In physical Human-Robot Interaction, the basic problem of fast detection and safe robot reaction to unexpected collisions has been addressed successfully on advanced research robots that are torque controlled, possibly equipped with joint torque sensors, and for which an accurate dynamic model is available. In this paper, an end-user approach to collision detection and reaction is presented for an industrial manipulator having a closed control architecture and no additional sensors. The proposed detection and reaction schemes have minimal requirements: only the outer joint velocity reference to the robot manufacturer's controller is used, together with the available measurements of motor currents and joint positions. No a priori information on the robot dynamic model and existing low-level joint controllers is strictly needed. A suitable on-line processing of the motor currents allows to distinguish between accidental collisions and intended human-robot contacts, so as to switch the robot to a collaboration mode when needed. Two examples of reaction schemes for collaboration are presented, with the user pushing/pulling the robot at any point of its structure (e.g., for manual guidance) or with a compliant-like robot behavior in response to forces applied by the human. The actual performance of the methods is illustrated through experiments on a KUKA KR5 manipulator. © 2013 IEEE

Minimally invasive surgery assisted by robots is characterized by the restriction of feasible motions of the manipulator link constrained to move through the entry port to the patient's body. In particular, the link is only allowed to translate along its axis and rotate about the entry point. This requires constraining the manipulator motion with respect to a point known as Remote Center of Motion (RCM). The achievement of any surgical task inside the patient's body must take into account this constraint. In this paper we provide a new, general characterization of the RCM constraint useful for task control in the minimally invasive robotic surgery context. To show the effectiveness of our formalization, we consider first a visual task for a manipulator with 6 degrees of freedom holding an endoscopic camera and derive the kinematic control law allowing to achieve the visual task while satisfying the RCM constraint. An example of application of the proposed kinematic modeling to a motion planning problem for a 9 degrees of freedom manipulator with assigned path for the surgical tool is then proposed to illustrate the generality of the approach. © 2013 IEEE

Sit-to-stand (STS) transfers are a common human task which involves complex sensorimotor processes to control the highly nonlinear musculoskeletal system. In this paper, typical unassisted and assisted human STS transfers are formulated as optimal feedback control problem that finds a compromise between task end-point accuracy, human balance, energy consumption, smoothness of motion and control and takes further human biomechanical control constraints into account. Differential dynamic programming is employed, which allows taking the full, nonlinear human dynamics into consideration. The biomechanical dynamics of the human is modeled by a six link rigid body including leg, trunk and arm segments. Accuracy of the proposed modelling approach is evaluated for different human healthy and patient/elderly subjects by comparing simulations and experimentally collected data. Acceptable model accuracy is achieved with a generic set of constant weights that prioritize the different criteria. Finally, the proposed STS model is used to determine optimal assistive strategies suitable for either a person with specific body segment weakness or a more general weakness. These strategies are implemented on a robotic mobility assistant and are intensively evaluated by 33 elderlies, mostly not able to perform unassisted

We recommend you cite the published version. The publisher's URL is: http://dx.doi.org/10.1007/s12369-016-0353-z Refereed: YesThe final publication is available at Springer via http://dx.doi.org/10.1007/s12369?016?0353?z Disclaimer UWE has obtained warranties from all depositors as to their title in the material deposited and as to their right to deposit such material. UWE makes no representation or warranties of commercial utility, title, or fitness for a particular purpose or any other warranty, express or implied in respect of any material deposited. UWE makes no representation that the use of the materials will not infringe any patent, copyright, trademark or other property or proprietary rights. UWE accepts no liability for any infringement of intellectual property rights in any material deposited but will remove such material from public view pending investigation in the event of an allegation of any such infringement. PLEASE SCROLL DOWN FOR TEXT.Noname manuscript No. Abstract Mobility assistance robots (MARs) provide support to elderly or patients during walking. The design of a safe and intuitive assistance behavior is one of the major challenges in this context. We present an integrated approach for the context-specific, on-line adaptation of the assistance level of a rollator-type mobility assistance robot by gain-scheduling of low-level robot control parameters. A human-inspired decision-making model, the Drift-Diffusion Model, is introduced as the key principle to gain-schedule parameters and with this to adapt the provided robot assistance in order to achieve a human-like assistive behavior. The mobility assistance robot is designed to provide a) cognitive assistance to help the user following a desired path towards a predefined destination as well as b) sensorial assistance to avoid collisions with obstacles while allowing for an intentional approach of them. Further, the robot observes the user long-term performance and fatigue to adapt the overall level of c) physical assistance provided. For each type of assistance a decision-making problem is formulated that affects different low-level control parameters. The effectiveness of the proposed approach is demonstrated in technical validation experiments. Moreover, the proposed approach is evaluated in a user study with 35 elderly persons. Obtained results indicate that the proposed gain-scheduling technique incorporating ideas of human decision-making models shows a general high potential for the application in adaptive shared control of mobility assistance robots.

High accurate absolute robot positioning is a requirement, and still a challenge, in many applications, such as drilling in the aerospace industry. The accuracy is affected due to many sources of errors from robot model, tool calibration, sensor and product uncertainties. While model-based error compensation cannot reach the desired accuracy, sensor-based compensation appears as the practical solution to increase the robot positioning accuracy. A structured analysis of the error sources in robotic manufacturing processes can facilitate error identification and further compensation. This paper describes an error source breaking down approach for analyzing robotic manufacturing processes. Moreover, an external sensor-based compensation is proposed for error reduction and error identification. Comparison with a compliance model-based compensation is performed. The proposed approach is applied to a robotic drilling process for aircraft manufacturing, considered a general and real industrial application. Further validation through experimentation is performed. The validation revealed a clear improvement in robot positioning accuracy and the benefits of the proposed error source structure for analysis