Heinz Wörn

The working principle of resistive tactile sensor cells

Tactile sensors systems are very important for todays service robotics. Designed as a holohedral cover for a robot, they are suitable for collision detection when working in unstructured environments, for human-machine interaction or, with a high resolution, as object sensors enabling dexterous hands for reactive gripping. In this paper, we explain construction and working principle of resistive tactile sensor cells. The latter is based on the change of the electrical resistance between a conductive polymer and at least two electrodes. For this effect, we formulate a model to describe the dependence between the sensors electrical resistance and the applied load. The model enables further improvements of resistive tactile sensor cells.

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.

A framework of space–time continuous models for algorithm design in swarm robotics

Designing and analyzing self-organizing systems such as robotic swarms is a challenging task even though we have complete knowledge about the robot’s interior. It is difficult to determine the individual robot’s behavior based on the swarm behavior and vice versa due to the high number of agent–agent interactions. A step towards a solution of this problem is the development of appropriate models which accurately predict the swarm behavior based on a specified control algorithm. Such models would reduce the necessary number of time-consuming simulations and experiments during the design process of an algorithm. In this paper we propose a model with focus on an explicit representation of space because the effectiveness of many swarm robotic scenarios depends on spatial inhomogeneity. We use methods of statistical physics to address spatiality. Starting from a description of a single robot we derive an abstract model of swarm motion. The model is then extended to a generic model framework of communicating robots. In two examples we validate models against simulation results. Our experience shows that qualitative correctness is easily achieved, while quantitative correctness is disproportionately more difficult but still possible.

Robot Manipulation of Deformable Objects

Besides the work in the field of manipulating rigid objects, currently, there are several research and development activities going on in the field of manipulating non-rigid or deformable objects. Several papers have been published on international conferences in this field from various projects and countries. But there has been no comprehensive work which provides both a representative overview of the state of the art and identifies the important aspects in this field. Thus, we collected these activities and invited the corresponding working groups to present an overview of their research. Altogether, nineteen authors coming from Japan, Germany, Italy, Greece, United Kingdom, and Australia contributed to this book. Their research work covers all the different aspects that occur when manipulating deformable objects. The contributions can be characterized and grouped by the following four aspects: * object modeling and simulation, * planning and control strategies, * collaborative systems, and * applications and industrial experiences. In the following, we give a short motivation and overview of the single chapters of the book. The simulation of deformable objects is one way to approach the problem of manipulating these objects by robots. Based on a physical model of the object and the occurring constraints, the resulting object shape is calculated. In Chapter 2, Hirai presents an energy-based approach, where the internal energy under the geometric constraints is minimized. Frugoli et al. introduce a force-based approach, where the forces between discrete particles are minimized meeting given constraints. Finally, Remde and Henrich extend the energy-based approach to plastic deformation and give a solution of the inverse simulation problem. Even if the object behavior is predicted by simulation, there is still the question of how to control the robot during a single manipulation operation. An additional question is how to retrieve an overall plan for the concatenated manipulation operations. In Chapter 3, Wada investigates the control problems when positioning multiple points of a planar deformable object. McCarrager proposes a control scheme exploiting the flexibility, rather than minimizing it. Abegg et al. use a simple contact state model to describe typical assembly tasks and to derive robust manipulation primitives. Finally, Ono presents an automatic sewing system and suggests a strategy for unfolding fabric. In several manipulation tasks, it is reasonable to apply more than one robot. Especially in cases, where the deformable object has to take a specific shape. Since the robots working at the same object are influencing each other, different control algorithms have to be introduced. In Chapter 4, Yoshida and Kosuge investigates this problem for the task of bending a sheet of metal and exploits the relation ship between the static object deformation and the bending moments. Tanner and Kyriakopoulos regard the deformable object as underactuated mechanical system and make use of the existence of non-holonomic constraints. Both approaches model the deformable object as finite elements. All of the above aspects have their counterpart in different applications and industrial experiences. In Chapter 5, Rizzi et al. present test cases and applications of their approach to simulate the manipulation of fabric, wires, cables, and soft bags. Buckingham and Graham give an overview of two European projects processing white fish including locating, gripping, and deheading the fish. Maruyama outlines the three development phases of a robot system for performing outage-free maintenance of live-line power supply in Japan. Finally, Kamper presents the development of a flexible automatic cabling unit for the wiring of long-tube lighting with plug components.

