38 research outputs found
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
Distributed Estimation of Graph Spectrum
In this paper, we develop a two-stage distributed algorithm that enables
nodes in a graph to cooperatively estimate the spectrum of a matrix
associated with the graph, which includes the adjacency and Laplacian matrices
as special cases. In the first stage, the algorithm uses a discrete-time linear
iteration and the Cayley-Hamilton theorem to convert the problem into one of
solving a set of linear equations, where each equation is known to a node. In
the second stage, if the nodes happen to know that is cyclic, the algorithm
uses a Lyapunov approach to asymptotically solve the equations with an
exponential rate of convergence. If they do not know whether is cyclic, the
algorithm uses a random perturbation approach and a structural controllability
result to approximately solve the equations with an error that can be made
small. Finally, we provide simulation results that illustrate the algorithm.Comment: 15 pages, 2 figure
Connectivity-Preserving Swarm Teleoperation With A Tree Network
During swarm teleoperation, the human operator may threaten the
distance-dependent inter-robot communications and, with them, the connectivity
of the slave swarm. To prevent the harmful component of the human command from
disconnecting the swarm network, this paper develops a constructive strategy to
dynamically modulate the interconnections of, and the locally injected damping
at, all slave robots. By Lyapunov-based set invariance analysis, the explicit
law for updating that control gains has been rigorously proven to synchronize
the slave swarm while preserving all interaction links in the tree network. By
properly limiting the impact of the user command rather than rejecting it
entirely, the proposed control law enables the human operator to guide the
motion of the slave swarm to the extent to which it does not endanger the
connectivity of the swarm network. Experiment results demonstrate that the
proposed strategy can maintain the connectivity of the tree network during
swarm teleoperation
Stability and Vulnerability of Bird Flocking Behaviour: A Mathematical Analysis
Given a large number of birds in the flock, we mathematically investigate the mechanism the birds move in a collective behavior. We assume that each bird is able to know its position and velocity of other birds within a radius of communication. Thus, to be able to fly in the flock, a bird has to adjust its position and velocity according to his neighbors. For this purpose, first of all, we analyze how the connectedness of the bird interaction network affects the cohesion of the stable bird flock. We further analyze a condition when the flock is vulnerable, which is mathematically indicated by means of the presence of an articulation point in bird communication network
Maximizing Algebraic Connectivity of Constrained Graphs in Adversarial Environments
This paper aims to maximize algebraic connectivity of networks via topology
design under the presence of constraints and an adversary. We are concerned
with three problems. First, we formulate the concave maximization topology
design problem of adding edges to an initial graph, which introduces a
nonconvex binary decision variable, in addition to subjugation to general
convex constraints on the feasible edge set. Unlike previous methods, our
method is justifiably not greedy and capable of accommodating these additional
constraints. We also study a scenario in which a coordinator must selectively
protect edges of the network from a chance of failure due to a physical
disturbance or adversarial attack. The coordinator needs to strategically
respond to the adversary's action without presupposed knowledge of the
adversary's feasible attack actions. We propose three heuristic algorithms for
the coordinator to accomplish the objective and identify worst-case preventive
solutions. Each algorithm is shown to be effective in simulation and we provide
some discussion on their compared performance.Comment: 8 pages, submitted to European Control Conference 201
Robust Environmental Mapping by Mobile Sensor Networks
Constructing a spatial map of environmental parameters is a crucial step to
preventing hazardous chemical leakages, forest fires, or while estimating a
spatially distributed physical quantities such as terrain elevation. Although
prior methods can do such mapping tasks efficiently via dispatching a group of
autonomous agents, they are unable to ensure satisfactory convergence to the
underlying ground truth distribution in a decentralized manner when any of the
agents fail. Since the types of agents utilized to perform such mapping are
typically inexpensive and prone to failure, this results in poor overall
mapping performance in real-world applications, which can in certain cases
endanger human safety. This paper presents a Bayesian approach for robust
spatial mapping of environmental parameters by deploying a group of mobile
robots capable of ad-hoc communication equipped with short-range sensors in the
presence of hardware failures. Our approach first utilizes a variant of the
Voronoi diagram to partition the region to be mapped into disjoint regions that
are each associated with at least one robot. These robots are then deployed in
a decentralized manner to maximize the likelihood that at least one robot
detects every target in their associated region despite a non-zero probability
of failure. A suite of simulation results is presented to demonstrate the
effectiveness and robustness of the proposed method when compared to existing
techniques.Comment: accepted to icra 201
Route Swarm: Wireless Network Optimization through Mobility
In this paper, we demonstrate a novel hybrid architecture for coordinating
networked robots in sensing and information routing applications. The proposed
INformation and Sensing driven PhysIcally REconfigurable robotic network
(INSPIRE), consists of a Physical Control Plane (PCP) which commands agent
position, and an Information Control Plane (ICP) which regulates information
flow towards communication/sensing objectives. We describe an instantiation
where a mobile robotic network is dynamically reconfigured to ensure high
quality routes between static wireless nodes, which act as source/destination
pairs for information flow. The ICP commands the robots towards evenly
distributed inter-flow allocations, with intra-flow configurations that
maximize route quality. The PCP then guides the robots via potential-based
control to reconfigure according to ICP commands. This formulation, deemed
Route Swarm, decouples information flow and physical control, generating a
feedback between routing and sensing needs and robotic configuration. We
demonstrate our propositions through simulation under a realistic wireless
network regime.Comment: 9 pages, 4 figures, submitted to the IEEE International Conference on
Intelligent Robots and Systems (IROS) 201
Robust Connectivity Analysis for Multi-Agent Systems
In this report we provide a decentralized robust control approach, which
guarantees that connectivity of a multi-agent network is maintained when
certain bounded input terms are added to the control strategy. Our main
motivation for this framework is to determine abstractions for multi-agent
systems under coupled constraints which are further exploited for high level
plan generation.Comment: 20 page