This work introduces a novel method for the generation of process-aligned robotic pathways specifically designed for surface processing applications. The proposed approach integrates the interpretation of sensor data, computer vision algorithms, and process knowledge modeling to address the complexities inherent in robotic programming. To mitigate programming challenges, the method incorporates intuitive interaction techniques, including hand gestures and human-computer interaction (HCI), thereby facilitating the efficient generation of robotic paths. Additionally, it augments the user’s teaching experience by enabling the seamless deployment of the methodology in serial production settings, accommodating the variability of both workpieces and environmental conditions. The proposed framework ensures smooth integration of robotic systems into complex workflows by aligning robotic paths with the unique requirements of surface processing tasks.

Industrial robot programming necessitates specialized expertise and significant time commitment, particularly for small-batch productions. In response to the escalating demand for production agility, novel approaches have emerged in intuitive robot programming. These inventive systems, rooted in diverse conceptual frameworks, are designed to expedite the deployment of robot systems. A prominent innovation in this domain is adopting no-code robot programming through finger-based gestures. A robot program can be generated by capturing and tracking non-expert users’ finger movements and gestures, converting 3D coordinates into an executable robot programming language. However, accurately determining finger positions for 3D coordinates and precise geometrical features presents an ongoing challenge. In pursuit of heightened trajectory precision and reducing more significant effort for the users, we propose a hybrid methodology that amalgamates finger-gesture programming with point cloud data. This synergistic integration demonstrates promising outcomes, substantiating its potential to facilitate the precise and adaptive generation of robot paths within robot applications.

The programming of industrial robots necessitates specialized expertise and significant time and effort, particularly for small batch sizes. However, with the increasing demand for agility in production, the solutions used for robot programming have evolved significantly. Intuitive robot programming systems based on diverse concepts have been introduced to facilitate rapid deployment of robot systems. One such approved concept is no-code robot programming with finger-based gesture. In this concept, non-expert users draw a robot path via finger movement, which is subsequently translated into robot programming language to facilitate the corresponding movement. A significant challenge associated with this method is the valid replication of the corresponding robot Tool Center Point (TCP) orientation. Reachability issues, non-compliant hand contortions, and sensor occlusions make it difficult to directly derive the robot’s TCP orientation from the finger’s orientation. This work presents two novel approaches for estimating robot orientation using numerical analysis and point cloud information for finger-based robot programming without requiring prior knowledge. The first approach utilizes numerical analysis to estimate the relative robot orientation based on the geometry of the trajectory. In contrast, the second approach uses point-cloud to derive the robot orientation based on the object contour. Input shaping algorithms are employed and evaluated to reduce the divergence in the orientation estimations. Experiments demonstrate the effectiveness of the proposed approach utilizing a low-cost camera as a cost-efficient alternative to existing no-code programming strategies, potentially accelerating the real-world deployment of robotic applications in industrial environments.

Circular economy and agile manufacturing require a safe and efficient industrial robot system working in close human proximity. Although, close proximity local sensing enables safe collaboration with small cobots. However, they cannot ensure safety at high velocities with a heavy-duty industrial robot. Stereo-camera and 3D LiDAR-based touch-less global sensing methods exist, but they do not address the safety standards. This work proposes a novel method for estimating the safety parameters in speed and separation monitoring mode for 3D vision sensors. Accurate estimation of these parameters ensures compact sensing zones. Thus, enabling efficient human-robot collaboration for permanent operator presence. The method requires less effort in setup. The developed software with a graphical user interface enables workers from wide technical expertise to perform safety measurements at a precision of ±15ms in reaction time estimation. The experiments are repeatable and capture the statistical data for low error in estimation. The estimated parameters for two exemplary 3D sensors enable a human to work in close proximity of 20 cm while enabling safety from a collision.

