Framework-level resource awareness in robotics and intelligent systems. Improving dependability by exploiting knowledge about system resources

Wienke J. Framework-level resource awareness in robotics and intelligent systems. Improving dependability by exploiting knowledge about system resources. Bielefeld: Universität Bielefeld; 2018.Modern robots have evolved to complex hardware and software systems. As such, their construction and maintenance have become more challenging and the potential for failures has increased. These failures and the resulting reduction of dependability have a considerable effect on the acceptance and usefulness of robotics systems in their intended applications. Even though different software engineering techniques have been developed to control dependability-critical aspects of such complex systems, the state of the art for experimental robotics and intelligent systems is that – if at all – functional properties are systematically controlled though techniques such as unit testing and simulation runs. Yet, system dependability can also be impaired if nonfunctional properties behave unexpectedly. This thesis focuses on the utilization of system resources such as CPU, memory, or network bandwidth as an important nonfunctional aspect, which has not received much systematic treatment in robotics and intelligent systems so far. Unexpected utilizations of system resources can have effects ranging from merely wasting energy and reducing a robot’s operational time to a degradation in its function due to processing delays. Even safety-critical situations can arise, for instance, if a motion planner or obstacle avoidance component cannot react before a collision. Therefore, the systematic analysis of a system’s resource utilization, a guidance of developers regarding these aspects, and testing and fault detection for unexpected resource utilization patterns are an effective contribution of this thesis towards more reliable robots. In this work I describe a concept for integrating resource awareness into component-based robotics and intelligent systems. This concept specifically addresses the often loosely controlled development process predominant in experimental research. As such, the presented methods have to be applicable without a high overhead or large changes to the evolved development methods and system structures. Within this concept, which I termed framework-level resource awareness, I have explored methods in two directions: On the one hand, a set of tools helps developers to understand and systematically control the resource utilization while developing and testing systems. On the other hand, I have applied machine learning techniques to enable autonomous reactions at runtime based on predictions about the resource utilization of system components. With the two views, this work explores novel directions for implementing resource awareness in research systems and the conducted evaluations underline the suitability of the framework-level resource awareness concept

A Middleware for Collaborative Research in Experimental Robotics

Wienke J, Wrede S. A Middleware for Collaborative Research in Experimental Robotics. In: IEEE/SICE International Symposium on System Integration (SII2011). IEEE; 2011: 1183-1190.This paper presents the Robotics Service Bus (RSB), a new message-oriented, event-driven middleware based on a logically unified bus with hierarchical structure. Major goals for the development of RSB were openness and scalability in order to integrate diverse components in the context of robotics and intelligent systems. This includes the ability to operate on embedded platforms as well as desktop computers, reduction of framework lock-in, and the integration with other middlewares. We describe the design of the RSB middleware and explain how it meets requirements which lead to a scalable and open middleware concept. These requirements are based on several application scenarios which are used to verify the applicability of RSB. Furthermore, we relate RSB to other middlewares in the robotics domain

Results of the Survey: Failures in Robotics and Intelligent Systems

Wienke J, Wrede S. Results of the Survey: Failures in Robotics and Intelligent Systems.; 2017.In January 2015 we distributed an online survey about failures in robotics and intelligent systems across robotics researchers. The aim of this survey was to find out which types of failures currently exist, what their origins are, and how systems are monitored and debugged - with a special focus on performance bugs. This report summarizes the findings of the survey

Performance regression testing and run-time verification of components in robotics systems

Wienke J, Wrede S. Performance regression testing and run-time verification of components in robotics systems. Advanced Robotics. 2017;31(22):1177-1192

The Cognitive Interaction Toolkit – Improving Reproducibility of Robotic Systems Experiments (POSTER)

Lier F, Wienke J, Nordmann A, Wachsmuth S, Wrede S. The Cognitive Interaction Toolkit – Improving Reproducibility of Robotic Systems Experiments (POSTER). Presented at the International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR), Bergamo, Italy

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

Engagement-based Multi-party Dialog with a Humanoid Robot

When a robot is situated in an environment containing multiple possible interaction partners, it has to make decisions about when to engage specific users and how to detect and react appropriately to actions of the users that might signal the intention to interact. In this demonstration we present the integration of an engagement model in an existing dialog system based on interaction patterns. As a sample scenario, this enables the humanoid robot Nao to play a quiz game with multiple participants

The Cognitive Interaction Toolkit – Improving Reproducibility of Robotic Systems Experiments

Lier F, Wienke J, Nordmann A, Wachsmuth S, Wrede S. The Cognitive Interaction Toolkit – Improving Reproducibility of Robotic Systems Experiments. In: Brugali D, Broenink JF, Kroeger T, MacDonald BA, eds. SIMPAR: International Conference on Simulation, Modeling, and Programming for Autonomous Robots. Lecture Notes in Computer Science . Vol 8810. Cham: Springer; 2014: 400-411.Research on robot systems either integrating a large number of capabilities in a single architecture or displaying outstanding perfor- mance in a single domain achieved considerable progress over the last years. Results are typically validated through experimental evaluation or demonstrated live, e.g., at robotics competitions. While common robot hardware, simulation and programming platforms yield an improved ba- sis, many of the described experiments still cannot be reproduced easily by interested researchers to confirm the reported findings. We consider this a critical challenge for experimental robotics. Hence, we address this problem with a novel process which facilitates the reproduction of robotics experiments. We identify major obstacles to experiment repli- cation and introduce an integrated approach that allows (i) aggregation and discovery of required research artifacts, (ii) automated software build and deployment, as well as (iii) experiment description, repeatable exe- cution and evaluation. We explain the usage of the introduced process along an exemplary robotics experiment and discuss our approach in the context of current ecosystems for robot programming and simulation