Technical Report / Hochschule Bonn-Rhein-Sieg University of Applied Sciences. Department of Computer Science
Publisher: Dean Prof. Dr. Sascha Alda
Hochschule Bonn-Rhein-Sieg University of Applied Sciences, Department of Computer Science
Sankt Augustin, Germany
ISSN 1869-5272
Hochschule Bonn-Rhein-Sieg University of Applied Sciences, Department of Computer Science
Sankt Augustin, Germany
ISSN 1869-5272
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- Robotik (6)
- Cutting sticks-Problem (3)
- Teilsummenaufteilung (3)
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- Active Learning (2)
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- Gravitation (2)
01-2020
An essential measure of autonomy in service robots designed to assist humans is adaptivity to the various contexts of human-oriented tasks. These robots may have to frequently execute the same action, but subject to subtle variations in task parameters that determine optimal behaviour. Such actions are traditionally executed by robots using pre-determined, generic motions, but a better approach could utilize robot arm maneuverability to learn and execute different trajectories that work best in each context.
In this project, we explore a robot skill acquisition procedure that allows incorporating contextual knowledge, adjusting executions according to context, and improvement through experience, as a step towards more adaptive service robots. We propose an apprenticeship learning approach to achieving context-aware action generalisation on the task of robot-to-human object hand-over. The procedure combines learning from demonstration, with which a robot learns to imitate a demonstrator’s execution of the task, and a reinforcement learning strategy, which enables subsequent experiential learning of contextualized policies, guided by information about context that is integrated into the learning process. By extending the initial, static hand-over policy to a contextually adaptive one, the robot derives and executes variants of the demonstrated action that most appropriately suit the current context. We use dynamic movement primitives (DMPs) as compact motion representations, and a model-based Contextual Relative Entropy Policy Search (C-REPS) algorithm for learning policies that can specify hand-over position, trajectory shape, and execution speed, conditioned on context variables. Policies are learned using simulated task executions, before transferring them to the robot and evaluating emergent behaviours.
We demonstrate the algorithm’s ability to learn context-dependent hand-over positions, and new trajectories, guided by suitable reward functions, and show that the current DMP implementation limits learning context-dependent execution speeds. We additionally conduct a user study involving participants assuming different postures and receiving an object from the robot, which executes hand-overs by either exclusively imitating a demonstrated motion, or selecting hand-over positions based on learned contextual policies and adapting its motion accordingly. The results confirm the hypothesized improvements in the robot’s perceived behaviour when it is context-aware and adaptive, and provide useful insights that can inform future developments.
04-2019
Die Wahrnehmung des perzeptionellen Aufrecht (perceptual upright, PU) variiert in Abhängigkeit der Gewichtung verschiedener gravitationsbezogener und körperbasierter Merkmale zwischen Kontexten und aufgrund individueller Unterschiede. Ziel des Vorhabens war es, systematisch zu untersuchen, welche Zusammenhänge zwischen visuellen und gravitationsbedingten Merkmalen bestehen. Das Vorhaben baute auf vorangegangen Untersuchungen auf, deren Ergebnisse indizieren, dass eine Gravitation von ca. 0,15g notwendig ist, um effiziente Selbstorientierungsinformationen bereit zu stellen (Herpers et. al, 2015; Harris et. al, 2014).
In dem hier beschriebenen Vorhaben wurden nun gezielt künstliche Gravitationsbedingungen berücksichtigt, um die Gravitationsschwelle, ab der ein wahrnehmbarer Einfluss beobachtbar ist, genauer zu quantifizieren bzw. die oben genannte Hypothese zu bestätigen. Es konnte gezeigt werden, dass die zentripetale Kraft, die auf einer rotierenden Zentrifuge entlang der Längsachse des Körpers wirkt, genauso efektiv wie Stehen mit normaler Schwerkraft ist, um das Gefühl des perzeptionellen Aufrechts auszulösen. Die erzielten Daten deuten zudem darauf hin, dass ein Gravitationsfeld von mindestens 0,15 g notwendig ist, um eine efektive Orientierungsinformation für die Wahrnehmung von Aufrecht zu liefern. Dies entspricht in etwa der Gravitationskraft von 0,17 g, die auf dem Mond besteht. Für eine lineare Beschleunigung des Körpers liegt der vestibulare Schwellenwert bei etwa 0,1 m/s2 und somit liegt der Wert für die Situation auf dem Mond von 1,6 m/s2 deutlich über diesem Schwellenwert.
03-2019
Currently, a variety of methods exist for creating different types of spatio-temporal world models. Despite the numerous methods for this type of modeling, there exists no methodology for comparing the different approaches or their suitability for a given application e.g. logistics robots. In order to establish a means for comparing and selecting the best-fitting spatio-temporal world modeling technique, a methodology and standard set of criteria must be established. To that end, state-of-the-art methods for this type of modeling will be collected, listed, and described. Existing methods used for evaluation will also be collected where possible.
