Over the last years collaborative robots have gained great success in manufacturing applications where human and robot work together in close proximity. However, current ISO/TS-15066-compliant implementations often limit the efficiency of collaborative tasks due to conservative speed restrictions. For this reason, this paper introduces a deep-learning-based human-robot-safety framework (HRSF) that aims at a dynamical adaptation of robot velocities depending on the separation distance between human and robot while respecting maximum biomechanical force and pressure limits. The applicability of the framework was investigated for four different deep learning approaches that can be used for human body extraction: human body recognition, human body segmentation, human pose estimation, and human body part segmentation. Unlike conventional industrial safety systems, the proposed HRSF differentiates individual human body parts from other objects, enabling optimized robot process execution. Experiments demonstrated a quantitative reduction in cycle time of up to 15% compared to conventional safety technology.

A Safety-A ware Kinodynamic Architecture for Human-Robot Collaboration Andrea Pupa 1, Mohammad Arrfou 2, Gildo Andreoni 2 and Cristian Secchi 1 Abstract -- The new paradigm of human-robot collaboration has led to the creation of shared work environments in which humans and robots work in close contact with each other . Consequently, the safety regulations have been updated addressing these new scenarios. The mere application of these regulations may lead to a very inefficient behavior of the robot. In order to preserve safety for the human operators and allow the robot to reach a desired configuration in a safe and efficient way, a two layers architecture for trajectory planning and scaling is proposed. The first layer calculates the nominal trajectory and continuously adapts it based on the human behavior . The second layer, which explicitly considers the safety regulations, scales the robot velocity and requests for a new trajectory if the robot speed drops. The proposed architecture is experimentally validated on a Pilz PRBT manipulator . I. I NTRODUCTION The introduction and diffusion of collaborative robotics within the industrial environments has allowed to create shared workspace where humans and robots can work closely. While this new paradigm has led to an increase in the flexibility of production lines, the lack of physical barriers requires to pay more attention on how to guarantee human safety.

In robotics, many control and planning schemes have been developed to ensure human physical safety in human-robot interaction. The human psychological state and the expectation towards the robot, however, are typically neglected. Even if the robot behaviour is regarded as biomechanically safe, humans may still react with a rapid involuntary motion (IM) caused by a startle or surprise. Such sudden, uncontrolled motions can jeopardize safety and should be prevented by any means. In this letter, we propose the Expectable Motion Unit (EMU), which ensures that a certain probability of IM occurrence is not exceeded in a typical HRI setting. Based on a model of IM occurrence generated through an experiment with 29 participants, we establish the mapping between robot velocity, robot-human distance, and the relative frequency of IM occurrence. This mapping is processed towards a real-time capable robot motion generator that limits the robot velocity during task execution if necessary. The EMU is combined in a holistic safety framework that integrates both the physical and psychological safety knowledge. A validation experiment showed that the EMU successfully avoids human IM in five out of six cases.

In this paper, we present an approach for quantifying the propagated uncertainty of robot systems in an online and data-driven manner. Especially in Human-Robot Collaboration, keeping track of the safety compliance during run time is essential: Misclassifying dangerous situations as safe might result in severe accidents. According to official regulations (eg, ISO standards), safety in industrial robot applications depends on critical parameters, such as the distance and relative velocity between humans and robots. However, safety can only be assured given a measure for the reliability of these parameters. While different risk detection and mitigation approaches exist in literature, a measure that can be used to evaluate safety limits online, and succinctly implies whether a situation is safe or dangerous, is missing to date. Motivated by this, we introduce a generalizable method for calculating the propagated measurement uncertainty of arbitrary parameters, that captures the accumulated uncertainty originating from sensory devices and environmental disturbances of the system. To show that our approach delivers correct results, we perform validation experiments in simulation. In addition, we employ our method in two real-world settings and demonstrate how quantifying the propagated uncertainty of critical parameters facilitates assessing safety online in Human-Robot Collaboration.