is accurate and real-time capable
( \SI{32}{\micro\second}) on the KUKA LBR iiwa 14 R820
Optimal control of quasi-1D Bose gases in optical box potentials
In this paper, we investigate the manipulation of quasi-1D Bose gases that
are trapped in a highly elongated potential by optimal control methods. The
effective meanfield dynamics of the gas can be described by a one-dimensional
non-polynomial Schr\"odinger equation. We extend the indirect optimal control
method for the Gross-Pitaevskii equation by Winckel and Borzi (2008) to obtain
necessary optimality conditions for state and energy cost functionals. This
approach is then applied to optimally compress a quasi-1D Bose gase in an
(optical) box potential, i.e., to find a so-called short-cut to adiabaticity,
by solving the optimality conditions numerically. The behavior of the proposed
method is finally analyzed and compared to simple direct optimization
strategies using reduced basis functions. Simulations results demonstrate the
feasibility of the proposed approach.Comment: 6 pages, 5 figures, 2 tables, accepted for IFAC World Congress 202
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