Uncertainties in grasp prediction for unknown, arbitrarily shaped objects in cluttered environments and un- certainty of the kinematics (e.g., series elastic robots or soft robots) can lead to poor grasps. For delicate objects, such poor grasps may damage the objects when they are dropped or when high local pressure is introduced in the grasping process. We propose a tactile sensor concept that allows predicting the quality of a grasp such that the object can be safely moved without being dropped. This prediction is done using an initial low force grasp and the force is only increased when the contact area is sufficiently large. The proposed customizable wireless Capacitive Tactile Sensor Array (CTSA) uses the deformation of a polymer to assess the contact area and the force distribution. A common homogeneous deformable electrode is embedded in the polymer. This electrode does not require any patterning nor any electrical connection but to ground. We present the manufacturing process which allows for robust yet cost effective realizations with a variety of electrode materials including conductive inks, conductive textiles, metal meshes and metal sheets. With the different approaches, parameters such as sensitivity and recovery time can be adjusted. Furthermore, the robustness of the sensor towards strong forces and objects with sharp edges and corners is shown. Finally, we demonstrate the benefits of the proposed sensor for grasping in a series of scenarios with rigid and soft 3D printed objects of various shapes. Allowing a reasonable false positive rate, 100 % of unsuccessful grasps in our evaluation experiments could be detected from the initial low force grasp.

A robot must comply with very restrictive safety standards in close human-robot collaboration applications. These standards limit the robot's performance because of speed reductions to avoid potentially large forces exerted on humans during collisions. On-robot capacitive proximity sensors (CPS) can serve as a solution to allow higher speeds and thus better productivity. They allow early reactive measures before contacts occur to reduce the forces during collisions. An open question on designing the systems is the selection of an adequate activation distance to trigger safety measures for a specific robot while considering latency and detection robustness. Furthermore, the systems' actual effectiveness of impact attenuation and performance gain has not been evaluated before. In this work, we define and conduct a unified test procedure based on collision experiments to determine these parameters and investigate the performance gain. Two capacitive proximity sensor systems are evaluated on this test strategy on two robots. A significant performance increase can be achieved, since a small detection distance doubles robot operation speed while maintaining the same contact force as without Capacitive Proximity Sensor (CPS). This work can serve as a reference guide for designing, configuring and implementing future on-robot CPS.

Proximity perception is a technology that has the potential to play an essential role in the future of robotics. It can fulfill the promise of safe, robust, and autonomous systems in industry and everyday life, alongside humans, as well as in remote locations in space and underwater. In this survey article, we cover the developments of this field from the early days up to the present, with a focus on human-centered robotics. In this domain, proximity sensors are typically deployed in two scenarios: first, on the exterior of manipulator arms to support safety and interaction functionality, and second, on the inside of grippers or hands to support grasping and exploration. Therefore, based on this observation, in the beginning of this article, we propose a categorization to organize the use cases of proximity sensors in human-centered robotics. Then, we devote effort to present the sensing technologies and different measuring principles that have been developed over the years, also providing a summary in form of a table. Following, we review the literature regarding the applications that have been proposed. Finally, we give an overview of the most important trends that will shape the future of this domain.

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.

Electronic data-sheets that are stored within and provided by a transducer such as ISO/IEC/IEEE 21450 Transducer Electronic Data-sheets are currently intended to ease the utilization of transducers. This is achieved as the data-sheets provide information about sensing and actuation as well as communication capabilities, data formats, calibration information and more. However, so far it can not be ensured that the provided information is trustworthy. Consequently, we propose a concept based on ISO/IEC/IEEE 21450 Transducer Electronic Data-sheets in which signatures and secure elements are used to validate the information in the data-sheets, the manufacturer and calibration labs. Furthermore, this information can be utilised for secure key exchange mechanisms and permission management. This approach ensures that the low-power consumption expected of transducers is still met while providing security and trust.

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.

We present a novel approach of active object categorization based on an iterative Bayesian method using capacitive sensing during object manipulation. The approach uses a novel type of capacitive sensor, which can measure internal properties of materials that are inaccessible to vision or tactile sensing. The electrodes of this capacitive sensor are sufficiently flexible and thin to be attached to various types of robot tools, including humanoid fingers and palms. They are mechanically robust and wear resistant. In comparison to earlier capacitive sensors systems, we perform single ended and differential measurements over an array of electrodes, and coordinate measurement with robot manipulation to extract information not available from static measurements. We demonstrate the capability of our active object categorization system in the James robot bartender system. This system can manipulate objects and measure continuously in order to categorize empty and non-empty bottles. The principle of capacitive sensing during manipulation can be applied to more general object manipulation tasks in robotics and also in other fields of industrial automation.

All mammals but humans use whiskers in order to rapidly acquire information about objects in the vicinity of the head. Collisions of the head and objects can be avoided as the contact point is moved from the body surface to the whiskers. Such a behavior is also highly desirable during many robot tasks such as for human-robot interaction. Using novel capacitive proximity sensors, robots sense when they approach a human (or an object) and react before they actually collide with it. We propose a sensor and control concept that mimics the behavior of whiskers by means of capacitive sensors. Major advantages are the absence of physical whiskers, the absence of blind spots and a very short response time. The sensors are flexible and thin so that they feature skin-like properties and can be attached to various robotic link and joint shapes. In comparison to capacitive proximity sensors, the proposed virtual whiskers offer better sensitivity towards small conductive as well as non conductive objects. Equipped with the new proximity sensors, a seven-joint robot for humanrobot interaction tasks shows the efficiency and responsiveness of our concept.