Improving robot deployment is a central step towards speeding up robot-based automation in manufacturing. A main challenge in robot deployment is how to best place the robot within the workcell. To tackle this challenge, we combine two knowledge sources: robotic knowledge of the system and workcell context awareness of the user, and intersect them with an Augmented Reality interface. RobotGraffiti is a unique tool that empowers the user in robot deployment tasks. One simply takes a 3D scan of the workcell with their mobile device, adds contextual data points that otherwise would be difficult to infer from the system, and receives a robot base position that satisfies the automation task. The proposed approach is an alternative to expensive and time-consuming digital twins, with a fast and easy-to-use tool that focuses on selected workcell features needed to run the placement optimization algorithm. The main contributions of this paper are the novel user interface for robot base placement data collection and a study comparing the traditional offline simulation with our proposed method. We showcase the method with a robot base placement solution and obtain up to 16 times reduction in time.

Industrial robots have been widely used in diverse activities and industries for more than six decades. However, these robots were initially designed to operate autonomously without human interaction. The emergence of a new generation of manipulators, namely lightweight robots such as collaborative robots, has revolutionized the industry by enabling robots to work alongside humans. In this paper, we qualitatively compare the energy consumption of these two types of robots. First, we propose experimental setups to investigate how specific variables, such as standstill position, motion commands, velocity and acceleration limits, time scaling, and joint temperatures, influence the energy consumption of a Cobot, namely, UR3e. Then, UR3e results are compared to IR experimental results which are mainly based on existing literature. The comparison reveals that the energy signature graph, which depicts the energy consumption versus execution time, differs between these two robots. Furthermore, industrial robots consume a considerably larger amount of mechanical energy compared to their electronic components’ energy, while UR3e consumes a higher proportion of energy in their electronic components. Energy optimization strategies for UR3e should focus on efficient electronic design, such as the distribution of computation tasks among system assets, rather than reducing energy consumption through motion planning.

Compliance with global guidelines for sustainable and responsible production in modern industry requires a comparative analysis of consumer devices' energy consumption (EC). This also holds true for the newly established generation of lightweight industrial robots (LIRs). To identify potential strategies for energy optimization, standardized benchmarking procedures are required. However, to the best of the authors' knowledge, there is currently no standardized method for benchmarking the EC of manipulators. In response to this need, we have developed a comprehensive benchmarking framework to evaluate the EC of various LIR designs, delving into the theoretical power consumption under both static and dynamic conditions. Our analysis has led to the proposal of seven proposed metrics—three static and four dynamic. The static metrics—controller consumption, joint electronics consumption, and mechanical brakes' consumption—evaluate the maintenance EC of the robot. Meanwhile, we suggest three dynamic metrics that gauge the system's energy efficiency during motion, with or without payload. We extend this metrics selection by introducing the cost of transportation map for manipulators. For each of the metrics, we suggest a standardized measurement procedure based on state-of-the-art norms and literature. The metric set and experimental procedures are demonstrated using five manipulators (UR3e, UR5e, FR3, M0609, Gen3). Among the results, we can see interesting trends for future optimization of the electronic components and their architecture, e.g., reducing the robot's EC by decentralizing computation via low-consumption onboard controllers for basic tasks and external servers for complex ones.

The concept of a Digital Twin has proved its worth over the past two decades, establishing itself as a cornerstone of contemporary industry. Augmented Reality, an emerging technology, enhances the interaction between humans and machines, including computers and robots. Today, numerous examples exist of the union of these two technologies to create real-augmented digital-twin models of collaborative robots. However, these models often lack data on motor currents and power consumption. In this study, we propose a real-augmented digital-twin model that accurately estimates energy consumption. This additional energy information equips the tool for various applications such as robot optimization, commissioning, and troubleshooting. We employ our real-augmented digital-twin model to test methods for reducing Cobots’ energy consumption, using the tool to demonstrate and train Cobot practitioners on these techniques’ applications. The model is also useful for anomaly detection (troubleshooting) when the robot’s consumption statistically deviates from the ideal model. Moreover, the model can anticipate the robot’s power consumption during the commissioning phase, prior to its installation. Through a series of experiments and a practical demonstration at a robot fair for practitioners, we illustrate the benefits and training capabilities of our approach.

