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

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

A Grid-based Sensor Floor Platform for Robot Localization using Machine Learning

Wireless Sensor Network (WSN) applications reshape the trend of warehouse monitoring systems allowing them to track and locate massive numbers of logistic entities in real-time. To support the tasks, classic Radio Frequency (RF)-based localization approaches (e.g. triangulation and trilateration) confront challenges due to multi-path fading and signal loss in noisy warehouse environment. In this paper, we investigate machine learning methods using a new grid-based WSN platform called Sensor Floor that can overcome the issues. Sensor Floor consists of 345 nodes installed across the floor of our logistic research hall with dual-band RF and Inertial Measurement Unit (IMU) sensors. Our goal is to localize all logistic entities, for this study we use a mobile robot. We record distributed sensing measurements of Received Signal Strength Indicator (RSSI) and IMU values as the dataset and position tracking from Vicon system as the ground truth. The asynchronous collected data is pre-processed and trained using Random Forest and Convolutional Neural Network (CNN). The CNN model with regularization outperforms the Random Forest in terms of localization accuracy with aproximate 15 cm. Moreover, the CNN architecture can be configured flexibly depending on the scenario in the warehouse. The hardware, software and the CNN architecture of the Sensor Floor are open-source under https://github.com/FLW-TUDO/sensorfloor.Comment: This is a preprint version for IEEE I2MTC 202

Towards Finding Optimal Solutions For Constrained Warehouse Layouts Using Answer Set Programming

A minimum requirement of feasible order picking layouts is the accessibility of every storage location. Obeying only this requirement typically leads to a vast amount of different layouts that are theoretically possible. Being able to generate all of these layouts automatically opens the door for new layouts and is valuable training data for reinforcement learning, e.g., for operating strategies of automated guided vehicles. We propose an approach using answer set programming that is able to generate and select optimal order picking layouts with regards to a defined objective function for given warehouse structures in a short amount of time. This constitutes a significant step towards reliable artificial intelligence. In a first step all feasible layout solutions are generated and in a second step an objective function is applied to get an optimal layout with regards to a defined layout problem. In brownfield projects this can lead to non-traditional layouts that are manually hard to find. The implementation can be customized for different use cases in the field of order picking layout generation, while the core logic stays the same