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In this paper a method is presented that allows an operator to hand-guide a robot along a predefined geometric path. This is a common use case in robot assisted surgery, which often has high demands on precision. In order to ensure the path accuracy of the robot, joint velocity and joint acceleration constraints are enforced to prevent undesired saturation effects of the actuators. Furthermore, necessary optimization steps are calculated in an offline phase and utilized during runtime to ensure realtime capabilities. The functionality of the method is evaluated using simulated sensor readings, controlling a kinematic model of the robot. While the focus is on surgical applications, the method can be useful in other domains as well, e.g. rehabilitation robotics or industrial applications

Observation of molecular dynamics is often biased by the optical very heterogeneous environment of cells and complex tissue. Here, we have designed an algorithm that facilitates molecular dynamic analyses within brain slices. We adjust fast astigmatism-based three-dimensional single-particle tracking techniques to depth-dependent optical aberrations induced by the refractive index mismatch so that they are applicable to complex samples. In contrast to existing techniques, our online calibration method determines the aberration directly from the acquired two-dimensional image stream by exploiting the inherent particle movement and the redundancy introduced by the astigmatism. The method improves the positioning by reducing the systematic errors introduced by the aberrations, and allows correct derivation of the cellular morphology and molecular diffusion parameters in three dimensions independently of the imaging depth. No additional experimental effort for the user is required. Our method will be useful for many imaging configurations, which allow imaging in deep cellular structures.

In this work, preparations for the nonparametric calibration of a 7 DOF light-weight robot using machine learning techniques are presented. The approach was developed to satisfy the requirements on absolute accuracy for robot-assisted surgery. With the kinematic and non-kinematic properties in mind, we showed that a decomposition of the robot's kinematic chain can drastically reduce the number of necessary samples for a sophisticated training set. Thus, the data acquisition can be accomplished in a feasible time frame. Furthermore, we cope with the problem of data registration between the robot's internal model and the external measurements. We can show that by carefully choosing the split point for the decomposition, errors caused by the dependency between sub-chains of the robot are small enough to yield satisfying results.