We explore the characteristics of secondary contacts when applying forces with the end-effector of a robot and address the question when these secondary contacts can increase maximum applicable end-effector forces or reduce required actuator efforts. To this end, we formalize the effect of such secondary contacts in terms of required actuator efforts and derive efficiency bounds depending on the contact characteristics and robot configuration. Our findings are confirmed by experiments with a redundant serial manipulator.

Projection operators are widely employed in multi-objective robot control. It is an open research question how to achieve continuous transitions between different idempotent projectors which is required for dynamic task priority rearrangement. We formalize projection shaping, providing a solution to deal with rank changes in a smooth fashion. Furthermore, we derive meaningful shaping operators and show that damped least squares is a special case of our general formulation. Finally, we extend the Stack-of-Tasks prioritization scheme for continuous priority rearrangement of single task dimensions. Simulation results validate our approach.

When learning forward and inverse kinematics maps of manipulators, usually little attention is paid to data-efficiency, i.e., the accuracy gained per action-outcome sample. This paper examines properties of popular (online) learning techniques and demonstrates that - regardless of the employed exploration strategy - the structure of kinematics mappings does not allow for a practically viable trade-off between the number of samples and the resulting approximation error for manipulators with more than a few DoFs - unless tailored parametric models are employed. We discuss suitable choices for these parametric models for both rigid and elastic discretely-actuated robots and compare their data -efficiency to that of popular exploratory learning approaches relying on non-parametric models. Our theoretical considerations are confirmed by various experimental results for inverse kinematics mappings of rigid and omnielastic manipulators.

Tactile surface sensors (TSSs) are often utilized for contact management in human robot interaction scenarios. To provide added value in these applications, online robot programming approaches may exploit TSSs as gesture input devices. To this end, we introduce an invariant, compact gesture representation which facilitates robust and efficient online gesture recognition even for very small training data sets. The proposed two-stage recognition approach permits reliable gesture classification as well as convenient parameter extraction in the presence of typical disturbances affecting TSSs attached to manipulator links. Our experimental results demonstrate the remarkable recognition performance of the proposed approach using a set of 16 gestures and data of up to 31 subjects.

Physical human robot interaction (pHRI) in industrial manufacturing environments requires reliable environment perception capabilities usually employing multiple sensor modalities and particularly tactile sensor matrices to facilitate the management of human robot contacts. However, their high cost still impedes the large-scale integration of pHRI in manufacturing environments. To enable the development of low-cost tactile sensor matrices for pHRI applications, we examine five cost-effective piezoresistive materials w.r.t. their suitability for tactile sensor matrices. Since hysteresis and drift can severely deteriorate the performance of a tactile sensor, particular attention is paid to these properties. For the apparently best material and a very low-cost alternative, the input-output behavior is modeled using a machine learning approach. The differences in the input-output behavior of the individual taxels are comparatively low - thus significantly simplifying parameter identification and calibration.

The lack of efficient and intuitive programming paradigms for industrial manipulators stands in the way of increasing the degree of automation in various industrial manufacturing environments. We approach this lack by exploring tactile surface sensors (TSS) enclosing the links of a manipulator as a gesture input device for intuitive robot programming. First, our low-cost tactile surface sensor is briefly presented. Subsequently, a preliminary gesture set is introduced and feature-based approaches for gesture recognition are addressed. Furthermore, the extraction of relevant parameters from the gestures as well as the integration of gesture input into our skill-based robot control architecture are outlined. Quantitative performance results based on a user study with 12 subjects and a set of 7 gestures show that even with a comparatively low spatial resolution of the sensor (20mm) average accuracies of over 90% can be obtained.

Applying Programming by demonstration, especially kinesthetic teaching, to industrial assembly tasks, offers the opportunity to measure agent forces, torques and the end-effector pose during an assembly operation. The main contribution of this paper is a hierarchical decomposition as a formal and abstract representation of assembly operations. In contrast to other approaches, the presented decomposition is based on task specific knowledge and experience of a domain expert. Hence, each state of the decomposition and also the conditions for state transition are human-understandable. According to the task specific knowledge of a domain expert, structure and depth of the decomposition can vary for different sections of an assembly task. An example of a hierarchical decomposed assembly task and particularly the formal representation of a problem which occurred during task execution is given. This representation also describes how to recover form the mentioned problem. The very same decomposition is also used for online fault detection and prediction.

