1,549 research outputs found
Urban Swarms: A new approach for autonomous waste management
Modern cities are growing ecosystems that face new challenges due to the
increasing population demands. One of the many problems they face nowadays is
waste management, which has become a pressing issue requiring new solutions.
Swarm robotics systems have been attracting an increasing amount of attention
in the past years and they are expected to become one of the main driving
factors for innovation in the field of robotics. The research presented in this
paper explores the feasibility of a swarm robotics system in an urban
environment. By using bio-inspired foraging methods such as multi-place
foraging and stigmergy-based navigation, a swarm of robots is able to improve
the efficiency and autonomy of the urban waste management system in a realistic
scenario. To achieve this, a diverse set of simulation experiments was
conducted using real-world GIS data and implementing different garbage
collection scenarios driven by robot swarms. Results presented in this research
show that the proposed system outperforms current approaches. Moreover, results
not only show the efficiency of our solution, but also give insights about how
to design and customize these systems.Comment: Manuscript accepted for publication in IEEE ICRA 201
Modulating interaction times in an artificial society of robots
In a collaborative society, sharing information is advantageous for each individual as well as for the whole community. Maximizing the number of agent-to-agent interactions per time becomes an appealing behavior due to fast information spreading that maximizes the overall amount of shared information. However, if malicious agents are part of society, then the risk of interacting with one of them increases with an increasing number of interactions. In this paper, we investigate the roles of interaction rates and times (aka edge life) in artificial societies of simulated robot swarms. We adapt their social networks to form proper trust sub-networks and to contain attackers. Instead of sophisticated algorithms to build and administrate trust networks, we focus on simple control algorithms that locally adapt interaction times by changing only the robots' motion patterns. We successfully validate these algorithms in collective decision-making showing improved time to convergence and energy-efficient motion patterns, besides impeding the spread of undesired opinions
Modeling and Mathematical Analysis of Swarms of Microscopic Robots
The biologically-inspired swarm paradigm is being used to design
self-organizing systems of locally interacting artificial agents. A major
difficulty in designing swarms with desired characteristics is understanding
the causal relation between individual agent and collective behaviors.
Mathematical analysis of swarm dynamics can address this difficulty to gain
insight into system design. This paper proposes a framework for mathematical
modeling of swarms of microscopic robots that may one day be useful in medical
applications. While such devices do not yet exist, the modeling approach can be
helpful in identifying various design trade-offs for the robots and be a useful
guide for their eventual fabrication. Specifically, we examine microscopic
robots that reside in a fluid, for example, a bloodstream, and are able to
detect and respond to different chemicals. We present the general mathematical
model of a scenario in which robots locate a chemical source. We solve the
scenario in one-dimension and show how results can be used to evaluate certain
design decisions.Comment: 2005 IEEE Swarm Intelligence Symposium, Pasadena, CA June 200
Coverage and Field Estimation on Bounded Domains by Diffusive Swarms
In this paper, we consider stochastic coverage of bounded domains by a
diffusing swarm of robots that take local measurements of an underlying scalar
field. We introduce three control methodologies with diffusion, advection, and
reaction as independent control inputs. We analyze the diffusion-based control
strategy using standard operator semigroup-theoretic arguments. We show that
the diffusion coefficient can be chosen to be dependent only on the robots'
local measurements to ensure that the swarm density converges to a function
proportional to the scalar field. The boundedness of the domain precludes the
need to impose assumptions on decaying properties of the scalar field at
infinity. Moreover, exponential convergence of the swarm density to the
equilibrium follows from properties of the spectrum of the semigroup generator.
In addition, we use the proposed coverage method to construct a
time-inhomogenous diffusion process and apply the observability of the heat
equation to reconstruct the scalar field over the entire domain from
observations of the robots' random motion over a small subset of the domain. We
verify our results through simulations of the coverage scenario on a 2D domain
and the field estimation scenario on a 1D domain.Comment: To appear in the proceedings of the 55th IEEE Conference on Decision
and Control (CDC 2016
Distributed Control of Microscopic Robots in Biomedical Applications
Current developments in molecular electronics, motors and chemical sensors
could enable constructing large numbers of devices able to sense, compute and
act in micron-scale environments. Such microscopic machines, of sizes
comparable to bacteria, could simultaneously monitor entire populations of
cells individually in vivo. This paper reviews plausible capabilities for
microscopic robots and the physical constraints due to operation in fluids at
low Reynolds number, diffusion-limited sensing and thermal noise from Brownian
motion. Simple distributed controls are then presented in the context of
prototypical biomedical tasks, which require control decisions on millisecond
time scales. The resulting behaviors illustrate trade-offs among speed,
accuracy and resource use. A specific example is monitoring for patterns of
chemicals in a flowing fluid released at chemically distinctive sites.
Information collected from a large number of such devices allows estimating
properties of cell-sized chemical sources in a macroscopic volume. The
microscopic devices moving with the fluid flow in small blood vessels can
detect chemicals released by tissues in response to localized injury or
infection. We find the devices can readily discriminate a single cell-sized
chemical source from the background chemical concentration, providing
high-resolution sensing in both time and space. By contrast, such a source
would be difficult to distinguish from background when diluted throughout the
blood volume as obtained with a blood sample
Towards Swarm Calculus: Urn Models of Collective Decisions and Universal Properties of Swarm Performance
Methods of general applicability are searched for in swarm intelligence with
the aim of gaining new insights about natural swarms and to develop design
methodologies for artificial swarms. An ideal solution could be a `swarm
calculus' that allows to calculate key features of swarms such as expected
swarm performance and robustness based on only a few parameters. To work
towards this ideal, one needs to find methods and models with high degrees of
generality. In this paper, we report two models that might be examples of
exceptional generality. First, an abstract model is presented that describes
swarm performance depending on swarm density based on the dichotomy between
cooperation and interference. Typical swarm experiments are given as examples
to show how the model fits to several different results. Second, we give an
abstract model of collective decision making that is inspired by urn models.
The effects of positive feedback probability, that is increasing over time in a
decision making system, are understood by the help of a parameter that controls
the feedback based on the swarm's current consensus. Several applicable
methods, such as the description as Markov process, calculation of splitting
probabilities, mean first passage times, and measurements of positive feedback,
are discussed and applications to artificial and natural swarms are reported
Decentralized Connectivity-Preserving Deployment of Large-Scale Robot Swarms
We present a decentralized and scalable approach for deployment of a robot
swarm. Our approach tackles scenarios in which the swarm must reach multiple
spatially distributed targets, and enforce the constraint that the robot
network cannot be split. The basic idea behind our work is to construct a
logical tree topology over the physical network formed by the robots. The
logical tree acts as a backbone used by robots to enforce connectivity
constraints. We study and compare two algorithms to form the logical tree:
outwards and inwards. These algorithms differ in the order in which the robots
join the tree: the outwards algorithm starts at the tree root and grows towards
the targets, while the inwards algorithm proceeds in the opposite manner. Both
algorithms perform periodic reconfiguration, to prevent suboptimal topologies
from halting the growth of the tree. Our contributions are (i) The formulation
of the two algorithms; (ii) A comparison of the algorithms in extensive
physics-based simulations; (iii) A validation of our findings through
real-robot experiments.Comment: 8 pages, 8 figures, submitted to IROS 201
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