Flexible, lightweight manipulators offer some advantages in contrast to rigid arms, such as compact and lighter drives, energy efficiency, reduced masses and costs. This paper presents a novel approach for vibration damping of a multi-link flexible arm. The strain of the elastic arms is measured with Fiber-Bragg-Grating (FBG) sensors and provides the feedback signal to dampen their flexural dynamics. A dynamic model of a three link arm is derived that accounts for the rigid and flexural dynamics including gravity. The arm vibrations are damped by nonlinear strain feedback. The controller is general and robust and its design does not require a model of the flexural dynamics. In the context of closed loop vibration control FBG sensors offer a better signal to noise ratio compared to strain gauges, which allows a higher static gain in the feedback loop with more efficient dissipation of vibrational energy. The feasibility and effectiveness of the proposed vibration control scheme in conjunction with FBG sensors is verified and analyzed in simulations and confirmed in experiments with a flexible three link robot arm.

This paper presents a novel approach to large view visual servoing in the context of object manipulation. In many scenarios the features extracted in the reference pose are only perceivable across a limited region of the work space. The limited visibility of features necessitates the introduction of additional intermediate reference views of the object and requires path planning in view space. In our scheme the visual control is based on decoupled moments of SIFT-features, which are generic in the sense that the control operates with a dynamic set of feature correspondences rather than a static set of individual features. The additional freedom of dynamic feature sets enables flexible path planning in the image space and online selection of optimal reference views during servoing to the goal view. The time to convergence to the goal view is estimated by a neural network based on the residual feature error and the quality of the SIFT feature distribution. The transition among reference views occurs on the basis of this estimated cost which is evaluated online based on the current set of visible features. The dynamic switching scheme achieves robust and nearly time-optimal convergence of the visual control across the entire task space. The effectiveness and robustness of the scheme is confirmed in an evaluation in a virtual reality simulation and on a real robot arm with a eye-in-hand configuration.

Vision being the most important human sensor and feedback system is considered to play a prominent role in the future of robotics. Robust techniques for visual robot navigation are essential in service robotics tasks. This contribution presents a novel approach to large view visual servoing of a mobile robot with a pan-tilt camera. A predictive camera control scheme guarantees that the image features remain in the cameras field of view. In a demonstration the robot is manually guided through the environment and reference images of landmarks are taken at distinct waypoints. During autonomous navigation the robot later follows the demonstrated path solely based on the visual feedback provided by the landmarks. The camera gaze is controlled independent of the robots egomotion which enables the robot to track landmark features across larger distances and orientations. Therefore, fewer visual landmarks are required to describe a smooth path through the environment. In order to control the robot motion independent of the gaze the observed features are transformed from the image plane into a virtual camera plane. The rotational, lateral and longitudinal motion are controlled separately by selecting appropriate image features that decouple the rotational and translational velocity component. This property is particular useful for traversing confined indoor spaces such as corridors, in which landmarks are located lateral to the direction of robot motion

Robotic manipulation of daily-life objects is an essential requirement in service robotic applications. In that context image based visual servoing is a means to position the end-effector in order to manipulate objects of unknown pose. This contribution proposes a 6 DOF visual servoing scheme that relies on the pixel coordinates, scale and orientation of SIFT features. The control is based on geometric moments computed over an alterable set of redundant SIFT feature correspondences between the current and the reference view. The method is generic as it does not depend on a geometric object model but automatically extracts SIFT features from images of the object. The foundation of visual servoing on generic SIFT features renders the method robust with respect to loss of redundant features caused by occlusion or changes in view point. The moment based representation establishes an approximate one-to-one relationship between visual features and degrees of motion. This property is exploited in the design of a decoupled controller that demonstrates superior performance in terms of convergence and robustness compared with an inverse image Jacobian controller. Several experiments with a robotic arm equipped with a monocular eye-in-hand camera demonstrate that the approach is efficient and reliable.