To effectively utilize the capacities of the production facilities, flexible and fast transport, and manipulation technology is essential. Current path planning methods use simple paths without exploiting the physical limitations of mobile robots. This paper deals with the time-efficient kinematic planning of path and velocity trajectories for autonomous mobile robots equipped with a differential-drive, used to load and unload machine tools. For a close parking position next to the machine tools, the mobile robot must approach practically straight without the necessity of turning at the end. After a brief definition of the kinematics of a mobile robot with nonholonomic constraints, a Bezier-curve is combined with multiple jerk-limited velocity trajectories considering the initial and final state as well as the physical limitations. For a time-efficient acceleration and deceleration the maximum acceleration of the mobile robot is used and differentiated into the radial and tangential acceleration. This allows a quickly reached close parking pose next to a machine tool. The algorithm is tested with simulations in RVIZ in combination with Gazebo and ROS2 as well as with the real mobile robot Tend-o-Bot.

Navigating mobile robots from remote servers offers numerous benefits in industrial settings, such as centralized decision-making and powerful hardware availability. However, the QoS of outsourced controllers is heavily influenced by the communication infrastructure state. Allowing robot applications to gain insights and control these states has the potential to significantly enhance mission performance. Programmable 5G O-RAN standardized near real-time network controllers allow application domain entities to access network-specific metrics and control parameters. We propose a solution where multiple robots are connected via 5G O-RAN to a common robot application framework capable of forwarding network performance metrics and managing network resource usage. This new component allows mobile robot applications to optimize their behavior based on network conditions. The system is evaluated in a simulated environment, where necessary navigation computations can be performed either on the mobile robot's local hardware or on more powerful remote hardware. Results demonstrate enhanced mission performance in the case of network-aware navigation and further improvements when the agent's QoS is prioritized.

Automating machine tending for high mix - low volume environment provides additional engineering challenges. Current machine tending systems are usually fixed in place and preprogrammed by a human operator. Available mobile robots are limited in their machine tending capabilities as they cannot provide the required accuracy that is necessary for the precise insertion of work pieces into a fixture. This paper presents a mobile machine tending system that uses computer vision and ROS2 to address these limitations and to enable autonomous machine tending. After a brief introduction to the system architecture, the article provides first insights into the systems capabilities based on experimental evaluation. The first experiment addresses the accuracy of the end effector, showing reasonable improvements using low cost sensors. The second experiment evaluates the network load of the distributed control system and discusses the requirements for the operation of a fleet of such robots.

Deploying navigation algorithms on an edge or cloud server according to the Software-as-a-Service paradigm has many advantages for autonomous mobile robots in indus-trial environments, e.g. cooperative planning and less onboard energy consumption. However, outsourcing corresponding real-time critical control functions requires a high level of reliability, which cannot be guaranteed either by modern wireless networks nor by the outsourced computing infrastructure. This work introduces redundancy concepts, which enable real-time capability within these uncertain infrastructures by providing redundant computation nodes, as well as robot-controlled switching between them. Redundancies can vary regarding their physical location, robot behavior during the switchover process and degree of activeness while quality of service concerning the primary controller is sufficient. In the case that fallback redun-dancies are not continuously active, when a disturbance occurs an initial state estimation of the robot pose has to be provided and an activation time has to be anticipated. To gain some insights on expected behavior, redundant computation nodes are deployed locally on the robot and on an outsourced computation node and consequently evaluated empirically. Quantitative and qualitative results in simulation and a real environment show that redun-dancies help to significantly improve the robot-trajectory within an unreliable network. Moreover, resource-saving redundancies, which are not continuously active, can robustly take over control by using an estimated state.