Complex handling and assembly tasks can be gradually decomposed into simpler sub-tasks until a level of elementary 'primitive' tasks is reached, which can be expressed generically by so-called Manipulation Primitives. Manipulation Primitives can hence be utilized as the foundation of a unified paradigm to specify and execute complex sensor-based robot tasks. By embedding these Manipulation Primitives into a fully hierarchical structure, the specification and reuse of common sub-tasks can be simplified immediately. However, without appropriate synchronization mechanisms the user-friendly and effective specification of tasks for multi-robot systems based on Manipulation Primitives still remains an issue. The underlying place-transition net formalism allows for synchronization by including the notions of forks and joins into the net but this approach is neither user-friendly nor does it allow for immediate support of more sophisticated synchronization mechanisms such as range synchronization. To eliminate this drawback of the Manipulation Primitive Concept, this contribution proposes powerful but at the same time easy-to-use synchronization primitives and introduces a revised Manipulation Primitive paradigm, which takes full advantage of the developed primitives. The advantages of the proposed concepts regarding the specification of tasks for industrial multi-robot systems are illustrated with several examples.

During the last decade(s), few new application fields for industrial robotics have developed although exploiting the state-of-the-art in (industrial) robotics research may unlock a plethora of new applications in industrial manufacturing and other areas. Ongoing structural changes affecting the population of Japan and various European countries, esp. population ageing and accompanying health issues, increase the need for assistive robotics systems in human-centered production environments and require the automation of potentially complex, but unergonomic tasks. Due to various issues - addressed in the paper - current robot control architectures and programming paradigms do not meet these needs. Fortunately, technology leaders in industrial robotics are currently striving to innovate and address these needs. Academia-industry collaboration can successfully accelerate these innovation processes and may thus have significant impact on commercial products and solutions. After reviewing the above mentioned needs and identifying crucial issues in current robot control architectures and programming paradigms, we present four case-studies which contributed to solving some of these issues together with industrial partners. Several factors contributing to the success and failure of academia industry partnerships related to our own project experience are discussed.

Grasping objects is a well-known problem in robotics. If the objects to be grasped are known, usually they are to be placed at a desired position in a desired orientation. Therefore, the object pose w.r.t the gripper has to be known before placing the object. In this paper we propose a simple and efficient, yet robust approach to this challenge, which can (nearly) eliminate dead times of the employed manipulator - hence speeding up the process significantly. Our approach is based on the observation that the problem of finding a pose at which the object can be grasped and the problem of computing the pose of the object w.r.t. the gripper can be solved separately at different stages. Special attention is paid to the popular bin-picking problem where this strategy shows its full potential. To reduce the overall cycle time, we estimate the grasp pose after the object has been grasped. Our estimation technique relies on the inertial parameters of the object - instead of visual features - which enables us to easily incorporate pose changes due to grasping. Experiments show that our approach is fast and accurate. Furthermore, it can be implemented easily and adapted to diverse pick and place tasks with arbitrary objects.

Manipulation Primitives are state-of-the-art for describing and performing complex manipulation tasks - especially in industrial applications. They combine easy task specification and execution which are key components for the success of this paradigm. However, there are some flaws when describing a complex task by decomposing it into Manipulation Primitives. For instance, the relations between the individual Manipulation Primitives cannot be captured by the original definition but have to be defined in additional glue code. This contribution introduces an extended definition of Manipulation Primitives to describe manipulation tasks as hierarchical structures using Manipulation Primitives at each level of the hierarchy. Additionally, a concept for a generic control architecture is presented which is capable of meeting the requirements of the extended Manipulation Primitive concept and which can be easily plugged“ together using modular code blocks.”

Rotary encoders and resolvers are by far the most common sensors to measure joint angles in articulated robots. Since various control approaches require angular rates as well, resolvers and encoders are also employed to derive angular rate signals. Due to the involved differentiation operation, however, quantization noise may be augmented significantly. Advanced filtering approaches can only partially overcome this drawback. Therefore, direct measurement of angular rates is desirable. Due to advancements in manufacturing technology and pushed by applications in entertainment devices, MEMS gyroscopes have become an attractive alternative for angular rate measurement. Unfortunately, they are affected by bias and other non-negligible disturbances, which may be a serious problem if consistent joint angle and angular rate measurements are required. The proposed approach fuses encoder and angular rate signals to mitigate their major drawbacks - bias and quantization errors - by exploiting their individual strong points. Bias in the angular rate signal is eliminated by analyzing the deviation between the integrated angular rate signal and the encoder signal. Quantization errors in the encoder signal are reduced by a so-called complementary filter which blends the integrated angular rate output with the encoder signal. Apart from a description of the approach and a theoretical analysis of its characteristics, experimental results demonstrating the effectiveness of the approach in closed-loop control of articulated robots are presented.