Using the collected methods, new criteria and techniques will be devised to enable the comparison of various methods in a qualitative manner. Experiments will be proposed to further narrow and ultimately select a spatio-temporal model for a given purpose. An example network of autonomous logistic robots, ROPOD, will serve as a case study used to demonstrate the use of the new criteria. This will also serve to guide the design of future experiments that aim to select a spatio-temporal world modeling technique for a given task. ROPOD was specifically selected as it operates in a real-world, human shared environment. This type of environment is desirable for experiments as it provides a unique combination of common and novel problems that arise when selecting an appropriate spatio-temporal world model. Using the developed criteria, a qualitative analysis will be applied to the selected methods to remove unfit options.
Then, experiments will be run on the remaining methods to provide comparative benchmarks. Finally, the results will be analyzed and recommendations to ROPOD will be made.
02-2019
Multi-robot systems (MRS) are capable of performing a set of tasks by dividing them among the robots in the fleet. One of the challenges of working with multirobot systems is deciding which robot should execute each task. Multi-robot task allocation (MRTA) algorithms address this problem by explicitly assigning tasks to robots with the goal of maximizing the overall performance of the system. The indoor transportation of goods is a practical application of multi-robot systems in the area of logistics. The ROPOD project works on developing multi-robot system solutions for logistics in hospital facilities. The correct selection of an MRTA algorithm is crucial for enhancing transportation tasks. Several multi-robot task allocation algorithms exist in the literature, but just few experimental comparative analysis have been performed. This project analyzes and assesses the performance of MRTA algorithms for allocating supply cart transportation tasks to a fleet of robots. We conducted a qualitative analysis of MRTA algorithms, selected the most suitable ones based on the ROPOD requirements, implemented four of them (MURDOCH, SSI, TeSSI, and TeSSIduo), and evaluated the quality of their allocations using a common experimental setup and 10 experiments. Our experiments include off-line and semi on-line allocation of tasks as well as scalability tests and use virtual robots implemented as Docker containers. This design should facilitate deployment of the system on the physical robots. Our experiments conclude that TeSSI and TeSSIduo suit best the ROPOD requirements. Both use temporal constraints to build task schedules and run in polynomial time, which allow them to scale well with the number of tasks and robots. TeSSI distributes the tasks among more robots in the fleet, while TeSSIduo tends to use a lower percentage of the available robots.
Subsequently, we have integrated TeSSI and TeSSIduo to perform multi-robot task allocation for the ROPOD project.
01-2019
Interactive Object Detection
(2019)
The success of state-of-the-art object detection methods depend heavily on the availability of a large amount of annotated image data. The raw image data available from various sources are abundant but non-annotated. Annotating image data is often costly, time-consuming or needs expert help. In this work, a new paradigm of learning called Active Learning is explored which uses user interaction to obtain annotations for a subset of the dataset. The goal of active learning is to achieve superior object detection performance with images that are annotated on demand. To realize active learning method, the trade-off between the effort to annotate (annotation cost) unlabeled data and the performance of object detection model is minimised.
Random Forests based method called Hough Forest is chosen as the object detection model and the annotation cost is calculated as the predicted false positive and false negative rate. The framework is successfully evaluated on two Computer Vision benchmark and two Carl Zeiss custom datasets. Also, an evaluation of RGB, HoG and Deep features for the task is presented.
Experimental results show that using Deep features with Hough Forest achieves the maximum performance. By employing Active Learning, it is demonstrated that performance comparable to the fully supervised setting can be achieved by annotating just 2.5% of the images. To this end, an annotation tool is developed for user interaction during Active Learning.
03-2018
Friction effects impose a requirement for the supplementary amount of torque to be produced in actuators for a robot to move, which in turn increases energy consumption. We cannot eliminate friction, but we can optimize motions to make them more energy efficient, by considering friction effects in motion computations. Optimizing motions means computing efficient joint torques/accelerations based on different friction torques imposed in each joint. Existing friction forces can be used for supporting certain types of arm motions, e.g standing still.
Reducing energy consumption of robot's arms will provide many benefits, such as longer battery life of mobile robots, reducing heat in motor systems, etc.
The aim of this project is extending an already available constrained hybrid dynamic solver, by including static friction effects in the computations of energy optimal motions. When the algorithm is extended to account for static friction factors, a convex optimization (maximization) problem must be solved.
The author of this hybrid dynamic solver has briefly outlined the approach for including static friction forces in computations of motions, but without providing a detailed derivation of the approach and elaboration that will show its correctness. Additionally, the author has outlined the idea for improving the computational efficiency of the approach, but without providing its derivation.
In this project, the proposed approach for extending the originally formulated algorithm has been completely derived and evaluated in order to show its feasibility. The evaluation is conducted in simulation environment with one DOF robot arm, and it shows correct results from the computation of motions. Furthermore, this project presents the derivation of the outlined method for improving the computational efficiency of the extended solver.
