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.

We present a system which improves the safety and accuracy of impedance controlled robotic manipulators with proximity perception. Proximity servoed manipulators, which use proximity sensors attached to the robot’s outer shell, have recently demonstrated robust collision avoidance abilities. Nevertheless, unwanted collisions cannot be avoided entirely. As a fallback safety mechanism, robots with joint force/torque sensing rely on impedance controllers for impact attenuation and compliant behavior. However, impedance controllers induce undesired deflections of the robot from its trajectory when it is not in contact. These deviations are more pronounced at soft configurations and when the robot grasps objects of unknown weight distribution, thus a compromise must be made between high positional accuracy and softness (safety). The proximity information allows the robot to react to anticipated impacts proactively for attenuation and damage reduction of unavoidable collisions, while still maintaining high accuracy during regular operation. This is achieved through variations of impedance parameters according to proximity measurements and motions towards safe joint configurations during the preimpact phase.

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.

Collision avoidance is a key technology towards safe human-robot interaction, especially on-line and fastreacting motions are required. Skins with proximity sensors mounted on the robot's outer shell provide an interesting approach to occlusion-free and low-latency perception. However, collision avoidance algorithms which make extensive use of these properties for fast-reacting motions have not yet been fully investigated. We present an improved collision avoidance algorithm for proximity sensing skins by formulating a quadratic optimization problem with inequality constraints to compute instantaneous optimal joint velocities. Compared to common repulsive force methods, our algorithm confines the approach velocity to obstacles and keeps motions pointing away from obstacles unrestricted. Since with repulsive motions the robot only moves in one direction, opposite to obstacles, our approach has better exploitation of the redundancy space to maintain the task motion and gets stuck less likely in local minima. Furthermore, our method incorporates an active behaviour for avoiding obstacles and evaluates all potentially colliding obstacles for the whole arm, rather than just the single nearest obstacle. We demonstrate the effectiveness of our method with simulations and on real robot manipulators in comparison with commonly used repulsive force methods and our prior proposed approach.

The ability of detecting materials plays an important role in robotic applications. The robot can incorporate the information from contactless material detection and adapt its behavior in how it grasps an object or how it walks on specific surfaces. In this, paper we apply machine learning on impedance spectra from capacitive proximity sensors for material detection. The unique spectra of certain materials only differ slightly and are subject to noise and scaling effects during each measurement. A best-fit classification approach to pre-recorded data is therefore inaccurate. We perform classification on ten different materials and evaluate different classification algorithms ranging from simple k-NN approaches to artificial neural networks, which are able to extract the material specific information from the impedance spectra.

Currently, the discriminative correlation filter‐based trackers have achieved higher tracking accuracy. However, visual tracking still faces challenges in terms of heavy occlusion, scale variation and so on. In this study, the authors intend to solve heavy occlusion by introducing collaborative model into classifier‐box. Firstly, they introduce complex colour features into correlation filter tracker to improve the effect of the tracker. Secondly, they introduce a multi‐scale method into their tracker to ease the scale problem. Thirdly, in order to solve the heavy occlusion in the tracking process, they adopt the locally weighted distance and classifier‐box. Their algorithm achieves distance precision rates of 81.7 and 77.4% on OTB2013 dataset and OTB2015 dataset, respectively. Their contribution focuses on solving heavy occlusion by using colour features, locally weighted distance and classifier‐box. The experimental results on OTB2013 and OTB2015 datasets demonstrate their algorithm to perform better than state‐of‐the‐art methods.

In this paper, we present a serial kinematic robot manipulator equipped with multimodal proximity sensing modules not only on the TCP but distributed on the robot's surface. The combination of close distance proximity information from capacitive and time-of-flight (ToF) measurements allows the robot to perform safe reflex-like and collision-free motions in a changing environment, e.g. where humans and robots share the same workspace. Our methods rely on proximity data and combine different strategies to calculate orthogonal avoidance motions. These motions are instantaneous optimal and are fed directly into the motion controller (proximity servoing). The strategies are prioritized, firstly to avoid collision and then secondly to maintain the task motion if kinematic redundancy is available. The motion is then optimized for avoidance, best manipulability, and smallest end-effector velocity deviation. We compare our methods with common force field based methods.

In this paper, we present a method for contactless material detection with capacitive proximity sensing skins. Our new approach extends the current state-of-the-art proximity and distance sensing methods and measures the characteristic impedance spectrum of an object to obtain material properties. By this, we gain further material information besides of the near field information in a contactless and non-destructive way. The measurement method requires sensors that provide absolute distance and frequency based capacitance measurement capabilities and can be applied to similar systems. The sensor system described in this paper measures proximity with a capacitance based sensor and absolute distance based on time-of-flight (ToF)sensors. Attached on a robot, we gain information about the robot's near field environment. The information is important not only for human- machine- interaction, but also for grasping and manipulation. We focus on signal processing and evaluate our method with measurements of numerous different materials and present a solution to differentiate between them.

The decline in genome sequencing costs has widened the population that can afford its cost and has also raised concerns about genetic privacy. Kim et al. present a practical solution to the scenario of secure searching of gene data on a semitrusted business cloud. However, there are three errors in their scheme. We have made three improvements to solve these three errors. (1) They truncate the variation encodings of gene to 21 bits, which causes LPCE error and more than 5% of the entries in the database cannot be queried integrally. We decompose these large encodings by 44 bits and deal with the components, respectively, to avoid LPCE error. (2) We abandon the hash function used in Kim’s scheme, which may cause HCE error with a probability of 2-22 and decompose the position encoding of gene into three parts with the basis 211 to avoid HCE error. (3) We analyze the relationship between the parameters and the CCE error and specify the condition that parameters need to satisfy to avoid the CCE error. Experiments show that our scheme can search all entries, and the probability of searching error is reduced to less than 2-37.4 .

In this paper we base upon capacitive tactile proximity sensor modules developed in a previous work to demonstrate applications for safe human-robot-interaction. Arranged as a matrix, the modules can be used to model events in the near proximity of the robot surface, closing the near field perception gap in robotics. The central application investigated here is object tracking. Several results are shown: the tracking of two human hands as well as the handling of occlusions and the prediction of collision for object trajectories. These results are important for novel pretouch- and touch-based humanrobot interaction strategies and for assessing and implementing safety capabilities with these sensor systems.