On Logistics and Motion Planning - An Informal Axiomatic Approach

Through the introduction of an informal axiomatic framework, this paper aims to contribute to the development of a general theory of logistics, which is currently still a blindspot in logistics research. It aims to combine the precision of robotic motion planning concepts with established logistics terminology, forging a link that balances the robustness of a mathematically rigorous theory with the rich semantic understanding inherent in logistics models. Centered around the notion of designing a logistic space, a possible way of structuring this space by grid-based and continuous spatial structures is discussed. The axiomatic framework is extended to include a new definition of logistics, queues, and other related concepts, providing a comprehensive view of logistics systems. Continuous spatial structures are semantically assigned to an idealized transport system, while the grid-based structure is recognized as an idealized storage system

DoUnseen: Tuning-Free Class-Adaptive Object Detection of Unseen Objects for Robotic Grasping

How can we segment varying numbers of objects where each specific object represents its own separate class? To make the problem even more realistic, how can we add and delete classes on the fly without retraining or fine-tuning? This is the case of robotic applications where no datasets of the objects exist or application that includes thousands of objects (E.g., in logistics) where it is impossible to train a single model to learn all of the objects. Most current research on object segmentation for robotic grasping focuses on class-level object segmentation (E.g., box, cup, bottle), closed sets (specific objects of a dataset; for example, YCB dataset), or deep learning-based template matching. In this work, we are interested in open sets where the number of classes is unknown, varying, and without pre-knowledge about the objects' types. We consider each specific object as its own separate class. Our goal is to develop an object detector that requires no fine-tuning and can add any object as a class just by capturing a few images of the object. Our main idea is to break the segmentation pipelines into two steps by combining unseen object segmentation networks cascaded by class-adaptive classifiers. We evaluate our class-adaptive object detector on unseen datasets and compare it to a trained Mask R-CNN on those datasets. The results show that the performance varies from practical to unsuitable depending on the environment setup and the objects being handled. The code is available in our DoUnseen library repository.Comment: presented at RSS 2023 Workshop on Perception and Manipulation Challenges for Warehouse Automatio

Zur innerbetrieblichen Logistik - Axiomatik und Betrachtung als kinodynamisches System

Der Anspruch zeitgemäßer Logistikforschung ist die Modellierung ihrer Herausforderungen und Probleme aus logistischer Sicht. Dabei ist die Betrachtung von Einzelproblemstellungen aus Perspektive der beteiligten Disziplinen und Fachbereiche zu überwinden, um zu einer übergeordneten Theorie zu finden, die anwendungsunabhängige Gültigkeit besitzt. Ein erster Schritt zu diesem Ziel wird in dieser Promotionsschrift durch die erstmalige Definition einer Axiomatik der Logistik versucht. Zentrale Zielstellung dieser Arbeit ist die Entwicklung eines allgemein- gültigen Verfahrens kinodynamischer Bewegungsplanung im idealen logistischen Raum auf Basis der Axiomatik. Die zu schließende Forschungslücke ergibt sich aus der Erkenntnis, dass für das durch den idealen logistischen Raum repräsentierte idealisierte Förderwesen bislang kein solches Verfahren existiert. Ein Herausstellungsmerkmal des neuen Verfahrens ist die Verwendung eines kontinuierlichen Weltmodells, das sich von bereits etablierten gitterbasierten Ansätzen durch die Berücksichtigung kinodynamischer Zwangsbedingungen abgrenzt. Das Schließen der Forschungslücke ermöglicht eine prinzipiell vollständige Beschreibung logistischer Bewegungsplanung. Das entwickelte Verfahren wird anhand eines neuartigen Transportrobotersystems für hochdynamische Sortieranwendungen validiert. In diesem Zuge wird das Konzept des Cyberphysischen Zwillings definiert. Damit einher geht die Konzeptionierung eines Versuchsfelds für die Entwicklung Cyberphysischer Zwillinge sowie der experimentelle Nachweis, dass ein schwarmbasiertes Sortiersystem mit diesem Verfahren leistungsfähiger ist als ein aktuelles Hochleistungssortiersystem mit Stetigfördertechnik.The aspiration of contemporary logistics research is its capability to model its challenges and problems from a logistics perspective. In doing so, the consideration of individual problems from the perspective of the disciplines and fields involved must be overcome in order to find a superordinate theory that holds application-independent validity. A first step towards that goal is attempted in this dissertation by defining an axiomatic of logistics for the first time. The central objective of this thesis is the development of a universally applicable method of kinodynamic motion planning in the ideal logistic space based on the axiomatics. The research gap to be filled arises from the finding that for an idealized transport system, which is represented by the ideal logistic space, no such method exists so far. A specific feature of the new method is the use of a continuous world model, which differs from already established grid-based approaches by considering kinodynamic constraints. In principle, closing this research gap enables a complete description of logistic motion planning. The developed method is validated using a novel transport robot system for dynamic sorting applications with high throughput. In doing so, the concept of a Cyberphysical Twin is defined. This is accompanied by the design of an experimentation environment for the development of Cyber- physical Twins as well as the experimental evidence that a swarm-based sorting system using this method is more performant than a state-of-the-art high-performance sorting system with continuous conveyor technology

UAVs for Industries and Supply Chain Management

This work aims at showing that it is feasible and safe to use a swarm of Unmanned Aerial Vehicles (UAVs) indoors alongside humans. UAVs are increasingly being integrated under the Industry 4.0 framework. UAV swarms are primarily deployed outdoors in civil and military applications, but the opportunities for using them in manufacturing and supply chain management are immense. There is extensive research on UAV technology, e.g., localization, control, and computer vision, but less research on the practical application of UAVs in industry. UAV technology could improve data collection and monitoring, enhance decision-making in an Internet of Things framework and automate time-consuming and redundant tasks in the industry. However, there is a gap between the technological developments of UAVs and their integration into the supply chain. Therefore, this work focuses on automating the task of transporting packages utilizing a swarm of small UAVs operating alongside humans. MoCap system, ROS, and unity are used for localization, inter-process communication and visualization. Multiple experiments are performed with the UAVs in wander and swarm mode in a warehouse like environment.Comment: Accpeted at the XXIV INTERNATIONAL CONFERENCE ON "MATERIAL HANDLING, CONSTRUCTIONS AND LOGISTICS

Object Pose Estimation Annotation Pipeline for Multi-view Monocular Camera Systems in Industrial Settings

Object localization, and more specifically object pose estimation, in large industrial spaces such as warehouses and production facilities, is essential for material flow operations. Traditional approaches rely on artificial artifacts installed in the environment or excessively expensive equipment, that is not suitable at scale. A more practical approach is to utilize existing cameras in such spaces in order to address the underlying pose estimation problem and to localize objects of interest. In order to leverage state-of-the-art methods in deep learning for object pose estimation, large amounts of data need to be collected and annotated. In this work, we provide an approach to the annotation of large datasets of monocular images without the need for manual labor. Our approach localizes cameras in space, unifies their location with a motion capture system, and uses a set of linear mappings to project 3D models of objects of interest at their ground truth 6D pose locations. We test our pipeline on a custom dataset collected from a system of eight cameras in an industrial setting that mimics the intended area of operation. Our approach was able to provide consistent quality annotations for our dataset with 26, 482 object instances at a fraction of the time required by human annotators