HMM-based anomaly interpretation for intelligent robots in Industry 4.0

We apply an anomaly detection method based on Hidden Markov Models and Hellinger distance to a Kairos mobile robot operating in the ICE lab, a research laboratory for Industry 4.0. Two main contributions are proposed: i) a decomposition of the Hellinger distance which allows to identify the causes of anomalous behaviours detected, ii) a graphical user interface that synchronously shows the robot movements in a map and the evolution of the Hellinger distance components, allowing a quick investigation of the causes of the detected anomalies. The tools are applied to a real-world dataset allowing to discover that an anomalous movement of the Kairos robot is caused by a wrong reading of the lidar from a window in the environment

HMMs for Anomaly Detection in Autonomous Robots

Detection of anomalies and faults is a key element for long-term robot autonomy, because, together with subsequent diagnosis and recovery, allows to reach the required levels of robustness and persistency. In this paper, we propose an approach for detecting anomalous behaviors in autonomous robots starting from data collected during their routine operations. The main idea is to model the nominal (expected) behavior of a robot system using Hidden Markov Models (HMMs) and to evaluate how far the observed behavior is from the nominal one using variants of the Hellinger distance adopted for our purposes. We present a method for online anomaly detection that computes the Hellinger distance between the probability distribution of observations made in a sliding window and the corresponding nominal emission probability distri- bution. We also present a method for onine anomaly detection that computes a variant of the Hellinger distance between two HMMs representing nominal and observed behaviors. The use of the Hellinger distance positively impacts on both detection performance and interpretability of detected anomalies, as shown by results of experiments performed in two real-world application domains, namely, water monitoring with aquatic drones and socially assistive robots for elders living at home. In particular, our approach improves by 6% the area under the ROC curve of standard online anomaly detection methods. The capabilities of our online method to discriminate anomalous behaviors in real-world applications are statistically proved

HMMs for Anomaly Detection in Autonomous Robots

Detection of anomalies and faults is a key element for long-term robot autonomy, because, together with subsequent diagnosis and recovery, allows to reach the required levels of robustness and persistency. In this paper, we propose an approach for detecting anomalous behaviors in autonomous robots starting from data collected during their routine operations. The main idea is to model the nominal (expected) behavior of a robot system using Hidden Markov Models (HMMs) and to evaluate how far the observed behavior is from the nominal one using variants of the Hellinger distance adopted for our purposes. We present a method for online anomaly detection that computes the Hellinger distance between the probability distribution of observations made in a sliding window and the corresponding nominal emission probability distribution. We also present a method for o!ine anomaly detection that computes a variant of the Hellinger distance between two HMMs representing nominal and observed behaviors. The use of the Hellinger distance positively impacts on both detection performance and interpretability of detected anomalies, as shown by results of experiments performed in two real-world application domains, namely, water monitoring with aquatic drones and socially assistive robots for elders living at home. In particular, our approach improves by 6% the area under the ROC curve of standard online anomaly detection methods. The capabilities of our o!ine method to discriminate anomalous behaviors in real-world applications are statistically proved

Data-driven fault detection for component based robotic systems

Golombek R. Data-driven fault detection for component based robotic systems. Bielefeld: Universität Bielefeld; 2014.Advancements in the field of robotics enable the creation of systems with cognitive abilities which are capable of close interaction with humans in real world scenarios. These systems may take over jobs previously executed by humans like house cleaning and cooking or they can be supportive and act as a helper for elderly people. One consequence of this progress is the increased need for dependable and fault tolerant behavior of today’s robotic systems because they share the same spaces with humans and operate in close proximity to them. Unreliable and faulty behavior may frustrate users or even endanger them resulting in poor acceptance of robotic systems. The contribution of this thesis is a fault detection approach called AuCom. Fault detection is a basis element for fault tolerant system behavior which is the ability of a system to autonomously cope with occurring faults while it is engaged in interaction. The approach is designed to tackle the specific needs of cognitive robotic systems which feature a component based hardware and software structure and are characterized by frequent changes due to research and development efforts as well as uncertain and variant behavior resulting from the interaction in real world environments. The solution presented in this thesis belongs to the class of data-driven fault detection approaches. This class of approaches assumes that fault relevant information can be directly derived from data gathered in the robotic system. The data exploited in this work for fault detection is the communication between the system’s components. This communication is represented with features which are common to all elements of the communication (i.e., they are generic). Furthermore, the approach assumes that the current element of the communication can be estimated from the history of the system’s communication and that a deviation from the expected estimate indicates a fault. This assumption is encoded in the model in terms of a novel representation of the communication as a time-series of temporal dynamic features. A concrete integration of the approach into a real system is exemplified on our robotic platform BIRON. In addition, exemplary integration solutions for robotic frameworks currently prominent in literature are discussed in this thesis. The actual capability of the approach to report faults is evaluated for several artificial systems in simulation and on BIRON in an off-line and on-line manner. The performance is compared to a histogram-based baseline approach

Learning a probabilistic self-awareness model for robotic systems

In order to address the problem of failure detection in the robotics domain, we present in this contribution a so-called self-awareness model, based on the system's internal data exchange and the inherent dynamics of inter-component communication. The model is strongly data driven and provides an anomaly detector for robotics systems both applicable in-situ at runtime as well as a-posteriori in post-mortem analysis. Current architectures or methods for failure detection in autonomous robots are either implementations of watch dog concepts or are based on excessive amounts of domain-specific error detection code. The approach presented in this contribution provides an avenue for the detection of more subtle anomalies originating from external sources such as the environment itself or system failures such as resource starvation. Additionally, developers are alleviated from explicitly modeling and foreseeing every exceptional situation, instead training the presented probabilistic model with the known normal modes within the specification of the robot system. As we developed and evaluated the self-awareness model on a mobile robot platform featuring an event-driven software architecture, the presented method can easily be applied in other current robotics software architectures. ©2010 IEEE.</p

Learning a probabilistic self-awareness model for robotic systems

In order to address the problem of failure detection in the robotics domain, we present in this contribution a so-called self-awareness model, based on the system's internal data exchange and the inherent dynamics of inter-component communication. The model is strongly data driven and provides an anomaly detector for robotics systems both applicable in-situ at runtime as well as a-posteriori in post-mortem analysis. Current architectures or methods for failure detection in autonomous robots are either implementations of watch dog concepts or are based on excessive amounts of domain-specific error detection code. The approach presented in this contribution provides an avenue for the detection of more subtle anomalies originating from external sources such as the environment itself or system failures such as resource starvation. Additionally, developers are alleviated from explicitly modeling and foreseeing every exceptional situation, instead training the presented probabilistic model with the known normal modes within the specification of the robot system. As we developed and evaluated the self-awareness model on a mobile robot platform featuring an event-driven software architecture, the presented method can easily be applied in other current robotics software architectures. ©2010 IEEE