Thorsten Ropertz

Verification of behaviour networks using finite-state automata

This paper addresses the problem of verifying properties of behaviour-based systems used for controlling robots. A behaviour-based system typically consists of many interconnected components, the behaviours, which in combination realise the systems overall functionality. The connections between the behaviours are crucial for the correct operation of a system. Therefore, key properties of behaviour-based systems are verifiable based on their behaviour interconnections. In this paper, it is described how behaviour-based networks can be (automatically) modelled using finite-state machines and how model checking with the Uppaal toolbox can then be applied for verification and analysis tasks.

Tool-assisted verification of behaviour networks

This paper deals with the problem of assisting developers when verifying properties of complex behaviour-based systems. A central aspect of behaviour-based systems is the interaction between the behaviours, as a lot of the functionality of a system typically arises from this interaction. Hence, verification has to deal with the specialities of behaviour interaction. Previous work has introduced a concept for modelling behaviour-based systems as networks of finite-state automata and for applying model checking as verification technique. As the manual verification of large networks is tedious and errorprone, the work at hand introduces a concept for assisting developers by partly automating the verification process. The applicability of the presented approach is demonstrated using the behaviour-based control system of an autonomous bucket excavator.

Soft Robot Control with a Behaviour-Based Architecture

In this chapter, we explain how behaviour-based approaches can be used to control soft robots. Soft robotics is a strongly growing field generating innovative concepts and novel systems. The term “soft” can refer to the basic structure, the actuators, or the sensors of these systems. The soft aspect results in a number of challenges that can only be solved with new modelling, control, and analysis methods whose novelty matches those of the hardware. We will present prior achievements in the area of behaviour-based systems and suggest their application in soft robots with the aim to increase the fault tolerance while improving the reaction to unexpected disturbances.

Quantitative Aspects of Behaviour Network Verification

This paper presents quantitative aspects of an approach for the modelling and verification of behaviour networks published previously and describes the application of said modelling technique to a complex coordinating behaviour. In order to decrease the number of interconnection failures in behaviour networks, verification techniques focusing on behaviour interaction can be applied. In previous work, the authors have introduced a novel approach for modelling behaviour networks as networks of finite-state automata, to which model checking can be applied as verification technique. This paper presents how the approach can be used to model complex behaviours and provides calculations of the numbers of states, transitions, and state variables in the resulting automata.

Behavior-Based Control for Safe and Robust Navigation of an Unimog in Off-Road Environments

Autonomously driving vehicles become more and more present in in road traffic, but in cluttered off-road environments the technological challenges for autonomous driving are currently much higher. Nonetheless, commercial vehicles operating autonomously in off-road environments can be easier economically exploited than within on-road scenarios due weaker legal restrictions. The project Driving of Commercial Vehicles in Off-Road Environments on the Example of the Unimog develops control concepts for a safe and robust navigation of commercial vehicles focusing on low speed locomotion in unstructured environments as construction sites, agricultural applications, forest, or search and rescue areas. Thereby, a stepwise automation is emphasized starting with driver-action recommendations in form of assistance systems evolving to fully autonomous solutions. This paper presents the behavior-based control architecture REACTiON tailored to the described scenarios and challenges. It provides standardized interfaces for different abstraction levels of control and perception. The architecture is applied to an Unimog special truck. Therefore a reactive low level safety system, differential, gear, and tire-pressure controllers, tailored to the vehicle, are implemented and tested in a simulation environment.

Cooperation and Communication of Autonomous Tandem Rollers in Street Construction Scenarios

In the area of street construction, multiple heterogeneous construction machines have to cooperate in order to fulfill their tasks. While current manual coordination exploits error-prone hand-gestures, and other visual or acoustical communication, the ongoing automation of construction sites requires digital wireless high-bandwidth communication. Thereby, many-faceted communication links are required to meet the application requirements while coping with the unstructured and possibly infrastructure-less environment. In this paper, a new communication approach is presented which seamlessly integrates these multiquality wireless connections into the behavior-based control system approach to allow for a safe and robust autonomous operation of construction machine fleets.

Quality-Based Behavior-Based Control for Autonomous Robots in Rough Environments.

Autonomous navigation in unstructured environments is a challenging task for which behavior-based control systems proved to be suitable due to their inherent robustness against unforeseen situations. But especially the robust perception is still an unsolved problem leading to severe system failures. This paper faces the perception problem by introducing a new data quality-based perception module based on the integrated Behavior-Based Control (iB2C) architecture. Therefore, a new concept of data quality in behavior-based systems and methods for quality-based data fusion are developed while taking advantage of the modularity, extensibility and traceability of the existing architecture. To demonstrate its capabilities, a perception network for robot localization is derived and its outcomes are compared to an state of the art localization filter in simulation and in a real world scenario as well.

A Behavior-based Architecture for Excavation Tasks

The paper describes a software architecture for autonomous excavation tasks. For this, it uses a behavior-based approach which allows adaptation to the changing environment and unexpected events. As part of the presented architecture, basic primitives are defined which solve spatially limited motions. These can be activated as and when required. It is possible to have multiple of them running in parallel. Normally, an excavation task can be split up into several different phases. During these phases, the primitives will be activated as needed. To be reusable, the primitives are designed in a very general way. To show the performance of the presented architecture, an example application is built up which can be used to dig a trench. This application is implemented on a system running on a real backhoe loader.