This article presents a holistic approach to the engineering of an artificial robot skin for robots. An example of a multimodal skin cell is given, one that supports multiple human-like sensing modalities, and support for skin cell network is also provided; this is essential to form large-area skin patches in order to cover the surfaces of robots. The essential elements of efficiently handling a large amount of tactile data are explained. A general control framework, which supports robots commanded in position, velocity, and torque, is provided and validated. Several applications of this robot skin will be presented, demonstrating the effectiveness and efficiency of our artificial robot skin to support a wide number of robotic platforms as well as its ease of use across different domains.

In this paper, we present the mechatronic design of our Tactile Omnidirectional Robot Manipulator (TOMM), which is a dual arm wheeled humanoid robot with 6DoF on each arm, 4 omnidirectional wheels and 2 switchable end-effectors (1 DoF grippers and 12 DoF Hands). The main feature of TOMM is its arms and hands which are covered with robot skin. We exploit the multi-modal tactile information of our robot skin to provide a rich tactile interaction system for robots. In particular, for the robot TOMM, we provide a general control framework, capable of modifying the dynamic behavior of the entire robot, e.g., producing compliance in a non-compliant system. We present the hardware, software and middleware components of the robot and provide a compendium of the base technologies deployed in it. Furthermore, we show some applications and results that we have obtained using this robot.

In this paper we propose event-based signaling for large-scale artificial robotic skin to reduce bandwidth requirements on data transmission and processing power. We use the send-on-delta principle to trigger the event generation only when tactile sensors are stimulated and transduce novel information. To compare the standard non-event based method with the proposed event-based method we present a comprehensive analysis of large-scale artificial skin systems for different test applications. For this purpose we collect data of 260 CellulARSkin cells on an UR-5 arm and calculate the events off-line. We determine the optimal packet size for event-based signaling and we show that the event-based system reduces the data rate with respect to the non-event based system for an unstimulated skin cell network to 16.45% and for a heavily stimulated skin cell network to 47.69%. The obtained results show that the event-based system reduces the data redundancy and the required transmission rates without loosing information.

In this paper, we present a new approach to spatially self-organize a modular artificial skin in 3D space. We were motivated by the demand to efficiently and automatically acquire the position and orientation of a steadily growing number of artificial skin sensor elements. Here, we combine our 3D surface reconstruction algorithm for individual patches of artificial skin, with a common active visual marker approach. Light emitting diodes, built into every element of our modular artificial skin, enable us to turn each reconstructed patch of skin into an active 6 DoF visual marker. With the help of a calibrated monocular camera, we can then estimate the homogeneous transformations between multiple, at least partially visible skin patches e.g. when distributed on the body of a robot. Our approach allows to quickly combine distributed tactile and visual coordinate systems into one homogeneous rigid body representation. We demonstrate the robustness of our approach by calibrating several patches mounted on a robot arm using only a standard web-cam.

In this paper, we present a tactile approach to grasp large and unknown objects, which can not be easily manipulated with a single end-effector or two-handed grasps, with the whole upper body of a humanoid robot. Instead of conventional joint level force sensing, we equip the robot with various patches of HEX-o-SKIN - a self-organizing, multi-modal cellular artificial skin. Low-level controllers, one allocated to each sensor cell, utilize a self-explored inverted jacobian-like sensory-motor map to directly transfer tactile stimulation into reactive arm motions, altering basic grasping trajectories to the need of the current object. A high-level state machine guides those low-level controllers during the different states of the grasping action. Desired contact points, and key poses for the trajectory generation, are taught through forceless tactile stimulation. First experiments on a position controlled robot, an HRP-2 humanoid, demonstrate the feasibility of our approach. Our paper contributes to the first realization of a self-organizing tactile sensor-behavior mapping on a full-sized humanoid robot, which enables: 1) a new general approach for grasping unknown objects with the whole-body; and 2) a novel way of teaching behaviors using pre-contact tactile sensing.

In this paper, we present a twofold, open-loop method to explore, model and calibrate articulated robots equipped with artificial skin. We do so, using a 3-axis accelerometer per artificial sensor skin unit (SU) and special excitation pattern on every actuated degree of freedom (DoF) of the robotic joints. The first algorithm extracts the kinematic dependencies in between segments, equipped with artificial skin units, and joints, featuring one or multiple rotatory DoFs. A second algorithm uses this structural knowledge to automatically build and estimate kinematic models in between a static reference and an end effector segment. We show experimental results for the structural exploration with a KUKA light weight robotic arm equipped with our own SU prototypes. Additional simulation results, supporting our approach on estimating the kinematic parameters of the robot, are also presented.

In this paper, we present a new approach to reconstruct the 3D surface of robotic body parts equipped with artificial skins. We do so by fusing static knowledge on the shape, size and tessellation capabilities of the uniform cell our skin consists of, together with dynamic knowledge on its neighbors and measurements from its orientation sensor - a 3-axis accelerometer. Our approach makes it possible to reconstruct the 3D surface of robotic body parts equipped with a patch of skin in a very short time, providing the location and orientation of every unit in a patch relative to an automatically chosen origin on the patch, utilizing no external sensors and only robot independent information. We show experimental results on the 3D reconstruction of different skin patches.

In this paper, we present a new generation of active tactile modules (i.e., HEX-O-SKIN), which are developed in order to approach multimodal whole-body-touch sensation for humanoid robots. To better perform like humans, humanoid robots need the variety of different sensory modalities in order to interact with their environment. This calls for certain robustness and fault tolerance as well as an intelligent solution to connect the different sensory modalities to the robot. Each HEX-O-SKIN is a small hexagonal printed circuit board equipped with multiple discrete sensors for temperature, acceleration, and proximity. With these sensors, we emulate the human sense of temperature, vibration, and light touch. Off-the-shelf sensors were utilized to speed up our development cycle; however, in general, we can easily extend our design with new discrete sensors, thereby making it flexible for further exploration. A local controller on each HEX-O-SKIN preprocesses the sensor signals and actively routes data through a network of modules toward the closest PC connection. Local processing decreases the necessary network and high-level processing bandwidth, while a local analog-to-digital conversion and digital-data transfers are less sensitive to electromagnetic interference. With an active data-routing scheme, it is also possible to reroute the data around broken connections-yielding robustness throughout the global structure while minimizing wirings. To support our approach, multiple HEX-O-SKIN are embedded into a rapid-prototyped elastomer skin material and redundantly connected to neighboring modules by just four ports. The wiring complexity is shifted to each HEX-O-SKIN such that a power and data connection between two modules is reduced to four noncrossing wires. Thus, only a very simple robot-specific base frame is needed to support and wire the HEX-O-SKIN to a robot. The potential of our multimodal sensor modules is demonstrated experimentally on a robot platform.