3,068 research outputs found
3-D Velocity Regulation for Nonholonomic Source Seeking Without Position Measurement
We consider a three-dimensional problem of steering a nonholonomic vehicle to
seek an unknown source of a spatially distributed signal field without any
position measurement. In the literature, there exists an extremum seeking-based
strategy under a constant forward velocity and tunable pitch and yaw
velocities. Obviously, the vehicle with a constant forward velocity may exhibit
certain overshoots in the seeking process and can not slow down even it
approaches the source. To resolve this undesired behavior, this paper proposes
a regulation strategy for the forward velocity along with the pitch and yaw
velocities. Under such a strategy, the vehicle slows down near the source and
stays within a small area as if it comes to a full stop, and controllers for
angular velocities become succinct. We prove the local exponential convergence
via the averaging technique. Finally, the theoretical results are illustrated
with simulations.Comment: submitted to IEEE TCST;12 pages, 10 figure
Prescribed-Time Seeking of a Repulsive Source by Unicycle Angular Velocity Tuning
All the existing source seeking algorithms for unicycle models in GPS-denied
settings guarantee at best an exponential rate of convergence over an infinite
interval. Using the recently introduced time-varying feedback tools for
prescribed-time stabilization, we achieve source seeking in prescribed time,
i.e., the convergence to the source, without the measurements of the position
and velocity of the unicycle, in as short a time as the user desires, starting
from an arbitrary distance from the source. The convergence is established
using a singularly perturbed version of the Lie bracket averaging method,
combined with time dilation and time contraction operations. The algorithm is
robust, provably, even to an arbitrarily strong gradient-dependent repulsive
velocity drift emanating from the source
Planar Cooperative Extremum Seeking with Guaranteed Convergence Using A Three-Robot Formation
In this paper, a combined formation acquisition and cooperative extremum
seeking control scheme is proposed for a team of three robots moving on a
plane. The extremum seeking task is to find the maximizer of an unknown
two-dimensional function on the plane. The function represents the signal
strength field due to a source located at maximizer, and is assumed to be
locally concave around maximizer and monotonically decreasing in distance to
the source location. Taylor expansions of the field function at the location of
a particular lead robot and the maximizer are used together with a gradient
estimator based on signal strength measurements of the robots to design and
analyze the proposed control scheme. The proposed scheme is proven to
exponentially and simultaneously (i) acquire the specified geometric formation
and (ii) drive the lead robot to a specified neighborhood disk around
maximizer, whose radius depends on the specified desired formation size as well
as the norm bounds of the Hessian of the field function. The performance of the
proposed control scheme is evaluated using a set of simulation experiments.Comment: Presented at the 2018 IEEE Conference on Decision and Control (CDC),
Miami Beach, FL, US
Newton Nonholonomic Source Seeking for Distance-Dependent Maps
The topics of source seeking and Newton-based extremum seeking have
flourished, independently, but never combined. We present the first
Newton-based source seeking algorithm. The algorithm employs forward velocity
tuning, as in the very first source seeker for the unicycle, and incorporates
an additional Riccati filter for inverting the Hessian inverse and feeding it
into the demodulation signal. Using second-order Lie bracket averaging, we
prove convergence to the source at a rate that is independent of the unknown
Hessian of the map. The result is semiglobal and practical, for a map that is
quadratic in the distance from the source. The paper presents a theory and
simulations, which show advantage of the Newton-based over the gradient-based
source seeking
Underactuated Source Seeking by Surge Force Tuning: Theory and Boat Experiments
We extend source seeking algorithms, in the absence of position and velocity
measurements, and with tuning of the surge input, from velocity-actuated
(unicycle) kinematic models to force-actuated generic Euler-Lagrange dynamic
underactuated models. In the design and analysis, we employ a symmetric product
approximation, averaging, passivity, and partial-state stability theory. The
proposed control law requires only real-time measurement of the source signal
at the current position of the vehicle and ensures semi-global practical
