It is advantageous to use an independently mobile camera in visually servoed disassembly operations since its degrees of freedom can be used to bring the camera into an optimal view-pose. Apart from the vision-based closed-loop control that guides the disassembly robot, a second vision-based closed-loop control can be set up which upgrades the camera from a passive to an active sensor and optimizes the view-pose according to changes in the scene domain. The objective of this contribution is to present an approach to model-based pose initialization and pose refinement steps of an active camera which is able to simultaneously observe and track several objects moving to some extent independently from each other. In both steps, constraints regarding object occlusions, the limited field-of-view, and the uncertainty of pose estimation are exploited to determine an optimal camera pose. The feasibility of this approach to model-based pose initialization is experimentally validated and documented for the case of the manipulation of a shock absorber lid.

The disassembly of engines of used cars based on visually servoed manipulators requires a state-of-the-art vision system which is able to evaluate images of dirty, partly occluded or possibly rearranged quasi- or non-polyhedral parts in real-time. As the pose of a stationary rigid part has, in general, six degrees of freedom, at least six degrees of freedom of a manipulator have to be visually controlled. Two pose estimators relying on different types of image features are discussed within a common framework. A position-based solution to the visually servoed manipulation of non-polyhedrals is presented that extends to real time and meets all given requirements. Experiments with the manipulation of a battery, which is partly occluded by wires, show the feasibility of the proposed approach.

Presents results of a model-based approach to visual tracking and pose estimation for a moving polyhedral tool in position-based visual servoing. This enables the control of a robot in look-and-move mode to achieve six degree of freedom goal configurations. Robust solutions of the correspondence problem-known as "matching" in the static case and "tracking" in the dynamic one-are crucial to the feasibility of such an approach in real-world environments. The object's motion along an arbitrary trajectory in space is tracked using visual pose estimates through consecutive images. Subsequent positions are predicted from robot joint angle measurements. To deal with inaccurate models and to relax calibration requirements, adaptive online calibration of the kinematic chain is proposed. The kinematic predictions enable unambiguous feature matching by a pessimistic algorithm. The performance of the suggested algorithms and the robustness of the proposed system are evaluated on real image sequences of a moving gripper. The results fulfill the requirements of visual servoing, and the computational demands are sufficiently low to allow for real-time implementation.

This contribution presents a position-based approach to visual servoing that allows the manipulation of quasi- or nonpolyhedral objects in a complex scene, namely the engine compartment of a used car. During a manipulation, the moving disassembly robot with its end-effector-mounted tool as well as the-possibly moving-object are observed by an independently mobile, binocular camera configuration. A simple proportional control law is used to guide the robot to its manipulation position. Vision-based tracking as implemented here tolerates mutual occlusions of tool and object. Moreover, it does not require additional feature configurations such as white circular patterns on homogeneous black background to reliably pursue tool and object. We demonstrate simultaneous movements of tool, object and independently mobile camera configuration during 6-DOF closed-loop control.

In order to automatically run a dismantling task, first a workpiece has to be recognized and its pose has to be estimated. This step is solved by a model-based recognition process using the information from three stationary cameras. Our workpiece model could be extracted from CAD-data and consists of planar patches and circular features, where the circular features are suitable to model parts of connection elements like screws. Using the initial estimation of the workpiece pose we control the robot by evaluating the image sequence given by a camera mounted on the robot hand. We use a multi-level 3D tracking approach to continuously estimate the workpiece pose using an iterative extended Kalman-filter process and integrate velocity estimation based on optical flow. To provide enough computing power for real-time image processing, we combine our dedicated image processing hardware with a heterogeneous parallel computer. Our approach has been tested in numerous experiments on loosening screws with a powered screwdriver and dismantling a workpiece consisting of two blocks of different sizes connected by two screws.