The benefits of the currently evolving IEEE Time-Sensitive Networking (TSN) standard have already been globally recognized. Whereas the application of TSN in a LAN is currently widely and globally tested, TSN in a Metropolitan Area Network (MAN) has not been a major focus until now. The possible benefits of utilizing co-located Edge Clouds in order to support multiple urban production sites with industrial realtime applications open a wide range of new business models. Therefore, we have analyzed the feasibility of transparently using PROFINET over TSN via a Dense Wavelength Division Multiplex (DWDM) link, where a machine park is controlled remotely by an Edge-based virtual Programmable Logic Controller (vPLC). As a result, we are able to setup a TSN connection over a MAN with a one-way delay of about 156.5 J.μs and a jitter of about 12 ns. This work can be extended to allow for dynamically provisioned TSN flows and multi-path Frame Replication and Elimination (FRER) for distributed hard real-time machine control and adoption to Ultra-Reliable Low-Latency Communication (URLLC) 5G campus networks.

Nowadays production industry and smart factory is dealing with methods of optimal resource load balancing and new types of flexible service-oriented strategies. It is seen crucial to adapt quickly to changes in manufacturing processes and new products or even integrate new hardware faster than the competition. Flexibility and Scalability can be improved by exchanging only a certain part of hardware and software without the need of touching all the other components. In this paper we present a methodical approach towards a typical use case in modern industrial robotic systems. The system consists of hardware components from different manufacturers which can be controlled and monitored separately by remote services. Those services can be combined to complex applications and integrate value added services. We show the independence and capability of exchangeable added value services running either centralized, decentralized, locally or remote. The experiments demonstrate how a process is improve by simply adding another service according to the Plug-and-Play paradigm. The service ensures the conditions of a computer vision system component to keep the reliability of the overall system workflow. In addition it will be demonstrated how system components could be virtualized in container-based cloud environments to save required on-board resources of the robotic system while keeping the whole system communication secure. Finally, results will be presented for different intercommunication scenarios.

Mobile autonomous transport systems are a crucial part of flexible factory logistics enabling an adaptable industrial production. Whereas connectivity of these mobile robots is still mostly covered through Wifi, applicators suffer from interferences and unreliableness due to the harsh environmental conditions. We present a case study using 4G campus networks applying network slicing technology and edge computing in order to realize a distributed control scenario. Control algorithms are offloaded to the edge following a navigation as a service approach. Basic safety functions remain onboard while self-localization and mapping as well as motion planning run on either a factory edge, a nearby edge or on a public cloud. We present an evaluation of the overall architecture and focus on effects of offloading control functions towards the edge. Finally, we provide an outlook towards the usage of 5G.

The controller design for industrial robot joint positioning is based on fixed cycle times and accurate modeling. Applying closed loop systems to decentralized control networks with variable connection properties would lead to unacceptable performance drops of the controlled mechanism. On the other hand service-oriented and networked control modules are providing full flexibility of location independent execution. But using wide-area connection like internet or wireless networks is yielding unpredictable latency and jitter conditions. That is not well handled by standard feedback controllers like PID or P-PI cascades. This paper presents the comparison of a modified MPC to a standard P-PI controller. That comparison reveals the strengths and weaknesses of each approach regarding controller performance under different communication conditions. Reusing the MPC control trajectory allows the robot to follow a small open-loop trajectory during latency deviation. That improves the performance criteria like overshoot and damping significantly. The overall robustness of the modified MPC is demonstrated empirically by extensive experiments with an laboratory industrial robot.

The classical design of industrial robot controllers relies strongly on constant and stable cycle times for any closed loop operation. Meanwhile, decentralizing robot control into independent services and rolling them out to virtual machines and cloud instances is an upcoming trend. The advantage of the distributed robot control lies in achieving full flexibility of location independent service execution. In contrast, the internet connectivity leads to unknown latency conditions and resulting unpredictable cycle times. That is not well handled by standard feedback controllers like PID or P-PI cascades. In addition to the time-critical aspect, the performance of a closed -loop control relies on the accuracy of the underlying model. Modeling is often labor-intensive and time-consuming. This paper presents four different implementations of a non-model based modified Switching Active Disturbance Rejection Control (ADRC) in a robotic use case. The comparison of these four concepts reveals the strengths and weaknesses of the ADRC approach regarding controller performance under the assumption of full dynamics compensation with tough communication conditions. By extending the Switching-ADRC approach by a gain-scheduler to smooth the switching between the linear and non-linear ADRC description and a static adjustment of control-parameters related to the measured round-trip time, the presented control-concept is able to stabilize the high-dynamic, partly unstable robot-system without model-information. The stability of the modified Switching-ADRC is demonstrated empirically by extensive experiments with a distributed industrial robot joint position control.

