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University of Southern Denmark, Denmark

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61 Papers

Fostering Well-being in Care with the Nautical Designed Plant Watering Robot

Topics: Agent-based Human-Robot Interaction; Model-Based and Mental Design; Mobile, Wearable and Pervasive Devices; Context-Aware and Adaptive Systems; Multimodal Systems and Application; Integration Mechanisms and Fusion Strategies; Social Agents and Emotional Interaction

Authors: Philipp Graf ¹ , Kevin Lefeuvre ² , Oskar Palinko ³ , Lakshadeep Naik ³ , Christian Zarp ³ , Andreas Bischof ¹ , Eva Hornecker ² and Norbert Krüger ³

Affiliations: ¹ Technische Universität Chemnitz, Strasse der Nationen 62, 09111 Chemnitz, Germany , ² Bauhaus-Universität Weimar, Bauhausstr. 11, 99423 Weimar, Germany , ³ University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark

Digital Object Identifier: 10.5220/0010909000003124

Proceedings of HUCAPP 2022, ISBN: 978-989-758-555-5

Quick Abstract

The well-being of older people in care homes does not only rely on health and bodily needs but also includes spiritual or social needs. The presence of plants and distraction from everyday routines are two rarely addressed issues in this regard. Having those in mind, we developed the concept of the ’Plant Watering Robot’ (PWR), a robotic device that has a double purpose: to water plants and serve as an attraction to observers thereby creating amusement. It is designed as a little ship inhabited by a small ’captain’ that is displayed as being in charge of the device’s actions. The pilot interacts with various synchronized elements building up a narrative of being in charge of watering the plants. We first report on related work before describing the interaction concept in more detail. We then elaborate the technical implementation of the PWR focussing on mechanical and software aspects.

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

Topics: Design Methods and Tools; Models for Human-Agent Interaction; Human Cooperation and Agent-Based Interaction

Authors: William Juel , Frederik Haarslev and Leon Bodenhagen

Affiliation: SDU Robotics, University of Southern Denmark, Campusvej 55, Odense C, Denmark

Digital Object Identifier: 10.5220/0010840800003124

Proceedings of HUCAPP 2022, ISBN: 978-989-758-555-5

Quick Abstract

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

Topics: Categorization and Scene Understanding; Event and Human Activity Recognition; Vision for Robotics; Egocentric Vision for Interaction Understanding

Authors: Avgi Kollakidou ¹ , Frederik Haarslev ¹ , Cagatay Odabasi ² , Leon Bodenhagen ¹ and Norbert Krüger ¹

Affiliations: ¹ SDU Robotics, University of Southern Denmark, Campusvej 55, Odense C, Denmark , ² Fraunhofer IPA, Nobelstraße 12, Stuttgart, Germany

Digital Object Identifier: 10.5220/0010871200003124

Proceedings of VISAPP 2022, ISBN: 978-989-758-555-5

Quick Abstract

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.

Classification of Visual Interest based on Gaze and Facial Features for Human-robot Interaction

Topics: Models for Human-Agent Interaction; Agent-based Human-Robot Interaction; Social Agents and Emotional Interaction

Authors: Andreas Sørensen , Oskar Palinko and Norbert Krüger

Affiliation: The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark

Digital Object Identifier: 10.5220/0010259301980204

Proceedings of HUCAPP 2021, ISBN: 978-989-758-488-6

Quick Abstract

It is important for a social robot to know if a nearby human is showing interest in interacting with it. We approximate this interest with expressed visual interest. To find it, we train a number of classifiers with previously labeled data. The input features for these are facial features like head orientation, eye gaze and facial action units, which are provided by the OpenFace library. As training data, we use video footage collected during an in-the-wild human-robot interaction scenario, where a social robot was approaching people at a cafeteria to serve them water. The most successful classifier that we trained tested at a 94% accuracy for detecting interest on an unrelated testing dataset. This allows us to create an effective tool for our social robot, which enables it to start talking to people only when it is fairly certain that the addressed persons are interested in talking to it.

Context-aware Social Robot Navigation

Topics: Collective and Social Robots; Guidance, Navigation and Control; Engineering Applications on Robotics and Automation

Authors: Frederik Haarslev , William Juel , Avgi Kollakidou , Norbert Krüger and Leon Bodenhagen

Affiliation: SDU Robotics, University of Southern Denmark, Campusvej 55, Odense C, Denmark

Digital Object Identifier: 10.5220/0010554204260433

Proceedings of ICINCO 2021, ISBN: 978-989-758-522-7

Quick Abstract

With the emergence of robots being deployed in unstructured environments outside the industrial domain, the importance of robots behaving appropriately in the vicinity of people is becoming more clear. These behaviours are hard to model as they depend on the social context. This context includes among other things where the robot is deployed, how crowded that place is, as well as who are residing in that place. In this paper we extend social space theory with the social context, making them adaptable to the current situation. We implement the social spaces as costmaps used in the standard ROS navigation stack. Our method – Context-Aware Social robot Navigation (CASN) – is tested in the context of people avoidance in social navigation. We compare CASN with the social navigation layer package, which also implements costs based on detected people. We show that by using CASN a mobile robot complies with social conventions in four different navigation scenarios.

An Integrated Object Detection and Tracking Framework for Mobile Robots

Topics: Mobile Robots and Autonomous Systems; Perception and Awareness; Engineering Applications on Robotics and Automation

Authors: William Juel , Frederik Haarslev , Norbert Krüger and Leon Bodenhagen

Affiliation: SDU Robotics, University of Southern Denmark, Campusvej 55, Odense C, Denmark

Digital Object Identifier: 10.5220/0009888405130520

Proceedings of ICINCO 2020, ISBN: 978-989-758-442-8

Quick Abstract

In this paper, we propose an end-to-end-solution to the problem of multi-object tracking on a mobile robot. The tracking system consists of a process where we project 2D multi-object detections to the robots base frame, using RGB-D sensor data. These detections are then transformed to the map frame using a localization algorithm. This system predicts trajectories of humans and objects in the environment of the robot and can be adapted to work with any detector and track from multiple cameras. The system can then be used to build a temporally consistent costmap to improve navigation strategies.

