5,953 research outputs found
Quantifying Robotic Swarm Coverage
In the field of swarm robotics, the design and implementation of spatial
density control laws has received much attention, with less emphasis being
placed on performance evaluation. This work fills that gap by introducing an
error metric that provides a quantitative measure of coverage for use with any
control scheme. The proposed error metric is continuously sensitive to changes
in the swarm distribution, unlike commonly used discretization methods. We
analyze the theoretical and computational properties of the error metric and
propose two benchmarks to which error metric values can be compared. The first
uses the realizable extrema of the error metric to compute the relative error
of an observed swarm distribution. We also show that the error metric extrema
can be used to help choose the swarm size and effective radius of each robot
required to achieve a desired level of coverage. The second benchmark compares
the observed distribution of error metric values to the probability density
function of the error metric when robot positions are randomly sampled from the
target distribution. We demonstrate the utility of this benchmark in assessing
the performance of stochastic control algorithms. We prove that the error
metric obeys a central limit theorem, develop a streamlined method for
performing computations, and place the standard statistical tests used here on
a firm theoretical footing. We provide rigorous theoretical development,
computational methodologies, numerical examples, and MATLAB code for both
benchmarks.Comment: To appear in Springer series Lecture Notes in Electrical Engineering
(LNEE). This book contribution is an extension of our ICINCO 2018 conference
paper arXiv:1806.02488. 27 pages, 8 figures, 2 table
Quantitative Assessment of Robotic Swarm Coverage
This paper studies a generally applicable, sensitive, and intuitive error
metric for the assessment of robotic swarm density controller performance.
Inspired by vortex blob numerical methods, it overcomes the shortcomings of a
common strategy based on discretization, and unifies other continuous notions
of coverage. We present two benchmarks against which to compare the error
metric value of a given swarm configuration: non-trivial bounds on the error
metric, and the probability density function of the error metric when robot
positions are sampled at random from the target swarm distribution. We give
rigorous results that this probability density function of the error metric
obeys a central limit theorem, allowing for more efficient numerical
approximation. For both of these benchmarks, we present supporting theory,
computation methodology, examples, and MATLAB implementation code.Comment: Proceedings of the 15th International Conference on Informatics in
Control, Automation and Robotics (ICINCO), Porto, Portugal, 29--31 July 2018.
11 pages, 4 figure
Distributed allocation of mobile sensing swarms in gyre flows
We address the synthesis of distributed control policies to enable a swarm of
homogeneous mobile sensors to maintain a desired spatial distribution in a
geophysical flow environment, or workspace. In this article, we assume the
mobile sensors (or robots) have a "map" of the environment denoting the
locations of the Lagrangian coherent structures or LCS boundaries. Based on
this information, we design agent-level hybrid control policies that leverage
the surrounding fluid dynamics and inherent environmental noise to enable the
team to maintain a desired distribution in the workspace. We establish the
stability properties of the ensemble dynamics of the distributed control
policies. Since realistic quasi-geostrophic ocean models predict double-gyre
flow solutions, we use a wind-driven multi-gyre flow model to verify the
feasibility of the proposed distributed control strategy and compare the
proposed control strategy with a baseline deterministic allocation strategy.
Lastly, we validate the control strategy using actual flow data obtained by our
coherent structure experimental testbed.Comment: 10 pages, 14 Figures, added reference
Biomimetic Algorithms for Coordinated Motion: Theory and Implementation
Drawing inspiration from flight behavior in biological settings (e.g.
territorial battles in dragonflies, and flocking in starlings), this paper
demonstrates two strategies for coverage and flocking. Using earlier
theoretical studies on mutual motion camouflage, an appropriate steering
control law for area coverage has been implemented in a laboratory test-bed
equipped with wheeled mobile robots and a Vicon high speed motion capture
system. The same test-bed is also used to demonstrate another strategy (based
on local information), termed topological velocity alignment, which serves to
make agents move in the same direction. The present work illustrates the
applicability of biological inspiration in the design of multi-agent robotic
collectives
Internal agent states : experiments using the swarm leader concept
In recent years, an understanding of the operating principles and stability of natural swarms has proven to be a useful tool for the design and control of artificial robotic agents. Many robotic systems, whose design or control principals are inspired by behavioural aspects of real biological systems such as leader-follower relationship, have been developed. We introduced an algorithm which successfully enhances the navigation performance of a swarm of robots using the swarm leader concept. This paper presents some applications based on that work using the simulations and experimental implementation using a swarming behaviour test-bed at the University of Strathclyde. Experimental and simulation results match closely in a way that confirms the efficiency of the algorithm as well as its applicability
- …