Accurate detection of the human body and its limbs in real-time is one of the challenges toward human-robot collaboration (HRC) technology in the factory of the future. In this work, a new algorithm has been developed to fuse thermal, depth, and color information. Starting with the calibration of the sensors to each other, proceeding with data fusion and detection of human body parts, and ending with pose estimation. The proposed approach has been tested in various HRC scenarios. It has shown a significant decrease in errors and noise in industrial environments. Furthermore, it produces not only a higher positioning accuracy of the head and hands compared to the state of art algorithms (e.g., OpenPose), but it is also faster. Hence, such an algorithm could be joined with digital twins for safe and efficient human-robot collaboration.

In this paper, a suitable measurement setup is presented and applied to conduct force and pressure measurements for transient contact cases with the shoulder at the example of lathe machine tending. Empirical measurements were executed on a selected collaborative robot's behavior regarding allowable operating speeds under consideration of sensor sensitivity, robot collision geometry, and damping materials. Comparisons between the theoretic calculations proposed in ISO/TS 15066 and the practical measurement results present a basis for future research. With the created database, preliminary risk assessment and economic assessment procedures of collaborative machine tending cells can be facilitated.

Shorter product lifecycles, mass individualization and agile concepts for lot size1 require more flexible production. Constant challenges may result in optimized processes. However, opimization effects not only on the production technologies but also on the operators and their ergonomics. A simulation based ergonomic evaluation in such hybrid workspaces is tough and fruitless due to constantly changing in the production processes and to the diversity of the human activities. An online ergonomics assessment and real-time feedback ensures that employees improve the quality of their workplace and maintain healthy working conditions. This work proposes a method for agile production or traditional factory floors using a marker less camera-based system using real-time joint position estimations as input and providing a continuous or summarized individual ergonomic feedback as output. Furthermore, first implementation results considering the requirements and overall system design for online ergonomic evaluation in agile production environment are discussed.

Unlike robots, humans do not have constant movements. Their pathways are individually changeable and influenced by circumstances. This paper presents a method to investigate human pathway variations in a real study. In systematically selected tasks, human pathways are examined for 100 participants in manual and human-robot collaboration (HRC) scenarios. As a result, the variations of pathways are presented depending on various features: e.g. in nearly all cases the variance of women's walking pathways is smaller than that of men. VariPath database can be used in any planning process of manual or HRC scenarios to ensure safety and efficiency.

Human-Robot shared workspace is a dynamic and unstructured environment. In such environment, pre-programmed robot paths might cause collisions or production disruptions. In order to solve this problem, motion planning framework has been proposed that adapts the robot's movement (path and speed)according to the human movement or any other dynamic obstacles in real time. Firstly, it defines the safety distance between robot and human or other obstacles. During run-time the 3D-Smart-Sensors capture the current position of human and other dynamic/static obstacles in human-robot shared workspace. The proposed framework plans and optimizes collision free robot trajectories with consideration for safety distance, path length and executing time. Once a new optimal trajectory is found, the framework controls the robot to adjust its movement paths. Moreover, the proposed framework can adjust the robot velocity based on the 3D-Zone Model of human-robot shared workspace. Therefore, the robot can reach its goal quickly and safely in a dynamic and unstructured environment.

This research work proposes a robotized workstation for automatic disassembly of electric vehicle motors. A novel image processing algorithm is proposed for autonomous detection of motor screws, to automate the task of motor disassembly. Instead of having a database of templates for matching, the screws are detected based on their characteristics with respect to its grayscale, depth and HSV values. Furthermore, with frame iterations, the accuracy of the system is increased mean while reducing its runtime. The algorithm is successfully tested and implemented and yields highly accurate results.

Most of robot applications require robots to interact with environment, objects or even with human, which need to integrate vision and force information. Generally, there are five types of vision-force control: pure position control, pure force control, traded control, shared control and hybrid control. The important questions here are: How to define the most appropriate control mode for every part of different tasks and when the control system should switch from one control mode to the other. In this work an automatic decision system is suggested to define the most appropriate control mode for successive tasks and to choose the optimal structure of vision/force control without human intervention. This research will focus on the operations of library automation as real application for the proposed control system such as shelving, storage and sorting of inaccurately placed objects.