Electric motors clearly constitute the most common drive principle in robotics and mechatronics. Smart materials, however, offer considerably higher power-to-mass ratios than electric motors. If mechanical energy instead of electrical energy can be distributed through a system, highly dynamic and efficient torque transmission elements based on smart materials, e.g. piezoceramics, can be used to transmit torque from an input to an output element. Just like electric motors, they can thus provide position, velocity, and force-torque control of the output element. This paper introduces machine components, called adaptronic couplers, which can transmit variable torques highly dynamically from an input element to an output element employing static and/or dynamic friction. In the long run, systems (e.g. robots) based on these machine components are envisaged to compete with systems based on classic drive principles - especially electric motors - w.r.t. dynamics and power-to-mass-ratio. Apart from the concept itself, this paper addresses different control approaches and discusses their influence on energy consumption and wear. Moreover, various experimental results proving the basic concept are presented.

Rotary encoders are the most common sensors to measure angles in mechatronic and robotic components, e.g., servo motors or robot joints. Especially, if encoders are not mounted on the motor shaft but on the output side of geared motors, high encoder resolutions are required. Resolution requirements may increase further if velocities are to be derived from the encoder signals, e.g., for motion control purposes. To avoid noise amplification problems when estimating angular rates from encoder signals, angular velocity sensors may be employed instead. However, a significant drawback of angular rate sensors - particularly of cheap MEMS gyroscopes - is their drift.

In this video we show our current prototype for robot assisted endoscopy. The system requires only few and simple instructions from the surgeon, in order to guide the endoscope in an intelligent, autonomous, and safe way: The surgeon tells what to do and the robot decides how to carry out the task by choosing the best manipulation primitive in every control cycle. Several sensors are integrated into the decision process: The endoscope camera, a stereo camera system, a force/torque sensor, a biomechanical model based on CT data and statistical knowledge, a position and velocity sensor for the manipulator, and interface devices like a foot switch. The executed manipulation primitive is handled by a hybrid controller allowing to switch the control mode (e.g. trajectory following or force control) for each degree of freedom of the task frame individually.

This paper introduces a parallelized variant of the Random Sample Matching (RANSAM) approach, which is a very time and memory efficient enhancement of the common Random Sample Consensus (RANSAC). RANSAM exploits the theory of the birthday attack whose mathematical background is known from cryptography. The RANSAM technique can be applied to various fields of application such as mobile robotics, computer vision, and medical robotics. Since standard computers feature multi-core processors nowadays, a considerable speedup can be obtained by distributing selected subtasks of RANSAM among the available cores. First of all this paper addresses the parallelization of the RANSAM approach. Several important characteristics are derived from a probabilistic point of view. Moreover, we apply a fuzzy criterion to compute the matching quality, which is an important step towards real-time capability. The algorithm has been implemented for Windows and for the QNX RTOS. In an experimental section the performance of both implementations is compared and our theoretical results are validated.

In bilateral teleoperation, the operator experiences forces and torques applied to the slave manipulator. These forces and torques, however, consist of two components: on the one hand, forces and torques due to contacts with the environment, and on the other hand, non-contact forces, i.e., inertial forces, centrifugal forces, Coriolis forces, and associated torques. For several reasons, eliminating these non-contact forces and torques from the force-torque measurements of the slave or scaling them can be advantageous. For instance, in highly-dynamic teleoperation tasks, these forces and torques may contribute to operator fatigue or hamper the detection of contacts with the environment. This paper briefly reviews the estimation of inertial parameters of the slave load, e.g., an end-effector or a gripper. Subsequently, a method for eliminating the non-contact forces and torques from the measurements of a wrist-mounted force-torque sensor or scaling them is presented. After a brief overview of our teleoperation system, experimental results are presented which demonstrate the effectiveness of our approach.

Teleoperation has a long history in the robotics community and numerous bilateral teleoperation systems employing manipulators have been proposed in the literature. On the one hand, systems have been designed which employ commercial hardware and hence generally suffer from low update rates and high delays due to restrictions of commercial manipulator controllers and haptic device controllers. On the other hand, bilateral teleoperation systems designed by research institutions often provide only few degrees of freedom. Our 6DoF bilateral teleoperation system, however, combines the amenities of commercial hardware with a high performance distributed control architecture which enables us to achieve update rates of more than 2 kHz and delays in the range of only 100 ¿s. This paper focuses on the architecture of our system and demonstrates how to achieve this performance using commercial hardware. Moreover, we show why update rates of more than 1 kHz are essential for certain teleoperation tasks. Especially with high approach velocities and stiff environments, high update rates and low delays are key requirements for stability and thus for realistic haptic perception. We present experimental results demonstrating the influence of the update rate on system stability. These results not only highlight the benefits of high update rates but also give hints on how to estimate the update rate necessary to achieve stable teleoperation for a given environment stiffness.