Simulation of robotic tasks allows for cheap evaluation and process optimization, which can then be transferred to the physical system. To avoid discrepancies between physical execution and simulation, real-world process data can be fed back to the simulation environment, a concept referred to as a "Digital Twin". This requires the development of a software architecture, that supports Digital Twins for robot tasks.In this work, we propose a system where an operator can take apart a complex assembly, thus creating digitized assembly instructions. These instructions are then used to visually program the robot setup by blocks, which contain functionality ranging from point-to-point motions to high-level skills. These "Skillblocks" allow for a seamless transition between execution in the simulation environment and on the physical robot through interchangeable execution layers in the software architecture. The system also allows for feedback from a physical execution to be monitored in real-time and fed back to the simulation environment for processing.The aim of the system is to close the gap between digital and physical workcells when integrating robot solutions. This increases intuitiveness and allows for process monitoring and optimization through direct feedback to the digital model.

We present a three-level cognitive system in a learning by demonstration context. The system allows for learning and transfer on the sensorimotor level as well as the planning level. The fundamentally different data structures associated with these two levels are connected by an efficient mid-level representation based on so-called “semantic event chains.” We describe details of the representations and quantify the effect of the associated learning procedures for each level under different amounts of noise. Moreover, we demonstrate the performance of the overall system by three demonstrations that have been performed at a project review. The described system has a technical readiness level (TRL) of 4, which in an ongoing follow-up project will be raised to TRL 6.

Although SMEs would benefit from robotic solutions in assembly, the required invests and efforts for their implementation are often too risky and costly for them. Here, the Horizon 2020 project “ReconCell” aims at developing a new type of highy-reconfigurable multi-robot assembly cell which adresses the particular needs of SMEs. At the Institute for Man- Machine Interaction (MMI), we are developing 3D simulation-based user interfaces for ReconCell as the central technology to enable the fast, easy and safe programming of the system. ReconCell heavily builds on previous developments that are transferred from research and prepared for industrial partners with real use cases and demands. Thus, in this contribution, we describe MMI's software platform that will be the basis of the desired user interfaces for robot simulation and control, assembly simulation and execution, Visual Programming and sensor simulation.

Today, laser manufacturers investigate and invest in automated assembly approaches in order to meet the high demands on flexibility, autonomy and efficiency and to counter competitive pressure. In the project “DeLas”, a consortium of manufacturers of micro-optical components and manufacturers of assembly equipment for micro-optical components came together with researchers in automation and robotics for transferring state-of-the-art methods in their academic fields to industrial assembly scenarios. Due to a surprising lack of automation and accompanying model- and simulation-based toolchains in this industrial sector so far, automation in micro-optical assembly strongly and newly benefits from tools well-established in other sectors, such as 3d simulation, visual programming and motion planning.

It contradicts the human's expectations when humanoid robots move awkwardly during manipulation tasks. The unnatural motion may be caused by awkward start or goal configurations or by probabilistic path planning processes that are often used. This paper shows that the choice of an arm's target configuration strongly effects planning time and how human-like a planned path appears. Human-like goal configurations are found using a criterion from ergonomics research. The knowledge which pose of the Tool Center Point (TCP) can be reached in a natural manner is encapsulated in a restricted reachability map for the robot arm.

Building on existent robotics knowledge to model and simulate anthropomorphic kinematics is an appealing approach, because sound knowledge gained in the fields of multi-robot and multi-agent systems can be applied. This provides-with little additional effort-the "new human" with capabilities ranging from natural arm movement up to the coordinated operation of his arms and the cooperation between multiple anthropomorphic kinematics. The same general hierarchical control structure that has successfully been used to control multi-robot systems for space and industrial application has therefore been enhanced to incorporate the newly required capabilities. The enhancements focus on a new approach to on the one hand consider human extremities as articulated robots which are mechanically connected to make up the body-and on the other hand to provide a control strategy to move the full body correctly under equilibrium conditions. We provide an overall control structure that preserves the capabilities of the single robots and introduces "sensible couplings" to move the body as a whole in a naturally looking way. As described in the paper, this work is currently being applied to several application fields in industry up to the simulation of astronauts' work on the International Space Station.