Ray-tracing through numerical weather models (NWMs) is one of the most accurate methods for determining slant tropospheric delays (STDs) in microwave remote sensing. However, the massive data volumes of high-resolution NWMs create substantial I/O operations, limiting large-scale ray-tracing on general hardware. This constraint has historically necessitated parameterized tropospheric delay models, which are disseminated as standardized products (e.g., zenith delays with mapping functions and horizontal gradients). Recently, the AI-driven VAE-former algorithm revolutionized NWM compression, achieving >470:1 ratios by compressing 37 pressure level, 0.25°×0.25° ERA5 data into files smaller than surface-only VMF3 products (1°×1° resolution). This breakthrough challenges the conventional reliance on parameterized models as the sole practical solution. We quantified discrepancies in tropospheric delay parameters between original ERA5 and VAEformer-compressed CRA5 data across 2022, evaluating compression fidelity on global grids and against in-situ zenith tropospheric delay (ZTD) estimates. Results show global average precision loss from compression is10 mm). Our findings demonstrate CRA5 as a reliable ERA5 substitute, with compression-induced inaccuracies being negligible for most microwave-based remote sensing applications. This work underscores that parameterized delay modeling is no longer the exclusive pathway, enabling efficient local computation of high-precision STDs without through mapping functions and gradients.

As a competitive strategy, thek-winners-take-all (kWTA) operation is capable of selectingkwinners fromnelements (such as neurons or input signals) to be activated, while the remaining elements are suppressed as losers to be inactivated. In a dynamic task allocation on a multirobot system, the relevant information can be subtly estimated through communications among robots. However, the interference in the communication process and the time-delayed problem caused by data processing are unavoidable. Therefore, a time-delayedkWTA network considering communication interference (TDCI-kWTA) is established in this article. Different from existingkWTA networks, the TDCI-kWTA network allows robots both directed and undirected communication while eliminating communication interference. Besides, the time-delayed problem is taken into account by the TDCI-kWTA network, and the maximum delay allowed is derived from theorems. By modeling thekWTA operation as a nonlinear equation, lagging errors that exist in the solving process are eliminated due to the consideration of dynamic parameters. Theoretical analyses are given to demonstrate the convergence and robustness of the TDCI-kWTA network. Besides, simulations and experiments are further given to validate the effectiveness of the proposed network.

Robot programming is still an elaborate process in industrial assembly, especially in cases requiring a specific force profile for successfully executing an assembly step. In this work, we present the Haptic Knob, a low-cost haptic device (around 100 EUR on hardware costs) for (currently) one-dimensional robot movement programming on both position and force profiles. The Haptic Knob is equipped with a Field-Oriented Controlled (FOC) motor and a positional encoder that reads the human input position and generates feedback forces to the operator. The Haptic Knob is not equipped with an external Force/Torque sensor to measure the human input force, but works together with the impedance controller of the robot arm, which allows a dynamic force teach-in. A control interface is implemented to map the positional signal from the Haptic Knob to the movement of different actuators, as well as the force/torque signals from various devices to the Haptic Knob for force feedback. We showcase and validate the proposed hardware and control interface on the industrial use case of automotive fuse box assembly.

This work aims at facilitating the integration of industrial robots and other devices such as their gripper tools at small and medium-sized enterprises (SMEs). For this purpose, an intuitive user interface for the skill-based instruction of robot systems is combined with standardized opc UA-based skill interfaces that support various hardware and software resources from different manufacturers. Special emphasis is laid on supporting different user groups with varying levels of expertise. Production system engineers are provided with a detailed graphical user interface (GUI) for hierarchically defining new skills by combining preexisting ones. System operators receive a simplified view with limited complexity for process instruction and changing high-level task parameterizations. The skills and relevant semantic context knowledge about products, processes, and resources (PPR) are formally represented in OWL ontologies to enable hardware-agnostic process descriptions that can be deployed to different production environments, while automatically deriving parameterizations for skill invocations. The proposed concept has been qualitatively evaluated in two real-world robot workcells based on a smartphone accessory packaging use case.

