620 research outputs found
Dynamical modeling of collective behavior from pigeon flight data: flock cohesion and dispersion
Several models of flocking have been promoted based on simulations with
qualitatively naturalistic behavior. In this paper we provide the first direct
application of computational modeling methods to infer flocking behavior from
experimental field data. We show that this approach is able to infer general
rules for interaction, or lack of interaction, among members of a flock or,
more generally, any community. Using experimental field measurements of homing
pigeons in flight we demonstrate the existence of a basic distance dependent
attraction/repulsion relationship and show that this rule is sufficient to
explain collective behavior observed in nature. Positional data of individuals
over time are used as input data to a computational algorithm capable of
building complex nonlinear functions that can represent the system behavior.
Topological nearest neighbor interactions are considered to characterize the
components within this model. The efficacy of this method is demonstrated with
simulated noisy data generated from the classical (two dimensional) Vicsek
model. When applied to experimental data from homing pigeon flights we show
that the more complex three dimensional models are capable of predicting and
simulating trajectories, as well as exhibiting realistic collective dynamics.
The simulations of the reconstructed models are used to extract properties of
the collective behavior in pigeons, and how it is affected by changing the
initial conditions of the system. Our results demonstrate that this approach
may be applied to construct models capable of simulating trajectories and
collective dynamics using experimental field measurements of herd movement.
From these models, the behavior of the individual agents (animals) may be
inferred
Applications of Biological Cell Models in Robotics
In this paper I present some of the most representative biological models
applied to robotics. In particular, this work represents a survey of some
models inspired, or making use of concepts, by gene regulatory networks (GRNs):
these networks describe the complex interactions that affect gene expression
and, consequently, cell behaviour
Geographic Centroid Routing for Vehicular Networks
A number of geolocation-based Delay Tolerant Networking (DTN) routing
protocols have been shown to perform well in selected simulation and mobility
scenarios. However, the suitability of these mechanisms for vehicular networks
utilizing widely-available inexpensive Global Positioning System (GPS) hardware
has not been evaluated. We propose a novel geolocation-based routing primitive
(Centroid Routing) that is resilient to the measurement errors commonly present
in low-cost GPS devices. Using this notion of Centroids, we construct two novel
routing protocols and evaluate their performance with respect to positional
errors as well as traditional DTN routing metrics. We show that they outperform
existing approaches by a significant margin.Comment: 6 page
Inferring the dynamics of underdamped stochastic systems
Many complex systems, ranging from migrating cells to animal groups, exhibit
stochastic dynamics described by the underdamped Langevin equation. Inferring
such an equation of motion from experimental data can provide profound insight
into the physical laws governing the system. Here, we derive a principled
framework to infer the dynamics of underdamped stochastic systems from
realistic experimental trajectories, sampled at discrete times and subject to
measurement errors. This framework yields an operational method, Underdamped
Langevin Inference (ULI), which performs well on experimental trajectories of
single migrating cells and in complex high-dimensional systems, including
flocks with Viscek-like alignment interactions. Our method is robust to
experimental measurement errors, and includes a self-consistent estimate of the
inference error
Swarming Reconnaissance Using Unmanned Aerial Vehicles in a Parallel Discrete Event Simulation
Current military affairs indicate that future military warfare requires safer, more accurate, and more fault-tolerant weapons systems. Unmanned Aerial Vehicles (UAV) are one answer to this military requirement. Technology in the UAV arena is moving toward smaller and more capable systems and is becoming available at a fraction of the cost. Exploiting the advances in these miniaturized flying vehicles is the aim of this research. How are the UAVs employed for the future military? The concept of operations for a micro-UAV system is adopted from nature from the appearance of flocking birds, movement of a school of fish, and swarming bees among others. All of these natural phenomena have a common thread: a global action resulting from many small individual actions. This emergent behavior is the aggregate result of many simple interactions occurring within the flock, school, or swarm. In a similar manner, a more robust weapon system uses emergent behavior resulting in no weakest link because the system itself is made up of simple interactions by hundreds or thousands of homogeneous UAVs. The global system in this research is referred to as a swarm. Losing one or a few individual unmanned vehicles would not dramatically impact the swarms ability to complete the mission or cause harm to any human operator. Swarming reconnaissance is the emergent behavior of swarms to perform a reconnaissance operation. An in-depth look at the design of a reconnaissance swarming mission is studied. A taxonomy of passive reconnaissance applications is developed to address feasibility. Evaluation of algorithms for swarm movement, communication, sensor input/analysis, targeting, and network topology result in priorities of each model\u27s desired features. After a thorough selection process of available implementations, a subset of those models are integrated and built upon resulting in a simulation that explores the innovations of swarming UAVs
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