Miniaturized robots become more and more common for handling and assembly of miniaturized products. This miniaturization usually refers to the actuators, the kinematics and the sensors, but also the control electronics and their computational resources are affected. Facing these restrictions, the present article discusses patterns of robot control software that can be transferred from architectures proposed previously to microcontroller platforms. It is proposed to organize the software components according to two aspects: timing context and organizational context. Results from a case study are reported, where the architecture was implemented for a parallel kinematic five-bar robot. This study concludes that the underlying patterns help to implement control software of miniaturized robots in handling and assembly efficiently, even if hardware resources are limited.

This article presents advances in optimal experiment design, which are intended to improve the parameter identification of nonlinear state space models. Instead of using a sequence of samples from one or just a few coherent sequences, the idea of identifying nonlinear dynamic models at distinct points in the state space is considered. In this way, the placement of the experiment points is fully flexible with respect to the set of reachable points. Also, a method for model-based generation of prediction errors is proposed, which is used to compute an a-priori estimate of the sample covariance of the prediction error. This covariance matrix may be used to approximate the Fisher information matrix a-priori. The availability of the Fisher matrix a-priori is a prerequisite for experiment optimization with respect to covariance in the parameter estimates. This work is driven by the problem of parameter identification of hydraulic models. There are methods for hydraulic systems regarding the estimation of parameters from experimental data, but the choice of experiments has not been treated adequately yet. A hydraulic servo system actuating a stewart platform serves as an illustrative example to which the methods above are applied.

Force guided assembly is attractive but the implementation is challenging when uncertainties are present. In these cases, contact models that guide the assembly process are attractive. This article elaborates the usage of static contact models, which map displacements to force-torque vectors of particular contact situations (force-torque map). This model, a map of discrete points, contains force and torque values that correspond to position errors. The inversion of this map, using forces and torques measured from an assembly attempt, yields correction movements in order to accomplish the assembly iteratively. A hypothesis stating the dependency of the model's quality on its injectivity is investigated. This aspect is studied thoroughly in so-called redundancy maps, which reveal regions of considerable ambiguity of the model. Experimental results are presented, which validate the hypothesis about the dependency of the convergence of the assembly process on the ambiguity of the initial position. In addition to the peg-in-hole problem, which has become a standard scenario to validate force guided assembly, the scope of this article also covers force guided assembly of more complex parts. Here, the analysis gives evidence to believe that it is unlikely that the implementation convergences acceptably, which is validated by experimental results.

This article aderesses aspects of the a-priori computation of the Fisher information matrix. The aim of the type of Fisher matrix discussed is the experiment design for parameter estimation of nonlinear state space models. A definition for the sampling of experiments is stated, which is convenient for software implementation. Both, measurement samples that belong to a coherent sequence and samples from independent experiments, may be stored together in one single data structure. Since the covariance matrix of the prediction error is required in the Fisher matrix, a method for its a-priori prediction is proposed. A servo-hydraulic positioning system serves as a validation example. The comparison of the a-priori and a-posteriori Fisher matrices of this example show that the method proposed is suitable to provide an approximation of an optimality criterion for experiment design.