Gerald Steinbauer

Movement prediction from real-world images using a liquid state machine

Prediction is an important task in robot motor control where it is used to gain feedback for a controller. With such a self-generated feedback, which is available before sensor readings from an environment can be processed, a controller can be stabilized and thus the performance of a moving robot in a real-world environment is improved. So far, only experiments with artificially generated data have shown good results. In a sequence of experiments we evaluate whether a liquid state machine in combination with a supervised learning algorithm can be used to predict ball trajectories with input data coming from a video camera mounted on a robot participating in the RoboCup. This pre-processed video data is fed into a recurrent spiking neural network. Connections to some output neurons are trained by linear regression to predict the position of a ball in various time steps ahead. Our results support the idea that learning with a liquid state machine can be applied not only to designed data but also to real, noisy data.

Real-Time diagnosis and repair of faults of robot control software

Faults in hardware and software are not totally avoidable not even if the components are carefully designed, implemented and tested. In this paper we present a solution for detection, localization and repair of faults in the control software for autonomous mobile robots. The presented diagnosis system uses model-based diagnosis for fault detection and localization. Furthermore, we present a method which enables the robot control software to recover from located faults. The novelty of our approach is that fault localization and repair takes place at runtime. Moreover, we present experimental results of the proposed diagnosis system obtained in the RoboCup Middle-Size scenario.

ROS-based mapping, localization and autonomous navigation using a Pioneer 3-DX robot and their relevant issues

The Robot Operating System (ROS) provides operating system-like services to operate robots. Mapping, localization, and autonomous navigation in an indoor environment are popular issues in the field of autonomous robots. Autonomous navigation in a dynamic environment is not only challenging but also uncovers many indoor environmental factors which affect the process of mapping and navigation. The presented work describes how a ROS-based control system is used with a Pioneer 3-DX robot for indoor mapping, localization, and autonomous navigation. Mapping of different challenging environments is presented in this work. Moreover, some factors associated with indoor environments that can affect mapping, localization, and automatic navigation, are also presented. For experiments, three environments (one artificial and two real) have been tested. Some implementation was done in C and Python.

An integrated model-based diagnosis and repair architecture for ROS-based robot systems

Autonomous robots are artifacts that comprise a significant number of heterogeneous hardware and software components and interact with dynamic environments. Therefore, there is always a chance of faults at run-time that negatively affect the reliability of the system. In this paper we present a novel diagnosis and repair architecture for ROS-based robot systems. It is an extension to the existing ROS diagnostics stack and follows a model-based diagnosis and repair approach. In the paper we discuss the integrated diagnosis and repair architecture in detail. Moreover, we show its application to an example robot system and report first experimental results. The presented work provides three major contributions: a combination of diagnosis and repair, the integration of hardware and software, and the integration into ROS.

A Teleo-Reactive Architecture for Fast, Reactive and Robust Control of Mobile Robots

One of the elementary tasks of an autonomous mobile robot is the execution of different behavior patterns in order to fulfill a given task. The complexity of this problem is especially high if the robot operates in a dynamic, unpredictable environment and requires the parallel control of multiple actuators. In this paper we present a novel architecture for robust and fast mobile robot control. The architecture is based on Teleo-Reactive Programs. We discuss the benefits and drawbacks of such programs, extend the basic definition for the parallel control of multiple actuators, and propose a new language and a compiler for extended Teleo-Reactive Programs. These tools simplify the creation of new behavior patterns and increase the runtime performance. Finally, we discuss implementation issues of the architecture when applying it to RoboCup Middle-Size soccer robots.

A Survey about Faults of Robots Used in RoboCup

Faults that occur in an autonomous robot system negatively affect its dependability. The aim of truly dependable and autonomous systems requires that one has to deal with these faults in some way. In order to be able to do this efficiently one has to have information on the nature of these faults. Very few studies on this topic have been conducted so far. In this paper we present results of a survey on faults of autonomous robots we conducted in the context of RoboCup. The major contribution of this paper is twofold. First we present an adapted fault taxonomy suitable for autonomous robots. Second we give information on the nature, the relevance and impact of faults in robot systems that are beneficial for researcher dealing with fault mitigation and management in autonomous systems.

Providing ground-truth data for the nao robot platform

Collecting ground truth-data for real-world applications is a non-trivial but very important task. In order to evaluate new algorithmic approaches or to benchmark system performance, they are inevitable. This is particularly true for robotics applications. In this paper we present our data collection for the biped humanoid robot Nao. Reflective markers were attached to Naos body, and the positions and orientation of its body and head were tracked in 6D with an accurate professional vision-based body motion tracking system. While doing so, the data of Naos internal state, i.e., the readings of all its servos, the inertial measurement unit, the force receptors plus a camera stream of the robots camera were stored for different, typical robotic soccer scenarios in the context of the RoboCup Standard Platform League. These data will be combined in order to compile an accurate ground-truth data set. We describe how the data were recorded, in which format they are stored, and show the usability of the logged data in some first experiments on the recorded data sets. The data sets will be made publicly available for the RoboCups Standard Platform League community.

Belief management for high-level robot programs

The robot programming and plan language IndiGolog allows for on-line execution of actions and offline projections of programs in dynamic and partly unknown environments. Basic assumptions are that the outcomes of primitive and sensing actions are correctly modeled, and that the agent is informed about all exogenous events beyond its control. In real-world applications, however, such assumptions do not hold. In fact, an actions outcome is error-prone and sensing results are noisy. In this paper, we present a belief management system in IndiGolog that is able to detect inconsistencies between a robots modeled belief and what happened in reality. The system furthermore derives explanations and maintains a consistent belief. Our main contributions are (1) a belief management system following a history-based diagnosis approach that allows an agent to actively cope with faulty actions and the occurrence of exogenous events; and (2) an implementation in IndiGolog and experimental results from a delivery domain.

Improving Robustness of Mobile Robots Using Model-based Reasoning

Retaining functionality of a mobile robot in the presence of faults is of particular interest in autonomous robotics. From our experiences in robotics we know that hardware is one of the weak points in mobile robots. In this paper we present the foundations of a system that automatically monitors the driving device of a mobile robot. In case of a detected fault, e.g., a broken motor, the system automatically reconfigures the robot in order to still allow to reach a certain position. The described system is based on a generalized model of the motion hardware. High-level control like path-planner only to change its behavior in case of a serious damage. The high-level control system remains the same. In the paper we present the model and the foundations of the diagnosis and reconfiguration system.

Model-based fault diagnosis and reconfiguration of robot drives

Modern drives of mobile robots are complex machines. Because of this complexity, as well as of wear and aging of components, faults occurs in such systems quite frequently at runtime. In order to use such drives in truly autonomous robots it is desirable that the robot is able to automatically react to such faults. Therefore, the robot needs reasoning and reconfiguration capabilities in order to be able to detect, localize and repair such faults on-line. In this paper we propose a model-based diagnosis and reconfiguration framework which allows an autonomous robot to detect and compensate faults in its drive. Moreover, we present an implementation for a real robot platform. Finally, we report experimental results which shows that the proposed framework is able to correctly cope with injected faults in the drive hardware, like broken motors.