No abstract

Tendon driven continuum robots have attracted a significant amount of attention in the last decade. As a result several discrete and continuous modeling approaches have been explored in order to understand their behavior. The following article proposes an analytical model describing the influence of frictional effects between the tendon and its conduit in tendon driven continuum robotic manipulators. Existing constant curvature models ignore internal device friction completely and treat it as a negligible effect. This results in an assumption that the tendon tension is always constant along the length of the continuum manipulator. While this assumption is reasonable for small articulation angles, internal device friction can significantly change internal tension distribution deteriorating performance of constant curvature models at high articulation angles. The proposed model departs from the constant tension assumption and predicts a tension distribution along the tendon. From the tension distribution a curvature distribution is determined. It is observed that the proposed nonlinear friction model performs better than the existing linear elastic constant curvature model. Following this, a consequence of internal device friction: hysteresis effects are predicted. An algorithm that estimates tendon tension distribution given applied tendon tension history is proposed.

Many physical tasks such as pulling out a drawer or wiping a table can be modeled with geometric constraints. These geometric constraints are characterized by restrictions on kinematic trajectories and reaction wrenches (forces and moments) of objects under the influence of the constraint. This paper presents a method to infer geometric constraints involving unmodeled objects in human demonstrations using both kinematic and wrench measurements. Our approach takes a recording of a human demonstration and determines what constraints are present, when they occur, and their parameters (e.g. positions). By using both kinematic and wrench information, our methods are able to reliably identify a variety of constraint types, even if the constraints only exist for short durations within the demonstration. We present a systematic approach to fitting arbitrary scleronomic constraint models to kinematic and wrench measurements. Reaction forces are estimated from measurements by removing friction. Position, orientation, force, and moment error metrics are developed to provide systematic comparison between constraint models. By conducting a user study, we show that our methods can reliably identify constraints in realistic situations and confirm the value of including forces and moments in the model regression and selection process.

Human demonstrations are important in a range of robotics applications, and are created with a variety of input methods. However, the design space for these input methods has not been extensively studied. In this paper, focusing on demonstrations of hand-scale object manipulation tasks to robot arms with two-finger grippers, we identify distinct usage paradigms in robotics that utilize human-to-robot demonstrations, extract abstract features that form a design space for input methods, and characterize existing input methods as well as a novel input method that we introduce, the instrumented tongs. We detail the design specifications for our method and present a user study that compares it against three common input methods: free-hand manipulation, kinesthetic guidance, and teleoperation. Study results show that instrumented tongs provide high quality demonstrations and a positive experience for the demonstrator while offering good correspondence to the target robot.

No abstract