58,173 research outputs found
Nonlinear Model Predictive Control for Constrained Output Path Following
We consider the tracking of geometric paths in output spaces of nonlinear
systems subject to input and state constraints without pre-specified timing
requirements. Such problems are commonly referred to as constrained output
path-following problems. Specifically, we propose a predictive control approach
to constrained path-following problems with and without velocity assignments
and provide sufficient convergence conditions based on terminal regions and end
penalties. Furthermore, we analyze the geometric nature of constrained output
path-following problems and thereby provide insight into the computation of
suitable terminal control laws and terminal regions. We draw upon an example
from robotics to illustrate our findings.Comment: 12 pages, 4 figure
Wing and body motion during flight initiation in Drosophila revealed by automated visual tracking
The fruit fly Drosophila melanogaster is a widely used model organism in studies of genetics, developmental biology and biomechanics. One limitation for exploiting Drosophila as a model system for behavioral neurobiology is that measuring body kinematics during behavior is labor intensive and subjective. In order to quantify flight kinematics during different types of maneuvers, we have developed a visual tracking system that estimates the posture of the fly from multiple calibrated cameras. An accurate geometric fly model is designed using unit quaternions to capture complex body and wing rotations, which are automatically fitted to the images in each time frame. Our approach works across a range of flight behaviors, while also being robust to common environmental clutter. The tracking system is used in this paper to compare wing and body motion during both voluntary and escape take-offs. Using our automated algorithms, we are able to measure stroke amplitude, geometric angle of attack and other parameters important to a mechanistic understanding of flapping flight. When compared with manual tracking methods, the algorithm estimates body position within 4.4±1.3% of the body length, while body orientation is measured within 6.5±1.9 deg. (roll), 3.2±1.3 deg. (pitch) and 3.4±1.6 deg. (yaw) on average across six videos. Similarly, stroke amplitude and deviation are estimated within 3.3 deg. and 2.1 deg., while angle of attack is typically measured within 8.8 deg. comparing against a human digitizer. Using our automated tracker, we analyzed a total of eight voluntary and two escape take-offs. These sequences show that Drosophila melanogaster do not utilize clap and fling during take-off and are able to modify their wing kinematics from one wingstroke to the next. Our approach should enable biomechanists and ethologists to process much larger datasets than possible at present and, therefore, accelerate insight into the mechanisms of free-flight maneuvers of flying insects
An optimal control approach to cell tracking
Cell tracking is of vital importance in many biological studies, hence robust cell tracking algorithms are needed for inference of dynamic features from (static) in vivo and in vitro experimental imaging data of cells migrating.
In recent years much attention has been focused on the modelling of cell motility from physical principles and the development of state-of-the art numerical methods for the simulation of the model equations. Despite this, the vast majority of cell tracking algorithms proposed to date focus solely on the imaging data itself and do not attempt to incorporate any physical knowledge on cell migration into the tracking procedure.
In this study, we present a mathematical approach for cell tracking, in which we formulate the cell tracking problem as an inverse problem for fitting a mathematical model for cell motility to experimental imaging data. The novelty of this approach is that the physics underlying the model for cell migration is encoded in the tracking algorithm. To illustrate this we focus on an example of Zebrafish (Danio rerio's larvae) Neutrophil migration and contrast an ad-hoc approach to cell tracking based on interpolation with the model fitting approach we propose in this study
Time-Optimal Path Tracking via Reachability Analysis
Given a geometric path, the Time-Optimal Path Tracking problem consists in
finding the control strategy to traverse the path time-optimally while
regulating tracking errors. A simple yet effective approach to this problem is
to decompose the controller into two components: (i)~a path controller, which
modulates the parameterization of the desired path in an online manner,
yielding a reference trajectory; and (ii)~a tracking controller, which takes
the reference trajectory and outputs joint torques for tracking. However, there
is one major difficulty: the path controller might not find any feasible
reference trajectory that can be tracked by the tracking controller because of
torque bounds. In turn, this results in degraded tracking performances. Here,
we propose a new path controller that is guaranteed to find feasible reference
trajectories by accounting for possible future perturbations. The main
technical tool underlying the proposed controller is Reachability Analysis, a
new method for analyzing path parameterization problems. Simulations show that
the proposed controller outperforms existing methods.Comment: 6 pages, 3 figures, ICRA 201
Implementation of Nonlinear Model Predictive Path-Following Control for an Industrial Robot
Many robotic applications, such as milling, gluing, or high precision
measurements, require the exact following of a pre-defined geometric path. In
this paper, we investigate the real-time feasible implementation of model
predictive path-following control for an industrial robot. We consider
constrained output path following with and without reference speed assignment.
We present results from an implementation of the proposed model predictive
path-following controller on a KUKA LWR IV robot.Comment: 8 pages, 3 figures; final revised versio
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