20,593 research outputs found
Sampling-based Motion Planning for Active Multirotor System Identification
This paper reports on an algorithm for planning trajectories that allow a
multirotor micro aerial vehicle (MAV) to quickly identify a set of unknown
parameters. In many problems like self calibration or model parameter
identification some states are only observable under a specific motion. These
motions are often hard to find, especially for inexperienced users. Therefore,
we consider system model identification in an active setting, where the vehicle
autonomously decides what actions to take in order to quickly identify the
model. Our algorithm approximates the belief dynamics of the system around a
candidate trajectory using an extended Kalman filter (EKF). It uses
sampling-based motion planning to explore the space of possible beliefs and
find a maximally informative trajectory within a user-defined budget. We
validate our method in simulation and on a real system showing the feasibility
and repeatability of the proposed approach. Our planner creates trajectories
which reduce model parameter convergence time and uncertainty by a factor of
four.Comment: Published at ICRA 2017. Video available at
https://www.youtube.com/watch?v=xtqrWbgep5
Adaptive constraints for feature tracking
In this paper extensions to an existing tracking algorithm are described.
These extensions implement adaptive tracking constraints in the form
of regional upper-bound displacements and an adaptive track smoothness
constraint. Together, these constraints make the tracking algorithm
more flexible than the original algorithm (which used fixed tracking
parameters) and provide greater confidence in the tracking results.
The result of applying the new algorithm to high-resolution ECMWF
reanalysis data is shown as an example of its effectiveness
Inferring models of bacterial dynamics toward point sources
Experiments have shown that bacteria can be sensitive to small variations in
chemoattractant (CA) concentrations. Motivated by these findings, our focus
here is on a regime rarely studied in experiments: bacteria tracking point CA
sources (such as food patches or even prey). In tracking point sources, the CA
detected by bacteria may show very large spatiotemporal fluctuations which vary
with distance from the source. We present a general statistical model to
describe how bacteria locate point sources of food on the basis of stochastic
event detection, rather than CA gradient information. We show how all model
parameters can be directly inferred from single cell tracking data even in the
limit of high detection noise. Once parameterized, our model recapitulates
bacterial behavior around point sources such as the "volcano effect". In
addition, while the search by bacteria for point sources such as prey may
appear random, our model identifies key statistical signatures of a targeted
search for a point source given any arbitrary source configuration
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