Welcome, I am pleased that you are visiting my tiny website! I am head of the robotics research portfolio at Bosch Research. It is truly a great privilege for me to work with so many brilliant robotics researchers from Bosch locations around the world. In addition, as Chief Expert for Robotic Systems and Software Engineering, I support the Bosch business units on a wide variety of systems and software engineering issues in robotics and, in particular, on the use of the Robot Operating System (ROS) as well as on open-source contributions to ROS and on inner source collaborations in robotics. Before joining Bosch, I worked as a software developer for TRUMPF Machine Tools on CAD/CAM. I obtained my PhD in computer science in 2010 from the University of Stuttgart.
Kaiwalya Belsare, Antonio Cuadros Rodriguez, Pablo Garrido Sánchez, Juanjo Hierro, Tomasz Kołcon, Ralph Lange (corresponding author), Ingo Lütkebohle, Alexandre Malki, Jaime Martin Losa, Francisco Melendez, Maria Merlan Rodriguez, Arne Nordmann, Jan Staschulat, and Julian von Mendel: Micro-ROS. In:
Anis Koubaa (ed.) Robot Operating System (ROS): The Complete Reference (Volume 7), Springer, pp. 3–55, 2023. (Online since 2 February 2023.)
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.
@INBOOK{Belsare_et_al_2023_Micro-ROS,
author = {Kaiwalya Belsare and Antonio Cuadros Rodriguez and Pablo Garrido S\'{a}nchez and Juanjo Hierro and Tomasz Ko\l{}con and Ralph Lange and Ingo L\"{u}tkebohle and Alexandre Malki and Jaime Martin Losa and Francisco Melendez and Maria Merlan Rodriguez and Arne Nordmann and Jan Staschulat and and Julian von Mendel},
title = {Micro-ROS},
editor = {Anis Koubaa},
booktitle = {Robot Operating System (ROS): The Complete Reference (Volume 7)},
year = {2023},
publisher = {Springer},
pages = {3--55},
doi = {10.1007/978-3-031-09062-2_2}
}
Ralph Lange, Silvio Traversaro, Oliver Lenord, and Christian Bertsch: Integrating the Functional Mock-Up Interface with ROS and Gazebo. In:
Anis Koubaa (ed.) Robot Operating System (ROS): The Complete Reference (Volume 5), Springer, pp. 187–231, 2021. (Online since 22 August 2020.)
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.
@INBOOK{Lange_et_al_2021_Integrating_the_FMI_with_ROS_and_Gazebo,
author = {Ralph Lange and Silvio Traversaro and Oliver Lenord and Christian Bertsch},
title = {Integrating the Functional Mock-Up Interface with ROS and Gazebo},
editor = {Anis Koubaa},
booktitle = {Robot Operating System (ROS): The Complete Reference (Volume 5)},
year = {2021},
publisher = {Springer},
pages = {187--231},
doi = {10.1007/978-3-030-45956-7_7}
}
Jon Arrizabalaga, Niels van Duijkeren, Markus Ryll, and Ralph Lange: A caster-wheel-aware MPC-based motion planner for mobile robotics. In
Proc. of 20th Int'l Conf. on Advanced Robotics (ICAR 2021), pp. 613–618. Virtual event. Dec 2021. IEEE.
Differential drive mobile robots often use one or more caster wheels for balance. Caster wheels are appreciated for their ability to turn in any direction almost on the spot, allowing the robot to do the same and thereby greatly simplifying the motion planning and control. However, in aligning the caster wheels to the intended direction of motion they produce a so-called bore torque. As a result, additional motor torque is required to move the robot, which may in some cases exceed the motor capacity or compromise the motion planner's accuracy. Instead of taking a decoupled approach, where the navigation and disturbance rejection algorithms are separated, we propose to embed the caster wheel awareness into the motion planner. To do so, we present a caster-wheel-aware term that is compatible with MPC-based control methods, leveraging the existence of caster wheels in the motion planning stage. As a proof of concept, this term is combined with a model-predictive trajectory tracking controller. Since this method requires knowledge of the caster wheel angle and rolling speed, an observer that estimates these states is also presented. The efficacy of the approach is shown in experiments on an intralogistics robot and compared against a decoupled bore-torque reduction approach and a caster-wheel agnostic controller. Moreover, the experiments show that the presented caster wheel estimator performs sufficiently well and therefore avoids the need for additional sensors.
@INPROCEEDINGS{Arrizabalaga_et_al_2021_A_caster-wheel-aware_MPC-based_motion_planner_for_mobile_robotics,
author = {Jon Arrizabalaga and Niels van Duijkeren and Markus Ryll and Ralph Lange},
title = {A caster-wheel-aware MPC-based motion planner for mobile robotics},
booktitle = {Proceedings of the 20th International Conference on Advanced Robotics (ICAR 2021)},
address = {Virtual event},
month = {December},
year = {2021},
publisher = {IEEE},
pages = {613--618},
doi = {10.1109/ICAR53236.2021.9659478}
}
Tobias Blass, Arne Hamann, Ralph Lange, Dirk Ziegenbein, and Björn B. Brandenburg: Automatic Latency Management for ROS 2: Benefits, Challenges, and Open Problems. In
Proc. of 27th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2021). Virtual event. May 2021. IEEE.
Robotic systems are typically subject to real-time constraints. Still, the ROS ecosystem-the most popular repository of open-source robotics software-exhibits little evidence of the use of real-time theory to bound or control worst-case response times. Hurdles to adoption are the amount of expertise required to correctly use real-time scheduling mechanisms and the inherent unpredictability of typical robotics workloads, which defy static provisioning. To overcome these hurdles, ROS-Llama, an automatic latency manager for ROS2, is proposed. Crucially, use of ROS-Llama requires only little effort and knowledge of realtime concepts. Relevant properties of ROS2 and essential requirements of the robotics domain are identified, and the conceptual and practical challenges in developing such a mostly automatic tool are discussed. Experiments on a mobile robot demonstrate the viability of the approach and show that ROS-Llama reduces the maximum observed latency under load compared to the default Linux scheduler. Finally, open problems in the underlying real-time analysis and major platform limitations in Linux and ROS2 that prevent further improvements are identified.
@INPROCEEDINGS{Blass_et_al_2021_Automatic_Latency_Management_for_ROS_2,
author = {Tobias Blass and Arne Hamann and Ralph Lange and Dirk Ziegenbein and Bj\"{o}rn B. Brandenburg},
title = {Automatic Latency Management for ROS 2: Benefits, Challenges, and Open Problems},
booktitle = {Proceedings of the 27th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2021)},
address = {Virtual event},
month = {May},
year = {2021},
publisher = {IEEE},
doi = {10.1109/RTAS52030.2021.00029}
}