02-2018
Bei der Übertragung und Speicherung von Daten ist es eine wesentliche Frage, inwieweit die Daten komprimiert werden können, ohne dass deren Informationsgehalt verloren geht.
Ein Maß für den Informationsgehalt von Daten ist also von grundlegender Bedeutung. Vor etwa siebzig Jahren hat C. E. Shannon ein solches Maß eingeführt und damit das Lehr- und Forschungsgebiet der Informationstheorie begründet, welches seit dem bis heute hin wesentlich zur Konzeption und Realisierung von Informationsund Kommunikationstechnologien beigetragen hat. Etwa zwanzig Jahre später hat A. N. Kolmogorov ein anderes Maß für den Informationsgehalt von Daten eingeführt. Während die Shannonsche Informationstheorie zum Curriculum von mathematischen, informatischen und elektrotechnischen Studiengängen gehört, ist die Algorithmische Informationstheorie von Kolmogorov weit weniger bekannt und eher Gegenstand von speziellen Lehrveranstaltungen.
Seit einigen Jahren nimmt allerdings die Beschäftigung mit dieser Theorie zu, zumal in der einschlägigen Literatur von erfolgreichen praktischen Anwendungen der Theorie berichtet wird. Die vorliegende Arbeit gibt eine Einführung in grundlegende Ideen dieser Theorie und beschreibt deren Anwendungsmöglichkeiten bei einigen ausgewählten Problemstellungen der Theoretischen Informatik.
Die Ausarbeitung kann als Skript für einführende Lehrveranstaltungen in die Algorithmische Informationstheorie sowie als Lektüre zur Einarbeitung in die Thematik als Ausgangspunkt für Forschungs- und Entwicklungsarbeiten verwendet werden.
01-2018
Motion capture, often abbreviated mocap, generally aims at recording any kind of motion -- be it from a person or an object -- and to transform it to a computer-readable format. Especially the data recorded from (professional and non-professional) human actors are typically used for analysis in e.g. medicine, sport sciences, or biomechanics for evaluation of human motion across various factors. Motion capture is also widely used in the entertainment industry: In video games and films realistic motion sequences and animations are generated through data-driven motion synthesis based on recorded motion (capture) data.
Although the amount of publicly available full-body-motion capture data is growing, the research community still lacks a comparable corpus of specialty motion data such as, e.g. prehensile movements for everyday actions. On the one hand, such data can be used to enrich (hand-over animation) full-body motion capture data - usually captured without hand motion data due to the drastic dimensional difference in articulation detail. On the other hand, it provides means to classify and analyse prehensile movements with or without respect to the concrete object manipulated and to transfer the acquired knowledge to other fields of research (e.g. from 'pure' motion analysis to robotics or biomechanics).
Therefore, the objective of this motion capture database is to provide well-documented, free motion capture data for research purposes.
The presented database GraspDB14 in sum contains over 2000 prehensile movements of ten different non-professional actors interacting with 15 different objects. Each grasp was realised five times by each actor. The motions are systematically named containing an (anonymous) identifier for each actor as well as one for the object grasped or interacted with.
The data were recorded as joint angles (and raw 8-bit sensor data) which can be transformed into positional 3D data (3D trajectories of each joint).
In this document, we provide a detailed description on the GraspDB14-database as well as on its creation (for reproducibility).
Chapter 2 gives a brief overview of motion capture techniques, freely available motion capture databases for both, full body motions and hand motions, and a short section on how such data is made useful and re-used. Chapter 3 describes the database recording process and details the recording setup and the recorded scenarios. It includes a list of objects and performed types of interaction. Chapter 4 covers used file formats, contents, and naming patterns. We provide various tools for parsing, conversion, and visualisation of the recorded motion sequences and document their usage in chapter 5.
07-2017
Studienverläufe von Studenten weichen nicht selten vom offiziell geplanten Curriculum ab. Für eine den Studienerfolg verbessernde Planung und Weiterentwicklung von Studiengängen und Curricula fehlen den Verantwortlichen häufig Erkenntnisse über tatsächliche sowie typischerweise erfolgreiche und weniger erfolgreiche Studienverlaufsmuster. Process-Mining-Techniken können helfen, mehr Transparenz bei der Auswertung von Studienverläufen zu schaffen und so die Erkennung typischer Studienverlaufsmuster, die Überprüfung der Übereinstimmung der konkreten Studienverläufe mit dem vorgegebenen Curriculum sowie eine zielgerechte Verbesserung des Curriculums zu unterstützen.
06-2017
Das Cutting sticks-Problem ist in seiner allgemeinen Formulierung ein NP-vollständiges Problem mit Anwendungspotenzialen im Bereich der Logistik. Unter der Annahme, dass P ungleich NP (P != NP) ist, existieren keine effizienten, d.h. polynomiellen Algorithmen zur Lösung des allgemeinen Problems.
In diesem Papier werden für eine Reihe von Instanzen effiziente Lösungen angegeben.