Opening a door with a humanoid robot using multi-sensory tactile feedback

This paper presents a multi-sensor based generic approach to opening doors for a dexterous robot. Once the handle has been located by a computer vision algorithm and properly grasped, we are able to open doors without using a model or other prior knowledge of the door geometry. This is done by combining the sensor information of both a force-torque sensor in the robot wrist and a tactile sensor matrix in the robot gripper itself. Our experimental results show that the combination of both sensors achieves the most successful way to open the door.

A novel approach to proactive human-robot cooperation

This paper introduces the concept of proactive execution of robot tasks in the context of human-robot cooperation with uncertain knowledge of the humans intentions. We present a system architecture that defines the necessary modules of the robot and their interactions with each other. The two key modules are the intention recognition that determines the human users intentions and the planner that executes the appropriate tasks based on those intentions. We show how planning conflicts due to the uncertainty of the intention information are resolved by proactive execution of the corresponding task that optimally reduces the systems uncertainly. Finally, we present an algorithm for selecting this task and suggest a benchmark scenario.

A safe robot system for craniofacial surgery

The ultimate ambition of a robotic system in surgical theater has to be the safety of the involved humans: patient, physicians, nurses. In order to provide that claim the criteria of ergonomics, redundancy, short reaction time, and accuracy must be met. In contrast to many other surgical robotic applications we are studying complex bone cut trajectories which require several distinct orientations of the robot tool and milling cutter, respectively. Exact bone cuts are especially needed for bone repositionings placed at the human skull in craniofacial surgery. Therefore, the system has to be flexible enough to keep the capability of changing the patients position during the intra-operative phase. This paper introduces the overall concept and realization of the system.

Tactile sensing for an anthropomorphic robotic hand: Hardware and signal processing

In this paper, a tactile sensing system for an anthropomorphic robot hand is presented. The tactile sensing system is designed as a construction kit making it very versatile. The sensor data preprocessing is embedded into the hands hardware structure and is fully integrated. The sensor system is able to gather tactile pressure profiles and to measure vibrations in the sensors cover. Additionally to the introduction of the hardware, the signal processing and the classification of the acquired sensor data will be explained in detail. These algorithms make the tactile sensing system capable to detect contact points, to classify contact patterns and to detect slip conditions during object manipulation and grasping.

Two different approaches to a macroscopic model of a bio-inspired robotic swarm

By compiling macroscopic models we analyze the adaptive behavior in a swarm of autonomous robots generated by a bio-inspired, distributed control algorithm. We developed two macroscopic models by taking two different perspectives: A Stock & Flow model, which is simple to implement and fast to simulate, and a spatially resolved model based on diffusion processes. These two models were compared concerning their prediction quality and their analytical power: One model allowed easy identification of the major feedback loops governing the swarm behavior. The other model allowed analysis of the expected shapes and positions of observable robot clusters. We found a high correlation in the challenges posed by both modeling techniques and we highlighted the inherent problems of inferring emergent macroscopic rules from a microscopic description of swarm behavior.

Manipulating deformable linear objects - contact states and point contacts

The task of handling non-rigid one-dimensional objects by a robot manipulation system is investigated. To distinguish between different non-rigid object behaviors, five classes of deformable objects from a robotic point of view are proposed. Additionally, an enumeration of all possible contact states of one-dimensional objects with polyhedral obstacles is provided. Finally, the qualitative motion behavior of linear objects is analyzed for stable point contacts. Experiments with different materials validate the analytical results.