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.

Future industrial robotic systems increasingly rely on automation of dangerous and tedious tasks. The acquisition of diverse and partially redundant information using reliable and rugged multimodal sensors helps to improve the safety an dependability of such systems. In this paper we propose proximity sensors suitable for the integration into the gripper of a forestry robot. The suggested sensors are complementary to vision based data acquisition as it can in particular provide information on objects close to the gripper that could otherwise not be obtained due to occlusion and missing direct line of sight. We present the design, fabrication and evaluation of 3Dand inkjet-printed capacitive sensors for grasping applications in harsh industrial environment, especially forestry robotics. The sensor elements have been developed along with a complete gripper re-design. The suggested fabrication strategy allows retrofitting of various industry components and reduced maintenance and costs as well as application-specific design and optimization and are also suitable for wireless operation. The developed gripper allows the support of grasping tasks by providing proximity and contact information.