The pseudolite positioning system can enhance Global Navigation Satellite System (GNSS) by improving satellite geometry and providing independent services in environments where GNSS is unavailable. This paper investigates a GNSS/Pseudolite integrated positioning system and verifies its positioning service performance in urban canyon environments. The experimental results indicate that in challenging urban canyon environments, the number of visible satellites for the GNSS significantly decreases, leading to a substantial decline in positioning accuracy. The GNSS/Pseudolite integrated system demonstrates its advantages. Even in favorable experimental conditions, compared to using GNSS PPP alone, the GNSS/Pseudolite integrated PPP improves horizontal accuracy by 10% and 3D accuracy by 12%. When the obstruction is severe, such as when the cut-off elevation angle is 50°, the GNSS/Pseudolite integrated PPP improves horizontal accuracy by ${7 2. 5 \%}$ and 3D accuracy by 79.2% compared to GNSS PPP. In challenging urban canyon environments, the GNSS/Pseudolite integrated system can still provide high-precision positioning services.

In this paper, we introduce a knowledge-based Programming by Demonstration (kb-PbD) paradigm to facilitate robot programming in small and medium-sized enterprises (SMEs). PbD in production scenarios requires the recognition of product-specific actions but faces challenges in the lack of suitable and comprehensive datasets, due to the large variety of involved hand actions across different production scenarios. To address this issue, we utilize standardized grasp types as the fundamental feature to recognize basic hand movements, where a Long Short-Term Memory (LSTM) network is employed to recognize grasp types from hand landmarks. The product-specific actions, aggregated from the basic hand movements, are formally modeled in a semantic description language based on the Web Ontology Language (OWL). Description Logic (DL) is used to define the actions with their characteristic properties, which enables the efficient classification of new action instances by an OWL reasoner.The semantic models of hand actions, robot tasks, and work-cell resources are interconnected and stored in a Knowledge Base (KB), which enables the efficient pair-wise translation between hand actions and robot tasks. For the reproduction of human assembly processes, actions are converted to robot tasks via skill descriptions, while reusing the action parameters of involved objects to ensure product integrity. We showcase and evaluate our method in an industrial production setting for control cabinet assembly. Demonstration video available at: https://kb-pbd.github.io/.

Accurate tropospheric delay forecasts are imperative for microwave-based remote sensing techniques, playing a pivotal role in early warning and forecasting of natural disasters such as tsunamis, heavy rains, and hurricanes. Nevertheless, conventional methods for forecasting tropospheric delays entail substantial computational resources and high network transmission speeds, thereby restricting their real-time applicability in remote sensing operations. In this study, we introduce a novel approach to derive forecasted tropospheric delays using artificial intelligence (AI) weather forecast foundation models (FMs), exemplified by Huawei Cloud Pangu-Weather, Google DeepMind GraphCast, and Shanghai AI Lab FengWu. We assess the accuracy of these forecasts on a global scale employing fifth-generation ECMWF atmospheric re-analysis of the global climate (ERA5) (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5), ground-based Global Navigation Satellite System (GNSS), and in situ radiosonde (RS) measurements as reference data. Our results show that the FM-based scheme outperforms traditional methods in both forecast accuracy and length, with the ability to provide high-accuracy tropospheric delay parameters locally for 15-day forecasts at any location within minutes. Furthermore, the FM scheme still maintains accuracy better than empirical models when forecasting up to ten days in advance. This research demonstrates the potential of AI weather forecast FMs in delivering high-precision tropospheric delay medium-range forecasts and improvements for real-time remote sensing applications.

Digital tools for handling the whole product engineering phase are getting more and more important in the context of Industry 4.0 and an increasing product variety. However, especially in small and medium-sized enterprises, a lot of information about product development and production is stored in different documents or isolated data silos. A promising way to arrive at a solution is to model data and knowledge with ontologies and enrich it with context information. This paper presents a concept and a showcase implementation of a company-internal and personalized assistance system for an end-to-end digital product engineering process. We combine a generic and cost-efficient human assistance solution focusing on social aspects and a company-wide knowledge graph to create a seamless and highly integrated data structure that assists many stakeholders in the product engineering process, from product designers to assembly workers. As a result, more complex products can be handled and the product engineering process can be accelerated.