451 research outputs found
A Data Set for Fault Detection Research on Component-Based Robotic Systems
Wienke J, Meyer zu Borgsen S, Wrede S. A Data Set for Fault Detection Research on Component-Based Robotic Systems. In: Alboul L, Damian D, Aitken JM, eds. Towards Autonomous Robotic Systems. Lecture Notes in Artificial Intelligence. Vol 9716. Springer International Publishing; 2016: 339-350.Fault detection and identification methods (FDI) are an important aspect for ensuring consistent behavior of technical systems. In robotics FDI promises to improve the autonomy and robustness. Existing FDI research in robotics mostly focused on faults in specific areas, like sensor faults. While there is FDI research also on the overarching software system, common data sets to benchmark such solutions do not exist. In this paper we present a data set for FDI research on robot software systems to bridge this gap. We have recorded an HRI scenario with our RoboCup@Home platform and induced diverse empirically grounded faults using a novel, structured method. The recordings include the complete event-based communication of the system as well as detailed performance counters for all system components and exact ground-truth information on the induced faults. The resulting data set is a challenging benchmark for FDI research in robotics which is publicly available
Statistical Software Properties: Definition, Inference and Monitoring
Software properties define how software systems should operate. Specifying correct properties, however, can be difficult and expensive as it requires deep knowledge of the system\u27s expected behavior and the environment in which it operates. Automated analysis techniques to infer properties from code or code executions can mitigate that cost, but are still unable to go beyond state properties and the simplest patterns of temporal properties. This limitation renders properties that sacrifice fault detection power.
To address this problem, we introduce a new type of software properties called \textit{statistical properties}, which characterize significant statistical relationships among the values of variables across program states. We define an approach to infer these relationships automatically and support their monitoring while controlling the trade-offs between overhead and the precision and recall of the inferred properties.
We perform several experiments to assess the approach in the context of distributed robotics applications. Our findings indicate that the inferred statistical properties can be use to generate precise and cost-effective models capable of detecting faults in software systems while keeping the number of false positives close to zero and previous knowledge of the software system design and behavior unnecessary.
Adviser: Sebastian Elbau
Real-time Adaptive Sensor Attack Detection and Recovery in Autonomous Cyber-physical Systems
Cyber-Physical Systems (CPS) tightly couple information technology with physical processes, which rises new vulnerabilities such as physical attacks that are beyond conventional cyber attacks.Attackers may non-invasively compromise sensors and spoof the controller to perform unsafe actions. This issue is even emphasized with the increasing autonomy in CPS. While this fact has motivated many defense mechanisms against sensor attacks, a clear vision of the timing and usability (or the false alarm rate) of attack detection still remains elusive. Existing works tend to pursue an unachievable goal of minimizing the detection delay and false alarm rate at the same time, while there is a clear trade-off between the two metrics. Instead, this dissertation argues that attack detection should bias different metrics (detection delay and false alarm) when a system sits in different states. For example, if the system is close to unsafe states, reducing the detection delay is preferable to lowering the false alarm rate, and vice versa. This dissertation proposes two real-time adaptive sensor attack detection frameworks. The frameworks can dynamically adapt the detection delay and false alarm rate so as to meet a detection deadline and improve usability according to different system statuses. We design and implement the proposed frameworks and validate them using realistic sensor data of automotive CPS to demonstrate its efficiency and efficacy.
Further, this dissertation proposes \textit{Recovery-by-Learning}, a data-driven attack recovery framework that restores CPS from sensor attacks. The importance of attack recovery is emphasized by the need to mitigate the attack\u27s impact on a system and restore it to continue functioning. We propose a double sliding window-based checkpointing protocol to remove compromised data and keep trustful data for state estimation.
Together, the proposed solutions enable a holistic attack resilient solution for automotive cyber-physical systems
Plethora : a framework for the intelligent control of robotic assembly systems
Plethora : a framework for the intelligent control of robotic assembly system
Propositional and Activity Monitoring Using Qualitative Spatial Reasoning
SM thesisCommunication is the key to effective teamwork regardless of whether the team members are humans or machines. Much of the communication that makes human teams so effective is non-verbal; they are able to recognize the actions that the other team members are performing and take their own actions in order to assist. A robotic team member should be able to make the same inferences, observing the state of the environment and inferring what actions are being taken. In this thesis I introduce a novel approach to the combined problem of activity recognition and propositional monitoring. This approach breaks down the problem into smaller sub-tasks. First, the raw sensor input is parsed into simple, easy to understand primitive semantic relationships known as qualitative spatial relations (QSRs). These primitives are then combined to estimate the state of the world in the same language used by most planners, planning domain definition language (PDDL) propositions. Both the primitives and propositions are combined to infer the status of the actions that the human is taking. I describe an algorithm for solving each of these smaller problems and describe the modeling process for a variety of tasks from an abstracted electronic component assembly (ECA) scenario. I implemented this scenario on a robotic testbed and collected data of a human performing the example actions
Set-based state estimation and fault diagnosis using constrained zonotopes and applications
This doctoral thesis develops new methods for set-based state estimation and
active fault diagnosis (AFD) of (i) nonlinear discrete-time systems, (ii)
discrete-time nonlinear systems whose trajectories satisfy nonlinear equality
constraints (called invariants), (iii) linear descriptor systems, and (iv)
joint state and parameter estimation of nonlinear descriptor systems. Set-based
estimation aims to compute tight enclosures of the possible system states in
each time step subject to unknown-but-bounded uncertainties. To address this
issue, the present doctoral thesis proposes new methods for efficiently
propagating constrained zonotopes (CZs) through nonlinear mappings. Besides,
this thesis improves the standard prediction-update framework for systems with
invariants using new algorithms for refining CZs based on nonlinear
constraints. In addition, this thesis introduces a new approach for set-based
AFD of a class of nonlinear discrete-time systems. An affine parametrization of
the reachable sets is obtained for the design of an optimal input for set-based
AFD. In addition, this thesis presents new methods based on CZs for set-valued
state estimation and AFD of linear descriptor systems. Linear static
constraints on the state variables can be directly incorporated into CZs.
Moreover, this thesis proposes a new representation for unbounded sets based on
zonotopes, which allows to develop methods for state estimation and AFD also of
unstable linear descriptor systems, without the knowledge of an enclosure of
all the trajectories of the system. This thesis also develops a new method for
set-based joint state and parameter estimation of nonlinear descriptor systems
using CZs in a unified framework. Lastly, this manuscript applies the proposed
set-based state estimation and AFD methods using CZs to unmanned aerial
vehicles, water distribution networks, and a lithium-ion cell.Comment: My PhD Thesis from Federal University of Minas Gerais, Brazil. Most
of the research work has already been published in DOIs
10.1109/CDC.2018.8618678, 10.23919/ECC.2018.8550353,
10.1016/j.automatica.2019.108614, 10.1016/j.ifacol.2020.12.2484,
10.1016/j.ifacol.2021.08.308, 10.1016/j.automatica.2021.109638,
10.1109/TCST.2021.3130534, 10.1016/j.automatica.2022.11042
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