Optimal Path Planning for Drone Inspections of Linear Infrastructures

Topics: Spatial Modeling and Reasoning; Storage, Indexing and Searching; Geographic Information Retrieval; Energy Information Systems

Authors: Golizheh Mehrooz and Peter Schneider-Kamp

Affiliation: Dept. of Mathematics & Computer Science, University of Southern Denmark, Denmark

Digital Object Identifier: 10.5220/0009846703260336

Proceedings of GISTAM 2020, ISBN: 978-989-758-425-1

Quick Abstract

Autonomous Beyond Visual Line of Sight (BVLOS) flights represent a huge opportunity in the drone industry due to their ability to monitor larger areas. Autonomous navigation and path planning are essential capabilities for BVLOS flights. In this paper, we introduce the routing component of a path planning system for inspecting linear infrastructures. We explore both a direct algorithm and a transformation algorithm. The direct algorithm is an extension of A* to allow limited routing through air as well as the use of non-logic intersections. The transformation algorithm pre-computes a graph that include edges for routing through air and nodes for non-logic intersections. We implemented both algorithms for routing along a particular type of linear infrastructure, power lines, and validated them through an empirical evaluation at three different scales: the Danish power grid, the French power grid, and the entire European power grid. The test results show that the transformation algorithm allows for sub-second routing performance for a small-to-medium sized power grid. Larger power grids can be routed in less than five seconds, and even an optimal route of more than six thousand kilometers along linear infrastructures from Portugal to Sweden via Russia is found in less than half a minute. All algorithms have been implemented and are available as an open-source Python package for Linear-infrastructure Mission Control (LiMiC).

Real-time On-board Detection of Components and Faults in an Autonomous UAV System for Power Line Inspection

Topics: Convolutional Neural Networks (CNN); Meta-Learning and Deep Networks; IoT and Smart Devices; Image Classification; Object Detection

Authors: Naeem Ayoub and Peter Schneider-Kamp

Affiliation: Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmmark

Digital Object Identifier: 10.5220/0009826700680075

Proceedings of DeLTA 2020, ISBN: 978-989-758-441-1

Quick Abstract

The inspection of power line components is periodically conducted by specialized companies to identify possible faults and assess the state of the critical infrastructure. UAV-systems represent an emerging technological alternative in this field, with the promise of safer, more efficient, and less costly inspections. In the Drones4Energy project, we work toward a vision-based beyond-visual-line-of-sight (BVLOS) power line inspection architecture for automatically and autonomously detecting components and faults in real-time on board of the UAV. In this paper, we present the first step towards the vision system of this architecture. We train Deep Neural Networks (DNNs) and tune them for reliability under different conditions such as variations in camera used, lighting, angles, and background. For the purpose of real-time on-board implementation of the architecture, experimental evaluations and comparisons are performed on different hardware such as Raspberry Pi 4, Nvidia Jetson Nano, Nvidia Jetson TX2, and Nvidia Jetson AGX Xavier. The use of such Single Board Devices (SBDs) is an integral part of the design of the proposed power line inspection architecture. Our experimental results demonstrate that the proposed approach can be effective and efficient for fully-automatic real-time on-board visual power line inspection.

Robotic Finger Design Workflow for Adaptable Industrial Assembly Tasks

Topics: Knowledge-Based Systems; Intelligent Robots

Authors: Adam Wolniakowski ^{1

;

2} , Anders Lindvig ² , Nicolai Iversen ³ , Norbert Krüger ² and Aljaž Kramberger ²

Affiliations: ¹ Faculty of Electrical Engineering, Bialystok University of Technology, Bialystok, Poland , ² The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark , ³ The Maersk Mc-Kinney Moller Institute, SDU UAS Center, University of Southern Denmark, Odense, Denmark

Digital Object Identifier: 10.5220/0010020100690076

Proceedings of ROBOVIS 2020, ISBN: 978-989-758-479-4

Quick Abstract

In this work, we introduce a web-based system connected to a simulation framework that can be used to facilitate the design of industrial fingers. We provide an overview of the state of the art and of the currently used manual gripper finger design methods prevailing in the industry. With a concrete use case we demonstrate the advantages in terms of quality and saved time for designing the fingers when utilizing our presented framework compared to a common manual method of designing the gripper fingers.

Wilson Score Kernel Density Estimation for Bernoulli Trials

Topics: Optimization Algorithms; Industrial Automation and Robotics; Engineering Applications on Robotics and Automation

Authors: Lars Sørensen , Simon Mathiesen , Dirk Kraft and Henrik Petersen

Affiliation: SDU Robotics, University of Southern Denmark, Campusvej, Odense, Denmark

Digital Object Identifier: 10.5220/0009816503050313

Proceedings of ICINCO 2020, ISBN: 978-989-758-442-8

Quick Abstract

We propose a new function estimator, called Wilson Score Kernel Density Estimation, that allows to estimate a mean probability and the surrounding confidence interval for parameterized processes with binomially distributed outcomes. Our estimator combines the advantages of kernel smoothing, from Kernel Density Estimation, and robustness to low number of samples, from Wilson Score. This allows for more robust and data efficient estimates compared to the individual use of these two estimators. While our estimator is generally applicable for processes with binomially distributed outcomes, we will present it in the context of iterative optimization. Here we first show the advantage of our estimator on a mathematically well defined problem, and then apply our estimator to an industrial automation process.

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