Projects
Distributed Fault Diagnosis for Nonlinear Large-Scale Stochastic Systems
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The goal of this project is to establish an analytical and computational foundation
for distributed fault diagnosis algorithms for nonlinear, large-scale
stochastic systems. Complex processes are significantly more vulnerable to faults,
as a malfunction in a single component may impact the entire system.
Our work formulates the problem of fault-diagnosis in a non-centralized way,
making it applicable to real-life, large-scale systems.
The monolithic process is monitored by a network of interconnected diagnostic
nodes with local processing and communication capabilities.We are working on
analytical tools for obtaining global inference about the health of the system
based on local observations and information exchange. This aim targets high-dimensional
processes with geographically sparse subcomponents. Our work focuses on the derivation
of data fusion protocols that work on parallel with estimation-based failure-sensitive
filters.
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Distributed Source Tracing
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Source tracing is a search and localization task where the main goal
is to identify the location of a toxic substance emitter from its
gaseous plume. The prompt hazardous source localization is a safety-critical
component of numerous environmental, commercial and military applications.
The primary motivation for substituting first responders by autonomous robots
is to minimize or even eliminate the human exposure to the potentially lethal toxin.
The mobile robotic detectors are not constrained by human limitations such as
fatigue or inattentiveness. The chemical source tracing can become significantly
faster and reliable if a swarm of communicating mobile robots is deployed instead
of a singleton unit.
The theoretical approach of this work targets real-life scenarios
where a large geographical region is monitored by a finite number
of interconnected sensors (potentially mobile). We apply a model-based
approach where the concentration of the chemical source is represented
by a spatial and time varying mathematical model. The challenge is
to estimate the parameters of the plume's model by a sequence of noise
infested spatial measurements. We investigate distributed sampling
methods in order estimate the source location and intensity. At each
iteration, the filtered estimates are used to transport the robot
towards the source while maximizing the likelihood of the acquired
observations.
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Fault Diagnosis for Networked Multi-Agent Systems
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Even though distributed control algorithms have been well investigated
for mobile robotic swarms, there are only few distributed FD methods
for this class of systems. The development of a distributed FD scheme for networked robots is
challenging due to the distributed nature of the system and the limited
information shared among the nodes.
The ability of a monitoring system to diagnose and isolate faults is analytically
quantifiable trough its Detectability, Isolability and Identifiability (DII) properties.
In this project, we investigate the DII properties for multi-agent systems
that are executing the agreement protocol and are subject to both sensor
and actuator faults. We adopt a geometric multivariable control approach
for the synthesis of observer-based failure-sensitive filters with prescribed performance.
This primary centralized treatment of the multi-agent problem is pivoted to the
design of distributed for large-scale linear systems.
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Warehouse Automation – Multi-Robot Coordination
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In the recent years, Autonomous Guided Vehicles (AGVs) are gradually integrated to warehouse management systems.
The employment of AGVs has numerous advantages over conventional warehouse systems in terms of cost, scalability and efficiency.
In this project, we present the development of a small-scale test-bed platform for testing and validating
warehouse automation control algorithms utilizing a swarm of AGVs. The proposed platform is scalable, fast,
and effective in both cost and dimensions. The robotic drives are centimeter-scale forklifts that transport
autonomously an arbitrary number of circular pallets to predefined reference locations.
This work is extended to the multi-agent coordination of robotic fleets that operate in cluttered industrial layouts.
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Large-Scale Actuator Networks: The Morphing Surface
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A class of cyber-physical systems that is gradually attracting increased scientific attention
is Large-Scale Actuator Networks (LSAN). A prospective application of actuator networks is
distributed manipulation. Distributed manipulation has the potential to become a game-changing
technology in the area of industrial automation. To examine this class of systems, this paper
presents a reactive elastic surface that autonomously morphs its shape by using a grid
of linear actuators to transport an object into a target location. The combined action
of the actuator grid overcomes the limitations of individual actuators,
resulting in a system with multiple degrees-of-freedom.
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