The potential of six-axis acceleration sensors in the field of robotic manipulation applications is quite high and most of it has not been used yet - neither in theoretic literature nor in research experiments. When considering six-joint industrial manipulators with six-axis force/torque and six-axis acceleration sensing, many new possibilities arise: all ten inertial parameters of any object can be identified and objects can be recognized based on these parameters, position control behavior can be improved; non-contact forces can be extracted and force control performance can be improved; visual-servoing methods can use acceleration signals to become more robust. The authors made numerous experiments in the mentioned fields and recognized major weaknesses during the realization of prototypic research setups with six-axis acceleration sensors. These problems regard sensor drift, undesired sensor-internal dependencies as the influence of any distal sensor part, noise, and undesired crosstalk behavior. In order to benefit from acceleration signals, it is important to clearly overcome these problems. This paper analyzes typical systematic errors, characterizes them, and suggests important solution methods for a successful usage of acceleration information.

Regarding manipulators with wrist-mounted force/torque sensors a major issue is the high execution time of force-guided and force-guarded motions compared to purely position-controlled tasks. An important factor that aggravates the reduction of the execution time is the influence of non-contact forces, e.g. inertial forces, centrifugal forces, Coriolis forces, and associated torques, which are exerted onto the sensor by a load attached to it. Considering force-guided or force-guarded motions, these non-contact forces may significantly deteriorate contact detection and force control performance when executing dynamic movements. In addition to these disturbance forces/torques, resets of the force/torque sensor consume execution time. This paper presents an approach to eliminating all non-contact forces and associated torques from force/torque sensor measurements thus enabling pure contact force control. Apart from facilitating pure contact force control, the presented approach renders resets of the force/torque sensor unnecessary. To achieve this aim, the ten inertial parameters (mass, coordinates of the center of mass, and the elements of the inertia matrix) of the load attached to the sensor as well as the force/torque sensor offsets are estimated on-line employing a variant of the recursive instrumental variables method. These parameters are used to calculate the non- contact forces/torques acting upon the sensor. The current non-contact forces/torques and sensor offsets are subtracted from the force/torque measurements thus yielding the contact forces/torques. Experimental results show that both contact detection and force control performance are improved significantly by this approach.

The estimation of the ten inertial parameters of rigid loads, which are attached to manipulators, may benefit several robotics applications, e.g.: force control, object recognition, and pose estimation. These applications require sufficiently accurate, robust, and fast estimation of the inertial parameters. Existing approaches, however, do not allow for robust on-line estimation, since they use standard batch least-squares techniques, which ignore noise in the data matrix. The proposed approach, however, estimates the inertial parameters on-line and very fast (approx. 1.5s), while explicitly considering noise in the data matrix by a total least-squares approach. Apart from estimation equations and estimation approaches, the design of estimation trajectories is addressed in this paper. The performance of the proposed estimation approach is compared with the recursive ordinary least-squares (RLS) and the recursive instrumental variables (RIV) method. Experimental results clearly recommend the proposed recursive total least-squares approach (RTLS).

This paper proposes an object recognition and gripping pose estimation approach based on on-line estimation of the complete set of inertial parameters, i.e. the mass, the coordinates of the center of mass, and the elements of the inertia matrix, of an object gripped by or attached to a manipulator. A multi-sensor fusion approach combining 6D force/torque, 6D acceleration, 3D angular velocity, and joint angle data to estimate these parameters is presented. In order to facilitate practical implementation, approaches to handling force/torque sensor offsets and to compensating the forces/torques caused by the distal mounting plate of the force/torque sensor and the gripper are incorporated. Regarding the joint angle signals, preprocessing steps to derive the angular velocity, linear acceleration and angular acceleration vector w.r.t. the sensor frame are addressed. The estimation of the complete set of inertial parameters employing the recursive instrumental variables (RIV) method is discussed. The extraction of features that are invariant w.r.t. translation and rotation, i.e. the mass and the principal moments of inertia, as well as a recognition approach based on the Kullback-Leibler divergence are presented. Experimental results show very low errors in the estimates of the inertial parameters, good pose estimation accuracy, and the viability of the recognition approach.

This paper focusses on sensor fusion in robotic manipulation: 6D force/torque signals and 6D acceleration signals are used to extract forces and torques caused by inertia. As result, only forces and torques established by environmental contact(s) remain. Beside an improvement of hybrid force/pose control behavior, an additional major benefit is that regular resetting/zeroing of force/torque sensors before free space/contact transitions can be omitted. All essential equations, transformations, and calculations that are required for this 6D fusion approach are derived. To highlight the meaning for practical implementations, numerous experiments with a six-joint Staeubli RX60 industrial manipulator are presented, and the achieved results are discussed