Software-in-the-Loop Simulation of a Planetary Rover

The development of autonomous navigation algorithms for planetary rovers often hinges on access to rover hardware. Yet this access is usually very limited. In order to facilitate the continued development of these algorithms even when the hardware is temporarily unavailable, simulations are used. To minimize any additional work, these simulations must tightly integrate with the rover’s software infrastructure. They are then called Software-in-the-Loop simulators. In preparation for the 2015 DLR SpaceBot Camp, a simulation of the DLR LRU rover became necessary to ensure a timely progress of the navigation algorithms development. This paper presents the Software-in-the-loop simulator of the LRU, including details on the implementation and application

The LRU Rover for Autonomous Planetary Exploration and its Success in the SpaceBotCamp Challenge

The task of planetary exploration poses many challenges for a robot system, from weight and size constraints to sensors and actuators suitable for extraterrestrial environment conditions. As there is a significant communication delay to other planets, the efficient operation of a robot system requires a high level of autonomy. In this work, we present the Light Weight Rover Unit (LRU), a small and agile rover prototype that we designed for the challenges of planetary exploration. Its locomotion system with individually steered wheels allows for high maneuverability in rough terrain and the application of stereo cameras as its main sensor ensures the applicability to space missions. We implemented software components for self-localization in GPS-denied environments, environment mapping, object search and localization and for the autonomous pickup and assembly of objects with its arm. Additional high-level mission control components facilitate both autonomous behavior and remote monitoring of the system state over a delayed communication link. We successfully demonstrated the autonomous capabilities of our LRU at the SpaceBotCamp challenge, a national robotics contest with focus on autonomous planetary exploration. A robot had to autonomously explore a moon-like rough-terrain environment, locate and collect two objects and assemble them after transport to a third object - which the LRU did on its first try, in half of the time and fully autonomous

Preliminary Results for the Multi-Robot, Multi-Partner, Multi-Mission, Planetary Exploration Analogue Campaign on Mount Etna

This paper was initially intended to report on the outcome of the twice postponed demonstration mission of the ARCHES project. Due to the global COVID pandemic, it has been postponed from 2020, then 2021, to 2022. Nevertheless, the development of our concepts and integration has progressed rapidly, and some of the preliminary results are worthwhile to share with the community to drive the dialog on robotics planetary exploration strategies. This paper includes an overview of the planned 4-week campaign, as well as the vision and relevance of the missiontowards the planned official space missions. Furthermore, the cooperative aspect of the robotic teams, the scientific motivation, the sub task achievements are summarised

Finally! Insights into the ARCHES Lunar Planetary Exploration Analogue Campaign on Etna in summer 2022

This paper summarises the first outcomes of the space demonstration mission of the ARCHES project which could have been performed this year from 13 june until 10 july on Italy’s Mt. Etna in Sicily. After the second postponement related to COVID from the initially for 2020 planed campaign, we are now very happy to report, that the whole campaign with more than 65 participants for four weeks has been successfully conduced. In this short overview paper, we will refer to all other publication here on IAC22. This paper includes an overview of the performed 4-week campaign and the achieved mission goals and first results but also share our findings on the organisational and planning aspects

Simulation of Artificial Intelligence Agents using Modelica and the DLR Visualization Library

This paper introduces a scheme for testing artificial intelligence algorithms of autonomous systems using Modelica and the DLR Visualization Library. The simulation concept follows the ’Software-in-the-loop’ principle, whereas no adaptations are made to the tested algorithms. The environment is replaced by an artificial world and the rest of the autonomous system is modeled in Modelica. The scheme is introduced and explained by using the example of the ROboMObil, which is a robotic electric vehicle developed by the DLR’s Robotics and Mechatronics Center

PySimulator – A Simulation and Analysis Environment in Python with Plugin Infrastructure

A new simulation and analysis environment in Python is introduced. The environment provides a graphical user interface for simulating different model types (currently Functional Mockup Units and Modelica Models), plotting result variables and applying simulation result analysis tools like Fast Fourier Transform. Additionally advanced tools for linear system analysis are provided that can be applied to the automatically linearized models. The modular concept of the software enables easy development of further plugins for both simulation and analysis

The DLR Rover Simulation Toolkit

Autonomous exploration rovers are currently the primary means of research on extra-terrestrial bodies. Due to the circumstances of their deployment it is vital to ensure their unassisted performance in a harsh environment. Preliminary simulations are therefore indispensable. To facilitate these simulations we introduce the DLR Rover Simulation Toolkit (RST). The RST constitutes a framework of libraries, allowing engineers to quickly assemble digital rover twins, particularly for early design phases. Enabled by the modelling language Modelica it covers a wide range of aspects from different domains in one unifying framework. This paper establishes in detail the RST’s structure and design decisions before showing its practical application in a Software-in-the loop (SiL) simulator, elaborating on future enhancements and its use during collaborative engineering studies in the DLR Systems and Control Innovation Lab

Entwicklung einer intelligenten Sensoreinsatzsteuerung zur 360°-Stereo Umgebungserfassung eines autonomen Elektromobils

Teil- und Vollautonome Fahrzeuge sowie Fahrerassistenzsysteme benötigen Sensordatenerfassung und -auswertung zur Wahrnehmung ihrer Umgebung. Diese gestaltet sich in aller Regel sehr rechenaufwendig und ist somit mittels der eingeschränkten Ressourcen eines Fahrzeuges lediglich mit begrenzter Wiederholrate möglich. Um die vorhandene Rechenkraft möglichst optimal einzusetzen wurde zunächst eine Umgebungsanalyse geschaffen, die im weiteren dazu genutzt wurde, einen Sensoreinsatzplaner zu entwickeln, der die vorhandenen Ressourcen intelligent auf Grundlage der Umgebungsbeschaffenheit einteilt. Die Entwicklung dieses Systems wird detailiert erklärt und an Hand von Beispielen verifiziert

Heterogeneous,multi-tier wheel ground contact simulation for planetary exploration

Today's growing scientific interest in extraterrestrial bodies increases the necessity of extended mobility on these bodies. Thus, planetary exploration systems are facing new challenges in terms of mission planning, obstacle and soil traversability. In order to fit the tight schedules of space missions and to cover a large variety of environmental conditions, experimental test setups are complemented by numerical simulation models used as virtual prototypes. In this context we present an integrated simulation environment which allows for using different available contact models, ranging from simple but real-time capable approximations based on rigid-body modeling techniques up to very accurate solutions based on Discrete Element Method (DEM). For this work, a one-point Bekker based approach and the so-called Soil Contact Model (SCM), which is a multi-point extension of the \textsc{Bekker}-\textsc{Wong} method taking soil deformation into account, are used. These two contact models are applied for homogeneous models with only one type of contact model for all wheels as well as for a heterogeneous multi-tiered model with different contact models for the wheels. It will be shown that the multi-tiered approach enhances the simulation result accuracy compared to the results of a homogeneous model with a low level of detail while speeding up the simulation in comparison to a homogeneous higher tier model

Compile-time dynamic and recursive data structures in Modelica

The current Modelica Standard (v3.3) does not support dynamic or recursive data structures. For many applications this constitutes a serious restriction rendering certain implementations either impossible or requires elaborate and unelegant constructs. In this paper we will show that support for dynamic and recursive data structures can be implemented in the Modelica IDE Dymola using a variety of advanced constructs. This proves the principle viability of the then proposed inclusion of those data structures in the Modelica Standard