Analysis and Utilisation of Conflicts in Multi-Agent Path Finding

With the escalation in deployments of robotic fleets in unstructured environments, the need to address the increasing number of conflicts in Multi-Agent Path Finding also rises. We discuss the evident issue of conflicts and analyse their spatial relationships. A method to use previous missions and their resulting conflicts to extract highways is proposed. The highways facilitate a modified heuristic for Conflict Based Search allowing for fewer initial conflicts and thus decreasing the computational complexity of the search. The importance of the analysis of conflict patterns is displayed with real life experiments of a simulated assembly line workplace

Adapted Conflict Detection for Conflict Based Search

Mobile robots are increasingly deployed in various applications, including autonomous vehicles and logistics. Conflict-Based Search (CBS) is a promising approach for Multi-Agent Path Finding (MAPF), but has limitations when applied to real-world scenarios. This paper explores the challenges of adapting CBS to real-world mobile robotics, focusing on additional conflicts caused by imperfect navigation. We propose an Adaptive Conflict Detection (ACD) approach that proactively identifies conflicts within a rolling time window, making CBS more suitable for real-world applications. Both virtual and real robots are used to evaluate the importance of an adaptation to CBS if adapted to real scenarios.Experimental results show that ACD outperforms traditional CBS when penalties for conflict resolution are applied, demonstrating its potential for improved performance and reliability in practical multi-agent path planning applications

Concurrent intramodal learning enhances multisensory responses of symmetric crossmodal learning in robotic audio-visual tracking

Tracking an audio-visual target involves integrating spatial cues about target position from both modalities. Such sensory cue integration is a developmental process in the brain involving learning, with neuroplasticity as its underlying mechanism. We present a Hebbian learning-based adaptive neural circuit for multi-modal cue integration. The circuit temporally correlates stimulus cues within each modality via intramodal learning as well as symmetrically across modalities via crossmodal learning to independently update modality-specific neural weights on a sample-by-sample basis. It is realised as a robotic agent that must orient towards a moving audio-visual target. It continuously learns the best possible weights required for a weighted combination of auditory and visual spatial target directional cues that is directly mapped to robot wheel velocities to elicit an orientation response. Visual directional cues are noise-free and continuous but arising from a relatively narrow receptive field while auditory directional cues are noisy and intermittent but arising from a relatively wider receptive field. Comparative trials in simulation demonstrate that concurrent intramodal learning improves both the overall accuracy and precision of the orientation responses of symmetric crossmodal learning. We also demonstrate that symmetric crossmodal learning improves multisensory responses as compared to asymmetric crossmodal learning. The neural circuit also exhibits multisensory effects such as sub-additivity, additivity and super-additivity

Increasing trust in human–robot medical interactions:Effects of transparency and adaptability

In this paper, we examine trust in a human-robot medical interaction. We focus on the influence of transparency and robot adaptability on people's trust in a human-robot blood pressure measuring scenario. Our results show that increased transparency, i.e. robot explanations of its own actions designed to make the process and robot behaviors and capabilities accessible to the user, has a consistent effect on people's trust and perceived comfort. In contrast, robot adaptability, i.e., the opportunity to adjust the robot's position according to users' needs, influences users' evaluations of the robot as trustworthy only marginally. Our qualitative analyses indicate that this is due to the fact that transparency and adaptability are complex factors; the investigation of the interactional dynamics shows that users have very specific needs, and that adaptability may have to be paired with responsivity in order to make people feel in control.</p

Robots for Elderly Care Institutions: How They May Affect Elderly Care

"Welfare robots” are supposed to help maintain the quality of elderly care in institutions, while a dramatic demographic shift will lead to a significant problem attracting a sufficient number of caregivers. We give a status on the state of the art of welfare robots with a focus on the technical challenges that will constrain the development of robots in the next two decades. From this it follows, that robots will be recognizable as machines in the near future. To stay in concrete grounds, we will describe three use cases that are currently addressed in a project in which we design robots that will be applied in elderly care centers. These serve as examples of the kind of welfare robots that could realistically built in the near future. In the last section, we discuss the role such robots could take and how they could change elderly care in the near future

WITH GREETINGS FROM MOSCOW: THE ADOPTION OF RUSSIAN PROPAGANDA NARRATIVES IN THE PUBLIC COMMUNICATION OF GERMANY’S BÜNDNIS SAHRA WAGENKNECHT

This study investigates what the German newcomer party Bündnis Sahra Wagenknecht (BSW) publicly communicates concerning the war in Ukraine and to what extent this communication aligns with Russian propaganda. The analysis is guided by propaganda theory, emphasizing consistent, persuasive narratives aimed at shaping public attitudes. By applying the method of thematic content analysis, the study compares BSW leader Sahra Wagenknecht’s public communication with six Russian propaganda narratives extrapolated from existing literature. The analysis covers talk show appearances, debates, and speeches between the party’s founding in January 2024 and its first federal election in February 2025. The findings reveal that especially policy positions concerning peace negotiations and independence from the United States (US) closely align with Russian narratives. Additionally, the BSW utilizes four Russian propaganda narratives more directly in other communicative contexts than justifying its policy positions. The thesis concludes that the BSW fully aligns with Russian propaganda narratives, raising concerns about the role of Western democratic parties in the domestic amplification of foreign authoritarian propaganda.Master of Art

Enabling robots to adhere to social norms by detecting F-formations

Robot navigation in environments shared with humans should take into account social structures and interactions. The identification of social groups has been a challenge for robotics as it encompasses a number of disciplines. We propose a hierarchical clustering method for grouping individuals into free standing conversational groups (FSCS), utilising their position and orientation. The proposed method is evaluated on the SALSA dataset with achieved F1 score of 0.94. The algorithm is also evaluated for scalability and implemented on a mobile robot attempting to detect social groups and engage in interaction.</p

Health-CAT:Development of a Mobile Robot for Assisting Caregivers

The demographic change is expected to challenge the healthcare sector which in many countries is already struggling today leading to, e.g., a shortage of staff. Since robot technology is playing a minor role in health- care today, robotics is considered to be one mean to mitigate some of the challenges related to the demographic changes. This paper discusses hurdles for introducing robotics solutions in healthcare and describes the identification of a use case as well as the development of a robot prototype. End users have been involved throughout an iterative development process leading to a prototype that has been tested during normal operations

HRI-Gestures:Gesture Recognition for Human-Robot Interaction

Most of people’s communication happens through body language and gestures. Gesture recognition in human-robot interaction is an unsolved problem which limits the possible communication between humans and robots in today’s applications. Gesture recognition can be considered as the same problem as action recognition which is largely solved by deep learning, however, current publicly available datasets do not contain many classes relevant to human-robot interaction. In order to address the problem, a human-robot interaction gesture dataset is therefore required. In this paper, we introduce HRI-Gestures, which includes 13600 instances of RGB and depth image sequences, and joint position files. A state of the art action recognition network is trained on relevant subsets of the dataset and achieve upwards of 96.9% accuracy. However, as the network is designed for the large-scale NTU RGB+D dataset, subpar performance is achieved on the full HRI-Gestures dataset. Further enhancement of gesture recognition is possible by tailored algorithms or extension of the dataset