Factory planning can increase the productivity of manufacturing significantly, though the process is expensive when it comes to cost and time. In this paper, we propose an Unmanned Aerial Vehicle (UAV) framework that accelerates this process and decreases the costs. The framework consists of a UAV that is equipped with an IMU, a camera and a LiDAR sensor in order to navigate and explore unknown indoor environments. Thus, it is independent of GNSS and solely uses on-board sensors. The acquired data should enable a DRL agent to perform autonomous decision making, applying a reinforcement learning approach. We propose a simulation of this framework including several training and testing environments, that should be used for developing a DRL agent.
drone, UAS, deep reinforcement learning
In Germany, demand for commercial drones is forecast to increase by 200% by 2025. As the use of drones increases, so does the danger they pose. This article describes a research project that aims to develop an acoustic operational monitoring system to improve the safety of critical components.
UAS, drones, operational monitoring
Unmanned aerial systems have changed the industry dramatically. The rapidly advancing technological development of so-called Unmanned Aircraft Systems (UAS) makes it necessary to address the design of future operational scenarios at an early stage.
UAS, Drones, Navigation
Unmanned Aircraft Systems (UAS) cover a wide range of applications not only outdoors but also indoors. The use of UAS has already been successfully tested and validated in some of these application areas, such as factory layout digitization or in-plant material transport. The current state of technical development of UAS for flight control, collision avoidance and flight performance basically allows their use in production operations. It is still unclear which legal and thus also insurance-related, technical and operational prerequisites must be created by applying companies for the use in production operations. This white paper discusses these issues. A technical and operational risk analysis is then presented, which is supplemented by a catalog of measures for the proper introduction and use of UAS technology. Potential risks are considered, even though the risks can be reduced by safety mechanisms and appropriate sensor technology as the degree of automation of UAS increases.
Drones, intralogistics, automated transport
Automated guided vehicles are a crucial component for more efficient production systems in intralogistics, but they have weaknesses in human-machine interaction. Scientists at IPH are developing a gesture-based control system to make the interaction intuitive and increase its acceptance.
Driverless transport vehicles, guidance control, gesture-based control
This article shows how the abilities known to humans to be flexible and adapt to changing environmental conditions, which are reflected in human cognitive characteristics, can be transferred to industrial trucks in intralogistics. As examples for the implementation of Industry 4.0 in intralogistics, technologies are presented that enable industrial trucks to recognize their environment, communicate information, draw conclusions, act autonomously, make decisions, learn or plan. These capabilities will be realized by an optical positioning system for position determination, camera-based storage/retrieval support and sensor technology integrated into tires, as well as novel forms of interaction for industrial trucks in the form of speech and gestures.
automated guided vehicle, augmented reality, smart glasses
Driverless transport systems are a building block for more efficient production systems in intralogistics, but have weaknesses in human-machine interaction. In a complex research project, a voice-based assignment is being developed, among other things, which is intended to make human-machine interaction more intuitive and increase its acceptance.
automated guided vehicle, augmented reality, smart glasses, voice control
Driverless transport systems (AGV-Systems) are an established and effective instrument for increasing the profitability of modern production plants and making intralogistical processes more efficient. In addition to a master control system and a communication system, driverless transport vehicles (AGVs) are among the main components of an AGV-System. In relation to manually controlled industrial trucks, automated AGVs are characterised by higher efficiency. The disadvantage of AGV-Systems is that they are not able to solve critical operating situations independently. In this case, extensive intervention by specialist personnel is required.
With the aim of overcoming these obstacles, the project "Mobile Human-Machine Interaction for commissioning and control of AGV-Systems (MobiMMI)" was developed. In this project, the human-machine interaction between an operator and an AGV is to be extended by the use of a speech and gesture-based system in order to make the intervention by the operator easier and more intuitive and thus significantly reduce the acquisition and operating costs of AGV-Systems.
Against the background of safety, ergonomics, user-friendliness and integrability, a mobile system will be developed for this purpose and equipped with various sensors for 3D detection of the environment, indoor positioning and multimodal communication. The recorded data is evaluated by means of computer vision and machine learning, enabling the operator to react quickly and easily to critical operating situations.
automated guided vehicle, human-machine-interface
