For the evaluation of grasp quality, different measures have been proposed that are based on wrench spaces. Almost all of them have drawbacks that derive from the non-uniformity of the wrench space, composed of force and torque dimensions. Moreover, many of these approaches are computationally expensive. We address the problem of choosing a proper task wrench space to overcome the problems of the non-uniform wrench space and show how to integrate it in a well-known, high precision and extremely fast computable grasp quality measure.

In the field of service robotics, whole arm contact with an unstructured environment or human beings becomes a major issue. Therefore soft robots, which mean robots with passively (or mechanically) compliant joints, become more and more important. In this work we analyze what Cartesian stiffness at the tool center point one can achieve with a passively compliant, redundant robot with variable joint stiffness. We restrict this work to the special case of uncoupled joint stiffness only, as coupling of joint stiffness seems to be mechanically difficult to realize. Finally we discuss a Cartesian controller, which incorporates the compliance of the joints and ensures the correct stiffness behavior also for high displacements from the desired position.

At our institute, two generations of antropomorphic hands have been designed. In quite a few experiments and demonstrations we could show the abilities of our hands and gain a lot of experience in what artificial hands can do, what abilities they need and where their limitations lie. In this paper, we would like to give an overview over the experiments performed with the DLR hands, our hands abilities and the things that need to be done in the near future.

In the robotic community more and more hands have been developed. These newly designed manipulators greatly outperform their ancestors in terms of available sensor signals, applicable grasping force, mechanical stability, reliability, kinematic design and more. This development extends the possible range and complexity of applications of robotic grippers also to areas outside of well structured laboratories and simple tasks. It also calls for more flexible control structures to provide a framework for implementing and executing these newly arising tasks without having to start from scratch for each new task. During the last few years we developed a control system architecture for DLR hand II that proved to be useful for a great variety of different applications. This paper presents the basic ideas behind DLR hand II's hard- and software architecture adapted to new needs in data processing.

Many methods for generating and analyzing grasps have been developed in the recent years. They gave insight and comprehension of grasping with robot hands but many of them are rather complicated to implement and of high computational complexity. In this paper we study if the basic quality criterion for grasps, the force-closure property, is in principle easy or difficult to reach. We show that it is not necessary to generate optimal grasps, due to a certain quality measure, for real robot grasping tasks where an average quality grasp is acceptable. We present statistical data that confirm our opinion that a randomized grasp generation algorithm is fast and suitable for the planning of robot grasping tasks.

Usually, grasp planning can be split into two phases. In the first phase one tries to find a set of contacts that allow for stable grasping of an object. In the second phase a feasible hand pose that realizes the grasp with a given hand is calculated. While this point is important for a practical grasp planning system, it has either been considered trivial or been solved by crude heuristics in most cases. Here we present an approach for calculating the hand and finger pose for a given grasp. The problem is formulated as a constraint satisfaction problem and then solved using optimization techniques. The method is applied to two different grasp types: the well known precision grasp and the pinch grasp which is preferred by men when grasping small objects.

Describes design and development efforts in DLR's robotics lab towards a new generation of ultra-light weight robots with articulated hands. The design of fully sensorized joints with complete state feedback and the underlying mechanisms are outlined. The second light-weight arm generation is available now, as well as the second generation of a worldwide most highly integrated 4 finger-hand is available now. Thus we hope that important steps towards a new generation of service and personal robots have been achieved.

In order to grasp and manipulate real world objects, grasp planning systems are required, and they have to be very fast in order to be integrated in online planning systems for robots. This paper presents a method to compute a desirable grasp quality measure very fast and accurately. Based on this measure a heuristic approach towards fast planning of precision grasps for arbitrarily shaped 3D objects is described. A number of feasible grasp candidates are generated heuristically. These grasp candidates are qualified using the described grasp quality measure and the best candidate is chosen. The planned grasps are robust with respect to grasp placement. It is shown that only a relatively small number of grasp candidates has to be generated in order to obtain a good, although not optimal, grasp.

We present a setup to control a four-finger anthropomorphic robot hand using a dataglove. To be able to accurately use the dataglove we implemented a nonlinear learning calibration using a novel neural network technique. Experiments show that a resulting positioning error not exceeding 1.8 mm, but typically 0.5 mm, per finger can be obtained; this accuracy is sufficiently precise for grasping tasks. Based on the dataglove calibration we present a solution for the mapping of human and artificial hand workspaces that enables an operator to intuitively and easily telemanipulate objects with the artificial hand.

In the near future, more and more robots will be used for servicing tasks, tasks in hazardous environments or space applications. Dextrous hands are a powerful and flexible tool to interact with these real world environments that are not specially tailored for robots. In order to grasp and manipulate real world objects, grasp planning systems are required. Grasp planning for general 3D objects is quite a complex problem requiring a large amount of computing time. Fast algorithms are required to integrate grasp planners in online planning systems for robots. This paper presents an heuristic approach towards fast planning of precision grasps for realistic, arbitrarily shaped 3D objects. In this approach a number of feasible grasp candidates are generated heuristically. These grasp candidates are qualified using an efficiently computable grasp quality measure and the best candidate is chosen. It is shown that only a relatively small number of grasp candidates has to be generated in order to obtain a good-although not optimal-grasp.

This paper describes a new approach to planning collision-free paths for cooperating robots with dependent tool center points (TCPs) in environments with obstacles. A method for a formal specification of these tasks is described and different planning strategies for different classes of tasks are presented. These planning strategies are designed for online planning in three-dimensional, realistic scenes. On the one hand, online planning demands results in very short time intervals. On the other, the planners have to cope with complex 3D environments. Following these requirements, the planners presented here avoid the complexity of a complete search and instead use heuristics.>

This paper describes a new approach to planning collision-free paths for cooperating robots with dependent tool center points (TCPs) in realistic environments. A formal description and a classification of these task are presented. Then, a path planner for online planning in 3D realistic scenes is described. On the one hand, online planning demands results in very short time intervals, on the other hand, the planners have to cope with complex 3D environments. Following these requirements, the planners presented here avoid the complexity of a complete search and use heuristics instead. These heuristics and strategies for different task classes are presented.>