In the pursuit of fully autonomous robotic systems capable of taking over tasks traditionally performed by humans, the complexity of open-world environments poses a considerable challenge. Addressing this imperative, this study contributes to the field of Large Language Models (LLMs) applied to task and motion planning for robots. We propose a system architecture that orchestrates a seamless interplay between multiple cognitive levels, encompassing reasoning, planning, and motion generation. At its core lies a novel replanning strategy that handles physically grounded, logical, and semantic errors in the generated plans. We demonstrate the efficacy of the proposed feedback architecture, particularly its impact on executability, correctness, and time complexity via empirical evaluation in the context of a simulation and two intricate real-world scenarios: blocks world, barman and pizza preparation.

How can a robot provide unobtrusive physical support within a group of humans? We present Attentive Support, a novel interaction concept for robots to support a group of humans. It combines scene perception, dialogue acquisition, situation understanding, and behavior generation with the common-sense reasoning capabilities of Large Language Models (LLMs). In addition to following user instructions, Attentive Support is capable of deciding when and how to support the humans, and when to remain silent to not disturb the group. With a diverse set of scenarios, we show and evaluate the robot’s attentive behavior, which supports and helps the humans when required, while not disturbing if no help is needed.

Technical decisions that offer short-term gains but result in long-term disturbances and costs are often made due to the insufficient appreciation or underestimation of their scope, impact, and remedial actions. Technical Debt (TD) is a metaphor that embodies such phenomena and poses a particularly harmful threat when interdisciplinary teams interact and collaborate. The study presents new methods analyzing cross-company TD characteristics and positive TD best practice use cases gathered from 47 semi-structured expert interviews in the industrial automation domain. The three most important life cycle phases, the requirement, design, and testing phases, are addressed. The analysis demonstrates that, like adverse TD incidents, cross-life-cycle ripple effects can be advantageous or disadvantageous to the system. By implementing one measure, the system can benefit in multiple life-cycle phases and even disciplines. Additionally, the measures identified can prevent and eliminate numerous TD types and subtypes. The study elaborates on 31 measures that address 129 TD subtypes and proposes a systematic lessons-learned-based step for managing TD incidents in the automation sector.

The concept of data-centered companies is of rapidly increasing interest within the process industry due to applications like data analysis, plant optimization and visualization. However, chemical companies face high barriers regarding the wide variety and heterogeneity of data along the brownfield plant lifecycle caused by the long history of tool-focused enterprises. This paper proposes an Information Model architecture for the process industry and an approach to overcome the hurdles associated with brownfield plants and information mapping.

Technical Debt (TD) is a significant concern in software development, particularly when interdisciplinary teams collaborate and interact. The goal of the study is to investigate TD causal chains and patterns in the industrial automation sector by analyzing 123 mechatronic TD incidents from 47 expert interviews across ten companies. Findings reveal that Requirements, Process, and Test TD are most common, while Build, Versioning, Manufacturing, Code, and Maintenance/Service TD are less frequent. Key causes include ”other priorities”, ”lack of time”, ”historically grown products”, ”lack of market analysis” and ”copy-paste-modify without revising tolerances.” The research identifies correlations between TD subtypes and causes/consequences in relation to company size, experts’ experience, and position, utilizing the Chi-square test and PrefixSpan algorithm. The study also maps the contagious character of TD using Neo4J graphical representation. This first in-depth analysis of TD causal chains in industrial automation contributes qualitatively to understanding TD patterns, helping researchers and practitioners assess TD contagiousness, comprehend its effects, prevent diffusion, and develop repayment strategies To the best of our knowledge, this study’s quantitative analysis approach provides the foundation that will enable future research identifying TD metrics and TD management in multidisciplinary engineering.

In an increasingly competitive market, short times to market and flexibility are key to success. We aim to shorten times to market by providing feasibility feedback to engineers early on in the design phase. Within the scope of this paper, we focus on intralogistics and assess feasibility based on the resources' layout and their properties. In addition to the feasibility feedback, intralogistics checks also result in potential production sequences. This additional information can be used to increase flexibility on the shop floor in case of unexpected changes during operation, e.g., a machine failing. Additionally, we provide a comparison of taxonomies of logistics resources, which we believe to be useful as a reference for the production domain.

Companies producing Robot-Like Systems (RLS) must increase efficiency in design and production in order to stay competitive in the international market. Such RLS range from small SCARA robots to entire production facilities. Very different systems for similar tasks are published in research or offered in the market. A clear path to an optimal configuration for a given task is apparently not available to the engineer. This indicates that the gear drives of such RLS and their integration with automation technology are still insufficiently researched, and their efficient design poses significant challenges for the industry. This paper identifies and provides a concise overview of requirements and challenges in the context of drives for such RLS. Building on this overview, suggestions are made for the future course of action in research.

Engineers from the production systems domain have to cope with various challenges. These include interdisciplinarity, an ever increasing complexity, and time pressure. Due to these factors, engineers oftentimes choose suboptimal solutions, thus causing so-called Technical Debt (TD). No matter whether this TD is taken consciously or unconsciously, engineers need to become aware of their decisions' potential repercussions as early as possible. This paper presents a conceptual framework that helps engineers to identify conformance violations and inconsistencies based on formal knowledge bases. This increases awareness for potential TD. We hereby emphasize the importance of interdisciplinary inconsistencies, which are especially hard to keep track of. The framework also aims to assess the criticality of TD and thus supports decision making in the engineering of production systems.