Open Access

In the field of service robots, dealing with faults is crucial to promote user acceptance. In this context, this work focuses on some specific faults which arise from the interaction of a robot with its real world environment due to insufficient knowledge for action execution. In our previous work [1], we have shown that such missing knowledge can be obtained through learning by experimentation. The combination of symbolic and geometric models allows us to represent action execution knowledge effectively. However we did not propose a suitable representation of the symbolic model. In this work we investigate such symbolic representation and evaluate its learning capability. The experimental analysis is performed on four use cases using four different learning paradigms. As a result, the symbolic representation together with the most suitable learning paradigm are identified.

In Sensor-based Fault Detection and Diagnosis (SFDD) methods, spatial and temporal dependencies among the sensor signals can be modeled to detect faults in the sensors, if the defined dependencies change over time. In this work, we model Granger causal relationships between pairs of sensor data streams to detect changes in their dependencies. We compare the method on simulated signals with the Pearson correlation, and show that the method elegantly handles noise and lags in the signals and provides appreciable dependency detection. We further evaluate the method using sensor data from a mobile robot by injecting both internal and external faults during operation of the robot. The results show that the method is able to detect changes in the system when faults are injected, but is also prone to detecting false positives. This suggests that this method can be used as a weak detection of faults, but other methods, such as the use of a structural model, are required to reliably detect and diagnose faults.

The use of natural language to indicate robot tasks is a convenient way to command robots. As a result, several models and approaches capable of understanding robot commands have been developed, which however complicates the choice of a suitable model for a given scenario. In this work, we present a comparative analysis and benchmarking of four natural language understanding models - Mbot, Rasa, LU4R, and ECG. We particularly evaluate the performance of the models to understand domestic service robot commands by recognizing the actions and any complementary information in them in three use cases: the RoboCup@Home General Purpose Service Robot (GPSR) category 1 contest, GPSR category 2, and hospital logistics in the context of the ROPOD project.

We describe the development of an architecture for the DESIRE technology demonstrator based on principles of classical component based software engineering. The architecture is directly derived from the project requirements and resides on the concept of an Autonomous Component utilizing a smart feedback value called WishLists. This return type is able to provide expert advice about the reasons of occurring failures and give hints for possible recovery strategies. This is of key importance to advance towards robustness. The integration of an AI task planner allows the realization of higher flexibility, dependability and capability during task execution and may resolve conflicts between occurring WishLists. Furthermore the necessity of a central system-state model (Eigenmodel), which represents the current state and configuration of the whole system at runtime, is explained and illustrated. We conclude with some lessons learned.

While executing actions, service robots may experience external faults because of insufficient knowledge about the actions' preconditions. The possibility of encountering such faults can be minimised if symbolic and geometric precondition models are combined into a representation that specifies how and where actions should be executed. This work investigates the problem of learning such action execution models and the manner in which those models can be generalised. In particular, we develop a template-based representation of execution models, which we call delta models, and describe how symbolic template representations and geometric success probability distributions can be combined for generalising the templates beyond the problem instances on which they are created. Our experimental analysis, which is performed with two physical robot platforms, shows that delta models can describe execution-specific knowledge reliably, thus serving as a viable model for avoiding the occurrence of external faults.

The development of robot control programs is a complex task. Many robots are different in their electrical and mechanical structure which is also reflected in the software. Specific robot software environments support the program development, but are mainly text-based and usually applied by experts in the field with profound knowledge of the target robot. This paper presents a graphical programming environment which aims to ease the development of robot control programs. In contrast to existing graphical robot programming environments, our approach focuses on the composition of parallel action sequences. The developed environment allows to schedule independent robot actions on parallel execution lines and provides mechanism to avoid side-effects of parallel actions. The developed environment is platform-independent and based on the model-driven paradigm. The feasibility of our approach is shown by the application of the sequencer to a simulated service robot and a robot for educational purpose.

Ein gebräuchliche Methodik beim Entwurf eingebetteter Systeme, in Anwendung besonders bei kleinen- und mittleren Unternehmen, geht folgendermaßen vor: Man nehme das bereits existierende Mikrokontroller Entwicklungspaket und bereits vorhandene Funktionen aus einer alten Systemrealisierung, variiere bzw. passe sie an die neue Aufgabe an und teste dann durch Emulation, ob die Spezifikation erfüllt ist.

We present some advanced hardware units and an appropriate component based SW architecture for DESIRE. As an example we describe the integration of a enhanced AI task planner which allows for higher flexibility and dependability during complex task execution.

In the field of domestic service robots, recovery from faults is crucial to promote user acceptance. In this context we will focus, in particular, on some specific faults which arise from interaction of the robot with its real world environment. In these situations even a well modelled robot may fail to perform its tasks successfully due to external faults which occur while interacting. We reason along the most frequent failures in typical scenarios which we have observed in real-world demonstrations and competitions using the autonomous service robot Jenny. We propose four different fault classes caused by disturbances, imperfect perception, inadequate planning or chaining of action sequences. The faults are first classified and then mapped to a small number of fault handling techniques partly known, partly extended by us. In addition to existing techniques we present two approaches to handle external faults from inadequate descriptions of the planner operator class. The first approach uses naive physics concepts to find information about detected external faults. The second approach is simulation based, utilising a single simulation that shows a manipulated object’s behaviour for successfully completing an action. The approach uses the N-Bins learning algorithm to suggest a releasing state of the object that avoids the occurrence of external faults. We apply the proposed approaches to the scenarios where a robot performs the pick-and-place manipulation tasks. The results of these applications show that both approaches hold great promises for handling external faults in domestic service robotics.

Despite perfect functioning of its internal components, a robot can be unsuccessful in performing its tasks because of unforeseen situations. Mostly these situations arise from the interaction of a robot with its ever-changing environment. In this paper we refer to these unsuccessful operations as external unknown faults. We reason along the most frequent failures in typical scenarios which we observed during real-world demonstrations and competitions using our Care-O-bot III robot. These events take place in an apartment-like environment.

The increasing complexity of tasks that are required to be executed by robots demands higher reliability of robotic platforms. For this, it is crucial for robot developers to consider fault diagnosis. In this study, a general non-intrusive fault diagnosis system for robotic platforms is proposed. A mini-PC is non-intrusively attached to a robot that is used to detect and diagnose faults. The health data and diagnosis produced by the mini-PC is then standardized and transmitted to a remote-PC. A storage device is also attached to the mini-PC for data logging of health data in case of loss of communication with the remote-PC. In this study, a hybrid fault diagnosis method is compared to consistency-based diagnosis (CBD), and CBD is selected to be deployed on the system. The proposed system is modular and can be deployed on different robotic platforms with minimum setup.