Recent developments in connected industries and internet of things identified demands for flexible reconfiguration and reprogramming of robots and machine tools. The demand for reconfiguration comes from changes in production process ordering or individual products; the demand for reprogramming comes from changing workplace organization and material flow. Yet these reconfiguration and reprogramming is often characterized by constants for a specific use-case in terms of precomputed trajectories. In this paper, we present an approach of monitoring the robot's workspace and using an online replanning of motion. We present a toolchain that is available and ready to use for a big class of industrial robots that have a position setpoint control interface. The feasibility is demonstrated in small laboratory experiments with a modular industrial robot in a common human-robot interface scenario.

In the bin picking problem, the task is to automatically unload objects from a container using a robotic manipulator. The task is often approached by organizing the objects into a predictable pattern, e.g., a workpiece carrier, in order to simplify all integral subtasks like object recognition, motion planning and grasping. In such a case, motion planning can even be solved offline as it is ensured that the objects are always at the same positions at known times. However, there is a growing demand for non-structured bin picking, where the objects can be placed randomly in the bins. This arises from recent trends of transforming classical factories into smart production facilities allowing small lot sizes at the efficiency of mass production. The demand for fast and highly flexible handling and manipulation abilities of industrial robots requires to solve all the bin picking methods, including motion planning, online. In this paper, we propose a novel technique for fast sampling-based motion planning of robotic manipulators using motion primitives. Motion primitives are short trajectories that boost search of the configuration space and consequently speed up the planning phase. The proposed work has been verified in a simulation and on a prototype of a bin picking system.

The fourth industrial revolution has called forth a transformation of how factories and their internal and external components and processes should integrate with each other. The need for fully connected cyber-physical systems is imperative to this vision. In the context of human-robot interaction this creates new exciting possibilities, but also creates challenges at the same time. The real-time requirements of robot control systems, to allow for safe and effective manipulation, have to be given serious consideration. This work develops a concept for a network service framework, which creates a distributed communication graph between systems. Using pre-existing tightly nit components in robot control and human-robot interaction application architecture, a modularized approach is developed, where in components are split up into self-contained units, if possible. The framework was developed in C++ using ZeroMQ, for message passing, and Protocol Buffers for data serialization and deserialization, and, in particular, shows promising behaviour concerning real-time performance.

This paper describes our concept for control of robots and machine tools through a private factory cloud which is dynamically extended by additional public resources. After discussing the opportunities and drawbacks of the virtualization of robot controllers (RC) and software programmable logic controllers (soft-PLC), we present first experiences with the setup of private and public infrastructures. Finally, we introduce our experimental design for investigating the requirements of machine control algorithms regarding availability, dependability and realtime capability in local and public configurations.

We propose an approach for integrated object and path demonstration. The main idea is to choose the motion of the robot depending on the object it grabs. When programming, an object is fixed at the robot's gripper. Path programming is done using haptic guidance. Afterwards, if a robot is handed over an object it knows before, it reproduces the path assigned to the object. Path programming and object recognition can be done with a single force/torque sensor. In our opinion, this way of programming is especially suited for creating pick and place tasks in frequently changing production environments where (re-)programming has to be done frequently.

This paper gives an introduction to the process of an experimental project in pertaining to the planning and integrating of a human-robot interactive stone carving system. The process includes an analysis of manual stone carving by a professional sculptor, a risk assessment of the projected robotic assistant system resulting in the safety concepts, a design of a tool carrier with force sensors and the human-machine interface. A standard industrial robot was selected and enhanced with extensive periphery to build up the intelligent tool carrier. Besides scientific results regarding work space and contact force suitable for human-robot collaboration, this paper presents the sculptural aspects within the experiment project. The professional scluptor evaluated the results as beyond manual processing.