Dr. Ralph Lange

Behavior Trees (BTs) have emerged as a modular and reactive framework for robotic decision-making. However, the lack of a definitive reference for execution semantics limits their applicability in safety-critical systems. This paper contributes a solution by providing a new translation of BTs to Petri Nets (PNs) to enable verification in scenarios where the embedding context of the BT is modeled also as a PN. This ensures that safety and response properties about the overall robotic system can be assessed using state-of-the-art model checkers. Our method takes a BT, translates each node to a so-called PN Template and then composes the templates to obtain a single PN. Additionally, we introduce optimization strategies to reduce the size of the final model, improving computational efficiency while preserving verification accuracy. We report evaluation results on different case studies, including industry-related ones, demonstrating the feasibility and the scalability of our approach.

The use of simulation in robotics is increasingly widespread for the purpose of testing, synthetic data gener- ation and skill learning. A relevant aspect of simulation for a variety of robot applications is physics-based simulation of robot-object interactions. This involves the challenge of accurately modeling and implementing different mechanical systems such as rigid and deformable bodies as well as their interactions via constraints, contact or friction. Most state-of- the-art physics engines commonly used in robotics either cannot couple deformable and rigid bodies in the same framework, lack important systems such as cloth or shells, have stability issues in complex friction-dominated setups or cannot robustly prevent penetrations. In this paper, we propose a framework for strongly coupled simulation of rigid and deformable bodies with focus on usability, stability, robustness and easy access to state-of-the-art deformation and frictional contact models. Our system uses the Finite Element Method (FEM) to model deformable solids, the Incremental Potential Contact (IPC) approach for frictional contact and a robust second order optimizer to ensure stable and penetration-free solutions to tight tolerances. It is a general purpose framework, not tied to a particular use case such as grasping or learning, it is written in C++ and comes with a Python interface. We demonstrate our system’s ability to reproduce complex real-world experiments where a mobile vacuum robot interacts with a towel on different floor types and towel geometries. Our system is able to reproduce 100% of the qualitative outcomes observed in the laboratory environment. The simulation pipeline, named Stark (the German word for strong, as in strong coupling) is made open-source.

The micro-ROS stack (micro.ros.org) integrates microcontrollers seamlessly with standard ROS 2 and brings all major ROS concepts such as nodes, publisher, subscriptions, parameters, and lifecycle onto deeply embedded systems. This enables accessing all software using the same ROS tools and APIs, regardless of the underlying computing hardware and operating system. Micro-ROS supports a broad spectrum of microcontroller families and the main open-source real-time operating systems like FreeRTOS, Zepyhr, or NuttX. It supports various microcontroller- or RTOS-specific build systems and provides ROS-CLI-based build tooling. Micro-ROS is an open-source project that has been under development at github.com/micro-ROS since 2018. It was initiated by the EU-funded innovation activity OFERA. During the the last two years, micro-ROS has been adopted by a relevant group of professional users inside the ROS community. In this chapter, we give a technical introduction to the micro-ROS stack including APIs and architecture, as well as the corresponding middleware Micro XRCE-DDS. Furthermore, tutorials for a simple application with an ESP32 microcontroller are provided together with a report on three use-cases from industrial and research applications.

The Functional Mock-up Interface (FMI) is a widely used industry standard for exchange and co-simulation of dynamic models as Functional Mock-up Units (FMU). It is supported by more than 100 modeling and simulation tools. In this chapter, we present two implementations of FMI that bridge the gap between these tools and the ROS and Gazebo community: First, the fmi_adapter package for running/simulating FMUs in ROS nodes, from https://github.com/boschresearch/fmi_adapter_ros2. Second, the gazebo-fmi package for integrating FMUs with Gazebo, from https://github.com/robotology/gazebo-fmi. After an introduction to the FMI standard, this chapter provides step-by-step, hands-on examples for both packages, followed by interface descriptions and selected implementation details. In addition to these tutorial-style sections, the chapter also provides comprehensive descriptions of two use-cases. First, it explains how the fmi_adapter enabled a convenient model-based control design workflow for a self-driving vehicle for industrial logistics. Second, it reports on the simulation of electrical actuators in Gazebo from a Modelica model.