Stefan Escaida Navarro

Haptic object recognition using passive joints and haptic key features

This paper presents a novel approach for haptic object recognition with an anthropomorphic robot hand. Firstly, passive degrees of freedom are introduced to the tactile sensor system of the robot hand. This allows the planar tactile sensor patches to optimally adjust themselves to the objects surface and to acquire additional sensor information for shape reconstruction. Secondly, this paper presents an approach to classify an object directly from the haptic sensor data acquired by a palpation sequence with the robot hand - without building a 3d-model of the object. Therefore, a finite set of essential finger positions and tactile contact patterns are identified which can be used to describe a single palpation step. A palpation sequence can then be merged into a simple statistical description of the object and finally be classified. The proposed approach for haptic object recognition and the new tactile sensor system are evaluated with an anthropomorphic robot hand.

Haptic object recognition for multi-fingered robot hands

In this paper, we present an approach for haptic object recognition and its evaluation on multi-fingered robot hands. The recognition approach is based on extracting key features of tactile and kinesthetic data from multiple palpations using a clustering algorithm. A multi-sensory object representation is built by fusion of tactile and kinesthetic features. We evaluated our approach on three robot hands and compared the recognition performance using object sets consisting of daily household objects. Experimental results using the five-fingered hand of the humanoid robot ARMAR, the three-fingered Schunk Dexterous Hand 2 and a parallel Gripper are performed. The results show that the proposed approach generalizes to different robot hands.

Methods for safe human-robot-interaction using capacitive tactile proximity sensors

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.

Plug & produce by modelling skills and service-oriented orchestration of reconfigurable manufacturing systems

Abstract Shortening product lifecycles and small lot sizes require manufacturing systems to adapt increasingly fast. Many existing machine tools, handling and logistics systems provide a generic functionality that is not bound to a specific product. But this flexibility and reconfigurability on the level of individual resources is lost in automated systems that are limited to the production of a fixed set of product variants. We propose a unified abstraction for the skills provided by the available resources and the product-specific manufacturing requirements. From these high-level descriptions, executable manufacturing procedures are derived, exposed as services and dynamically orchestrated at runtime in order to achieve the manufacturing goals.

Haptic object recognition using statistical point cloud features

This work presents a point cloud approach for haptic object recognition with an anthropomorphic robot hand. It introduces several statistical point cloud features to provide robust descriptions of objects. It addresses the domain specific problems of sparsely populated and distorted point clouds that result from the direct interaction with the object. Also the contact normals registered during exploration — a natural byproduct — are taken into account for computing some of these features.

6D proximity servoing for preshaping and haptic exploration using capacitive tactile proximity sensors

In this paper we present applications for a robot system whose gripper is equipped with distributed capacitive tactile proximity sensors (CTPS). Firstly, we introduce and evaluate a closed loop control scheme by which it is possible to align the gripper to objects or features of the environment using proximity values alone. We call this control method proximity servoing. It is implemented by equilibrating the sensor signals, resulting in a robust preshape in all 6DOF of the object pose. Objects can then be grasped with virtually no displacement. Secondly, also based on proximity servoing, we demonstrate and evaluate novel ideas for combined haptic and proximity-based exploration. Without any cues from an external camera the system is capable of detecting and exploring features such as curvatures, edges or corners of objects. It is also shown that tactile and proximity exploration steps can be used complementarily to increase efficiency in exploration while delivering accurate object measurements.

Capacitive Tactile Proximity Sensing: From Signal Processing to Applications in Manipulation and Safe Human-Robot Interaction

Recently we have shown developments on capacitive tactile proximity sensors (CTPS) and their applications. In this work we give an overview of these developments and put them into a more general perspective, emphasizing what the common grounds are for the different applications, i.e., preshaping and grasping, haptic exploration as well as collision avoidance and safe human-robot interaction. We discuss issues related to signal processing and the design of a smart skin for the robot arm and its end-effector. On a higher level we discuss the concept of proximity servoing and its use for the above mentioned applications.

Telemanipulation with force-based display of proximity fields

In this paper we show and evaluate the design of a novel telemanipulation system that maps proximity values, acquired inside of a gripper, to forces a user can feel through a haptic input device. The command console is complemented by input-devices that give the user an intuitive control over parameters relevant to the system. Furthermore, proximity sensors enable the autonomous alignment/centering of the gripper to objects in user-selected DoFs with the potential of aiding the user and lowering the workload. We evaluate our approach in a user study that shows that the telemanipulation system benefits from the supplementary proximity information and that the workload can indeed be reduced when the system operates with partial autonomy.

A versatile and modular capacitive tactile proximity sensor

In this work we discuss the design and realization of a novel capacitive tactile proximity sensor (CTPS) with applications in robotics and consumer-electronics. The current concept is an advancement with respect to a previous design developed at our lab. Experience in the development of applications based on the old sensor, such as manipulation and safe HRI, helped determining the requirements. The new sensor is capable of operating in a self- and mutual-capacitive proximity mode and in a mutual-capacitive tactile mode. The issue of having a trade-off between spatial resolution and sensing range in proximity mode due to the capacitive measurement principle is addressed. A circuit is designed to dynamically select or join electrodes involved in the proximity measurements, a technique which at the same time allows the implementation of the spatial resolution in the tactile mode. Also, all signal processing is done on board of the prototype sensor module, thus complete modularity is achieved.

A haptic display for tactile and kinesthetic feedback in a CHAI 3D palpation training scenario

Robot-assisted minimally invasive surgery generally prevents the use of an important diagnostic tool in medicine: palpation. There are various approaches to reestablish the haptic feedback for the surgeon, including tactile displays and haptic input devices. We present a novel haptic display that features seven pins mounted on compression springs that can be pre-loaded with servo motors. Each pin has a stroke of 10 mm and a maximum counterforce of 1.1 N. An additional force of 0.7 N per pin can be applied with the motors. This technique allows for simultaneous stimulation of kinesthetic as well as tactile perception. The control of the haptic display has been implemented in the open-source haptics library CHAI 3D. We extended the framework with a multi interaction point tool to represent the hardware. This eventually lead to a palpation training program where bodies with multiple adjustable parameters encapsulated in a virtual soft tissue can be simulated. We evaluated this software in a user study with 15 participants in order to demonstrate the usability of the haptic display. With 90 % of successful hits, we are confident that sensible haptic feedback can be generated with the presented device. Furthermore, we are currently extending the scope of the haptic display to make use of a novel capacitive tactile proximity sensor in exploration scenarios.