Werner Brockmann

ORCA – towards an organic robotic control architecture

We are working on a modular and self-organizing component based software architecture for autonomous mobile robots. To reach a certain degree of fault-tolerance without analyzing all kinds of possible error conditions, “Organic Components” will be added to the system to detect recognize variations from a defined “normal state” and then try to find counter measures. Once an action is identified to help in certain situations, the component will store that information and use it if a similar situation is reached later. The system will be self-optimizing and self-healing. We started to evaluate adaptive filters as one possible implementation for components detecting deviations from the normal system state.

A generic concept to increase the robustness of embedded systems by trust management

Embedded systems permeate increasingly complex, unstructured and non-stationary environments like e.g. car driver assistance systems and mobile robots. The demands on their dependability hence rise, especially in case of human interaction. But as the complexity increases, several sources of uncertainty are likely to increase as well up to an unacceptable level. Reasons are for instance noise, vagueness and ambiguity of information about the environment and also disturbances and anomalies in the interaction with it. Because many operations are safety-critical, this paper presents trust management as a generic approach for addressing these uncertainties. It builds on modeling the uncertainty of the information about the state of the system and about the interaction with its environment explicitly by so-called trust signals. These are propagated and processed throughout the whole embedded system in order to change from a high performance behavior in case of certain, hence trustworthy information to a robust behavior with less performance in case of uncertain information. The trust management approach works without a formal model of the application and is hence easy to use. Its application is outlined by a simple sensor fusion example.

Stable Classification in Environments with Varying Degrees of Uncertainty

Most practical signal processing problems have to deal with uncertainties, e. g., due to noisy input data. Usual strategies to do this are based on estimating these uncertainties by statistical methods in advance. For some systems with multi-staged signal processing it is possible to identify these estimates at runtime and to relate a degree of certainty to them. If such degrees of certainty are known for input signals, e. g. by earlier stages of processing, this knowledge can be used to get a more robust or accurate result in classification tasks in the later stages, even if they vary at runtime. In this paper we thus introduce an approach to extend support vector machines to incorporate such known uncertainties at runtime, given as certainty degrees. Based on the known certainty of each input, classification depends more on certain inputs and gradually less on uncertain input data. This is done by changing the decision (kernel) function online, i. e., during operation. An artificial two-dimensional dataset is used to visualize the effects of this extension. And the application to three different datasets is a first benchmark showing that the resulting classification quality increases when known uncertainties are considered.

The Incremental Risk Functional: Basics of a Novel Incremental Learning Approach

Incremental learning gets increasing attention in research and practice as it has the advantages of continuous adaptation and handling big data with a low computation and memory demand at the same time. Several approaches have been proposed recently for online learning, but only few work has been done to regard the influence of the approximation structure. Hence, we introduce the incremental risk functional which directly incorporates knowledge about the approximation structure into its parameter update. Exemplary, we apply this approach to regression estimation through linear-in-parameter approximators. We show that the resulting learning algorithm converges and changes the global functional behavior only as little as necessary with every learning step, thus resulting in a stable incremental learning approach.

Application of the Organic Robot Control Architecture ORCA to the Six-Legged Walking Robot OSCAR

Walking robots are complex machines, which are challenging to engineer and to program. In order to master this complexity, in this article Organic Computing (OC) principles in terms of self-organisation, self-reconfiguration and self-healing are applied to a six-legged walking robot named OSCAR (Organic Self-Configuring and Adapting Robot). The Organic Robot Control Architecture ORCA, developed in the same project, provides the architectural framework. OC principles are employed on all layers of the hierarchical robot control system starting at the reflexive layer with gait generation and reflexes over the reactive behavioural layer up to the deliberative planning layer. Many experimental evaluations with OSCAR have shown that the robot is able to flexibly adapt to internal faults as well as to unforeseen environmental situations and thus continues its mission in the best still possible way.

Concept for Controlled Self-optimization in Online Learning Neuro-fuzzy Systems

Many modern control systems, e.g., in automotive or robotic applications get increasingly complex and hard to design. This is due to the complex interactions of their internal subsystems, but additionally, these systems operate in a dynamically changing, complex environment. The Organic Computing (OC) initiative tries to cope with the resulting engineering demands by introducing emergence and self-x properties into the systems (e.g., self-organization, self-optimization). Within this context, we focus on control systems which adapt their behavior autonomously by learning.

A Framework for Controlled Self-optimisation in Modular System Architectures

Organic Computing tackles design issues of future technical systems by equipping them with self-x properties. A key self-x feature is self-optimisation, i.e. the system’s ability to adapt its dynamic behaviour to its current environment and requirements. In this article, it is shown how self-optimisation can be realised in a safe and goal-directed way, but also why it has to be enhanced and embedded into a suitable, modular system architecture. Then, a suitable framework for controlled self-optimisation is developed, which enables the system designer to give a priori guarantees of important dynamic system properties, and which ensures the system’s ability to cope dynamically with anomalies. The key features are online machine learning, which is complemented by incremental, local regularisation in a local Observer/Controller architecture as well as expressing anomalies by health signals, which are exploited to guide the learning process dynamically in order to achieve fast, but safe learning.

ORCA: An Organic Robot Control Architecture

Mastering complexity is one of the greatest challenges for future dependable information processing systems. Traditional fault tolerance techniques relying on explicit fault models seem to be not sufficient to meet this challenge. During their evolution living organisms have, however, developed very effective and efficient mechanisms like the autonomic nervous system or the immune system to make them adaptive and self-organising. Thus, they are able to cope with anomalies, faults or new unforeseen situations in a safe way. Inspired by these organic principles the control architecture ORCA (Organic Robot Control Architecture) was developed. Its aim is to transfer self-x properties from organic to robotic systems. It is described in this article with a specific focus on the way ORCA deals with dynamically changing uncertainties and anomalies.

Instantaneous anomaly detection in online learning fuzzy systems

In the field of self-optimizing automation systems, incremental local learning is an important technique. But especially in case of closed loop coupling, learnt anomalies may have a negative influence on the entire future of the evolving system. In the worst case, this may result in unstable or chaotic system behavior. Thus it is crucial to detect anomalies in online learning systems instantaneously to be able to take immediate counteractions. This paper presents an intuitive approach how to detect anomalies in incrementally and locally learning TS-fuzzy systems by looking at local meta-level characteristics of the learnt function. The practical feasibility of this approach is then investigated in experiments with a real pole-balancing cart.

Stable On-Line Learning with Optimized Local Learning, But Minimal Change of the Global Output

This work presents a novel approach to on-line learning regression. The well-known risk functional is formulated in an incremental manner that is aggressive to incorporate a new example locally as much as possible and at the same time passive in the sense that the overall output is changed as little as possible. To achieve this localized learning, knowledge about the model structure of the approximator is utilized to steer the adaptation of the parameter vector. We present a continuously adapting first order learning algorithm that is stable, even for complex model structures and low data densities. Additionally, we present an approach to extend this algorithm to a second order version with greater robustness but lower flexibility. Both algorithms are compared to state of the art methods as well on synthetic data as on benchmark datasets to show the benefits of the new approach.