During the co-presence of human workers and robots, measures are required to avoid injuries from undesired contacts. Capacitive Proximity Sensors (CPSs) offer a cost-effective solution to cover the entire robot manipulator with fast close-range perception for HRC tasks, closing the perception gap between tactile detection and mid-range perception. CPSs do not suffer from occlusion and compared to pure tactile or force sensing, they react earlier and allow increasing the operating speed of Collaborative Robots (Cobots) while still maintaining safety. However, since capacitive coupling to obstacles varies with their distance, shape and material properties, the projection from capacitance to actual distances is a general problem. In this work, we propose an universal benchmark test procedure for fellow researchers to evaluate their CPSs. Considering ISO/TS 15066 for Power and Force Limiting (PFL) as a reference, we derive the requirements for the specified body regions and propose a method for determining the operation speed to comply with PFL based on a pre-defined detection threshold. Finally, the benchmark test procedure is evaluated on three different concepts of CPSs from the contributed researchers, demonstrating the general applicability.

Awareness of its surroundings is a crucial capability for a robot meant to be working alongside other robots or human operators. When considering safety norms and modalities, in particular the Speed and Separation Monitoring (SSM), proper proximity information can make the difference in the overall efficiency of a use case, for example avoiding unnecessary penalizations in the cycle-time. This paper presents a method to exploit the proximity perception capabilities of radar sensors to construct a continuous speed control algorithm for a UR10 robot. With respect to standard implementations of the SSM in industrial and collaborative environments, the proposed speed control is enhanced by the addition of direct human’s velocity measurement, full direction of travel and target classification. The results are evalauted according to the SSM metrics for safety and productivity, showing an overall increase in efficiency while still maintaining safety level requirements.

With the availability of small system in package realizations, radar systems become more and more attractive for a variety of applications in robotics, in particular also for collaborative robotics. As the simulation of robot systems in realistic scenarios has become an important tool, not only for design and optimization, but also e.g. for machine learning approaches, realistic simulation models are needed. In the case of radar sensor simulations, this means providing more realistic results than simple proximity sensors, e.g. in the presence of multiple objects and/or humans, objects with different relative velocities and differentiation between background and foreground movement. Due to the short wavelength in the millimeter range, we propose to utilize methods known from computer graphics (e.g. z-buffer, Lambertian reflectance model) to quickly acquire depth images and reflection estimates. This information is used to calculate an estimate of the received signal for a Frequency Modulated Continuous Wave (FMCW) radar by superposition of the corresponding signal contributions. Due to the moderate computational complexity, the approach can be used with various simulation environments such as V-Rep or Gazebo. Validity and benefits of the approach are demonstrated by means of a comparison with experimental data obtained with a radar sensor on a UR10 arm in different scenarios.