uniform asymptotic stability (SPUAS) with respect to the linear motion
coordinates for the closed-loop system. The performance of our source seeker
with surge force tuning is illustrated with both numerical simulations and
experiments of an underactuated boat
Source Seeking Control of Unicycle Robots with 3-D-Printed Flexible Piezoresistive Sensors
We present the design and experimental validation of source seeking control algorithms for a unicycle mobile robot that is equipped with novel 3D-printed flexible graphene-based piezoresistive airflow sensors. Based solely on a local gradient measurement from the airflow sensors, we propose and analyze a projected gradient ascent algorithm to solve the source seeking problem. In the case of partial sensor failure, we propose a combination of Extremum-Seeking Control with our projected gradient ascent algorithm. For both control laws, we prove the asymptotic convergence of the robot to the source. Numerical simulations were performed to validate the algorithms and experimental validations are presented to demonstrate the efficacy of the proposed methods
Astrocyte Regulated Neuromorphic Central Pattern Generator Control of Legged Robotic Locomotion
Neuromorphic computing systems, where information is transmitted through
action potentials in a bio-plausible fashion, is gaining increasing interest
due to its promise of low-power event-driven computing. Application of
neuromorphic computing in robotic locomotion research have largely focused on
Central Pattern Generators (CPGs) for bionics robotic control algorithms -
inspired from neural circuits governing the collaboration of the limb muscles
in animal movement. Implementation of artificial CPGs on neuromorphic hardware
platforms can potentially enable adaptive and energy-efficient edge robotics
applications in resource constrained environments. However, underlying rewiring
mechanisms in CPG for gait emergence process is not well understood. This work
addresses the missing gap in literature pertaining to CPG plasticity and
underscores the critical homeostatic functionality of astrocytes - a cellular
component in the brain that is believed to play a major role in multiple brain
functions. This paper introduces an astrocyte regulated Spiking Neural Network
(SNN)-based CPG for learning locomotion gait through Reward-Modulated STDP for
quadruped robots, where the astrocytes help build inhibitory connections among
the artificial motor neurons in different limbs. The SNN-based CPG is simulated
on a multi-object physics simulation platform resulting in the emergence of a
trotting gait while running the robot on flat ground.
computational power savings is observed in comparison to a state-of-the-art
reinforcement learning based robot control algorithm. Such a
neuroscience-algorithm co-design approach can potentially enable a quantum leap
in the functionality of neuromorphic systems incorporating glial cell
functionality
Growth and migration of solids in evolving protostellar disks I: Methods and Analytical tests
This series of papers investigates the early stages of planet formation by
modeling the evolution of the gas and solid content of protostellar disks from
the early T Tauri phase until complete dispersal of the gas. In this first
paper, I present a new set of simplified equations modeling the growth and
migration of various species of grains in a gaseous protostellar disk evolving
as a result of the combined effects of viscous accretion and photo-evaporation
from the central star. Using the assumption that the grain size distribution
function always maintains a power-law structure approximating the average
outcome of the exact coagulation/shattering equation, the model focuses on the
calculation of the growth rate of the largest grains only. The coupled
evolution equations for the maximum grain size, the surface density of the gas
and the surface density of solids are then presented and solved
self-consistently using a standard 1+1 dimensional formalism. I show that the
global evolution of solids is controlled by a leaky reservoir of small grains
at large radii, and propose an empirically derived evolution equation for the
total mass of solids, which can be used to estimate the total heavy element
retention efficiency in the planet formation paradigm. Consistency with
observation of the total mass of solids in the Minimum Solar Nebula augmented
with the mass of the Oort cloud sets strong upper limit on the initial grain
size distribution, as well as on the turbulent parameter \alphat. Detailed
comparisons with SED observations are presented in a following paper.Comment: Submitted to ApJ. 23 pages and 13 figure
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