1,583 research outputs found
Decentralized formation control with connectivity maintenance and collision avoidance under limited and intermittent sensing
A decentralized switched controller is developed for dynamic agents to
perform global formation configuration convergence while maintaining network
connectivity and avoiding collision within agents and between stationary
obstacles, using only local feedback under limited and intermittent sensing.
Due to the intermittent sensing, constant position feedback may not be
available for agents all the time. Intermittent sensing can also lead to a
disconnected network or collisions between agents. Using a navigation function
framework, a decentralized switched controller is developed to navigate the
agents to the desired positions while ensuring network maintenance and
collision avoidance.Comment: 8 pages, 2 figures, submitted to ACC 201
A Message Passing Strategy for Decentralized Connectivity Maintenance in Agent Removal
In a multi-agent system, agents coordinate to achieve global tasks through
local communications. Coordination usually requires sufficient information
flow, which is usually depicted by the connectivity of the communication
network. In a networked system, removal of some agents may cause a
disconnection. In order to maintain connectivity in agent removal, one can
design a robust network topology that tolerates a finite number of agent
losses, and/or develop a control strategy that recovers connectivity. This
paper proposes a decentralized control scheme based on a sequence of
replacements, each of which occurs between an agent and one of its immediate
neighbors. The replacements always end with an agent, whose relocation does not
cause a disconnection. We show that such an agent can be reached by a local
rule utilizing only some local information available in agents' immediate
neighborhoods. As such, the proposed message passing strategy guarantees the
connectivity maintenance in arbitrary agent removal. Furthermore, we
significantly improve the optimality of the proposed scheme by incorporating
-criticality (i.e. the criticality of an agent in its
-neighborhood).Comment: 9 pages, 9 figure
Cybernetic automata: An approach for the realization of economical cognition for multi-robot systems
The multi-agent robotics paradigm has attracted much attention due to the
variety of pertinent applications that are well-served by the use of a multiplicity of
agents (including space robotics, search and rescue, and mobile sensor networks). The
use of this paradigm for most applications, however, demands economical, lightweight
agent designs for reasons of longer operational life, lower economic cost, faster and
easily-verified designs, etc.
An important contributing factor to an agent’s cost is its control architecture.
Due to the emergence of novel implementation technologies carrying the promise of
economical implementation, we consider the development of a technology-independent
specification for computational machinery. To that end, the use of cybernetics toolsets
(control and dynamical systems theory) is appropriate, enabling a principled specifi-
cation of robotic control architectures in mathematical terms that could be mapped
directly to diverse implementation substrates.
This dissertation, hence, addresses the problem of developing a technologyindependent
specification for lightweight control architectures to enable robotic agents
to serve in a multi-agent scheme. We present the principled design of static and dynamical
regulators that elicit useful behaviors, and integrate these within an overall
architecture for both single and multi-agent control. Since the use of control theory
can be limited in unstructured environments, a major focus of the work is on the engineering of emergent behavior.
The proposed scheme is highly decentralized, requiring only local sensing and
no inter-agent communication. Beyond several simulation-based studies, we provide
experimental results for a two-agent system, based on a custom implementation employing
field-programmable gate arrays
A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley
A distributed model predictive control (DMPC) approach based on distributed
optimization is applied to the power reference tracking problem of a hydro
power valley (HPV) system. The applied optimization algorithm is based on
accelerated gradient methods and achieves a convergence rate of O(1/k^2), where
k is the iteration number. Major challenges in the control of the HPV include a
nonlinear and large-scale model, nonsmoothness in the power-production
functions, and a globally coupled cost function that prevents distributed
schemes to be applied directly. We propose a linearization and approximation
approach that accommodates the proposed the DMPC framework and provides very
similar performance compared to a centralized solution in simulations. The
provided numerical studies also suggest that for the sparsely interconnected
system at hand, the distributed algorithm we propose is faster than a
centralized state-of-the-art solver such as CPLEX
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