The use of simulation in robotics is increasingly widespread for the purpose of testing, synthetic data generation 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.

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.

Robots are networks of a variety of computing devices, such as powerful computing platforms but also tiny microcontrollers. The Robot Operating System (ROS) is the dominant framework for powerful computing devices. While ROS version 2 adds important features like quality of service and security, it cannot be directly applied to microcontrollers because of its large memory footprint. The micro-ROS project has ported the ROS 2 API to microcontrollers. However, the standard ROS 2 concepts are not enough for real-time performance: In the ROS 2 release “Foxy”, the standard ROS 2 Executor, which is the central component responsible for handling timers and incoming message data, is neither real-time capable nor deterministic. Domain-specific requirements of mobile robots, like sense-plan-act control loops, cannot be addressed with the standard ROS 2 Executor. In this paper, we present an advanced Executor for the ROS 2 C API which provides deterministic scheduling and supports domain-specific requirements. A proof-of-concept is demonstrated on a 32-bit microcontroller.

For most applications in mobile robotics, precise state estimation is essential. Typically, the state estimation is based on the fusion of data from different sensors. In practice, these sensors differ in their characteristics and measurements are available to the sensor fusion algorithm only with delay. Based on a brief survey of sensor fusion approaches that consider delayed and out-of-sequence availability of measurements, suitable approaches for applications in mobile robotics are identified. In a consumer robot use-case, experiments show that the estimation is biased if delayed availability of measurements is not considered appropriately. However, if delays are considered in the fusion process, the estimation bias is reduced to almost zero and in consequence, the estimation performance is distinctly improved. Two computational favorable approximative methods are described and provide almost the same accuracy as - theoretically optimal - brute-force filter recalculation at much lower and well-distributed computational costs.

Modern lightweight dual-arm robots bring the physical capabilities to quickly take over tasks at typical industrial workplaces designed for workers. In times of mass-customization, low setup times including the instructing/specifying of new tasks are crucial to stay competitive. We propose a constraint programming approach to simultaneous task allocation and motion scheduling for such industrial manipulation and assembly tasks. The proposed approach covers dual-arm and even multi-arm robots as well as connected machines. The key concept are Ordered Visiting Constraints, a descriptive and extensible model to specify such tasks with their spatiotemporal requirements and task-specific combinatorial or ordering constraints. Our solver integrates such task models and robot motion models into constraint optimization problems and solves them efficiently using various heuristics to produce makespan-optimized robot programs. The proposed task model is robot independent and thus can easily be deployed to other robotic platforms. Flexibility and portability of our proposed model is validated through several experiments on different simulated robot platforms. We benchmarked our search strategy against a general-purpose heuristic. For large manipulation tasks with 200 objects, our solver implemented using Google's Operations Research tools and ROS requires less than a minute to compute usable plans.

Preprogramming of tasks still plays an important role in complex robotic systems despite the advances in automated planning and symbolic learning. Often, it is desired that end-users implement further tasks to adapt the robotic application to their needs. These user-defined tasks have to meet safety and integrity constraints for protecting the robotic platform and its users. We introduce a verifiable task specification language (vTSL) that enables to automatically prove that a task specification satisfies a set of predefined or task-specific constraints. We illustrate our approach using an example of a self-driving vehicle for intra-logistics and report experiences with two commercial applications.

Scaling heterogeneous information systems (HIS) to thousands of sources poses particular challenges to source discovery. It requires a powerful formalism for describing the contents of the sources in a concise manner and for formulating compatible queries as well as a suitable structure for indexing and retrieving the source descriptions efficiently.

Context-aware applications rely on models of the physical world. Within the Nexus project, we envision a World Wide Space which provides the conceptual and technological framework for integrating and sharing such context models in an open, global platform of context providers. In our ongoing research we tackle important challenges in such a platform including distributed processing of streamed context data, situation recognition by distributed reasoning, efficient management of context data histories, and quality of context information. In this paper we discuss our approach to cope with these challenges and present an extended Nexus architecture.

Moving objects databases (MODs) have been proposed for managing trajectory data, an important kind of information for pervasive applications. To save storage capacity, a MOD generally stores simplified trajectories only. A simplified trajectory approximates the actual trajectory of the mobile object according to a certain accuracy bound. In order to minimize the costs of communicating position information between mobile object and MOD, the trajectory simplification should be performed by the mobile object. To assure that the MOD always has a valid simplified trajectory of the remote object, we propose the generic remote trajectory simplification protocol (GRTS) allowing for computing and managing a simplified trajectory in such a system in real-time. We show how to combine GRTS with existing line simplification algorithms for computing the simplified trajectory and analyze trade-offs between the different algorithms. Our evaluations show that GRTS outperforms the two existing approaches by a factor of two and more in terms of reduction efficiency. Moreover, on average, the reduction efficiency of GRTS is only 12% worse compared to optimal offline simplification.

Space-partitioned Moving Objects Databases (SP-MODs) allow for the scalable, distributed management of large sets of mobile objects' trajectories by partitioning the trajectory data to a network of database servers.

Moving objects databases (MODs) store objects’ trajectories by spatiotemporal polylines that approximate the actual movements given by sequences of sensed positions. Determining such a polyline with as few vertices as possible under the constraint that it does not deviate by more than a certain accu

Many location-based systems rely on fine-grained tracking of mobile objects that determine their own locations with sensing devices like GPS receivers. For these objects, energy is a very valuable and limited resource. A distance-based reporting protocol can be employed to reduce the energy they consume by sending position updates. However, the energy required for position sensing has not been considered in the past. In this paper, we study how the resulting energy consumption from both sensing and update operations can be reduced for distance-based reporting. We show that significant savings are achieved by sending position updates earlier than actually required. For uniform movement, we derive the minimal power consumption analytically. Subsequently, two novel online heuristics are proposed that control the sending of position updates at runtime. Their effectiveness is validated by extensive simulations.