Conference Paper

Uniform regulation of a multi-section continuum manipulator

Clemson Univ., SC
DOI: 10.1109/ROBOT.2002.1014759 Conference: Robotics and Automation, 2002. Proceedings. ICRA '02. IEEE International Conference on, Volume: 2
Source: IEEE Xplore

ABSTRACT Continuum manipulators are robotic manipulators built using one continuous, elastic and highly deformable "backbone", instead of multiple rigid links and joints. In previous work (2000), we illuminated various kinematic and dynamic properties of continuum robots, but the question of controller design remained open. This paper presents a basic result for continuum robots that has long been known for rigid-link robots: a simple PD-plus-feedforward controller can exponentially regulate the position of a manipulator.

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    ABSTRACT: This paper focuses on a class of robot manipulators termed "continuum" robots - robots that exhibit behavior similar to tentacles, trunks, and snakes. In previous work, we studied details of the mechanical design, kinematics, path-planning and small-deflection dynamics for continuum robots such as the Clemson "tentacle manipulator". In this paper, we discuss the dynamics of a planar continuum backbone section, incorporating a large-deflection dynamic model. Based on these dynamics, we formulate a vibration-damping setpoint controller, and include experimental results to illustrate the efficacy of the proposed controller.
    IEEE/ASME Transactions on Mechatronics 07/2003; DOI:10.1109/TMECH.2003.812829
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    ABSTRACT: Continuum manipulators are robotic manipulators built using one continuous, elastic, and highly deformable "backbone" instead of multiple rigid links connected by joints. This paper extends a previous control result for planar continuum robots by proposing a new asymptotic convergence argument for a PD-plus-feedforward controller. The benefit of the asymptotic arguments is that the backbone bending stiffness can be adaptively updated by the controller if it is not known a priori.
    Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on; 11/2003
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    ABSTRACT: This dissertation describes the design and implementation of various nonlinear control strategies for robot manipulators whose dynamic or kinematic models are uncertain. Chapter 2 describes the development of an adaptive task-space tracking controller for robot manipulators with uncertainty in the kinematic and dynamic models. The controller is developed based on the unit quaternion representation so that singularities associated with the otherwise commonly used three parameter representations are avoided. Experimental results for a planar application of the Barrett whole arm manipulator (WAM) are provided to illustrate the performance of the developed adaptive controller. The controller developed in Chapter 2 requires the assumption that the manipulator models are linearly parameterizable. However there might be scenarios where the structure of the manipulator dynamic model itself is unknown due to difficulty in modeling. One such example is the continuum or hyper-redundant robot manipulator. These manipulators do not have rigid joints, hence, they are difficult to model and this leads to significant challenges in developing high-performance control algorithms. In Chapter 3, a joint level controller for continuum robots is described which utilizes a neural network feedforward component to compensate for dynamic uncertainties. Experimental results are provided to illustrate that the addition of the neural network feedforward component to the controller provides improved tracking performance. While Chapter’s 2 and 3 described two different joint controllers for robot manipulators, in Chapter 4 a controller is developed for the specific task of whole arm grasping using a kinematically redundant robot manipulator. The whole arm grasping control problem is broken down into two steps; first, a kinematic level path planner is designed which facilitates the encoding of both the end-effector position as well as the manipulators self-motion positioning information as a desired trajectory for the manipulator joints. Then, the controller described in Chapter 3, which provides asymptotic tracking of the encoded desired joint trajectory in the presence of dynamic uncertainties is utilized. Experimental results using the Barrett Whole Arm Manipulator are presented to demonstrate the validity of the approach.

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