In this paper, we present a method for planning the sensing features when the geometrical representation of the target object is known. The presented method is a synthesis form and can be used in several measurement applications in robotics. Sensing planning is an important issue when the measurement data is sparse, includes a lot of noise or there are tight time-requirements. The criteria for selecting the measurement locations and orientations is a posteriori error covariance matrix of the parameters to be estimated. The presented approach is verified by simulation tests in the case of work object location. The proposed method has been implemented into a commercial ConiferRob-robot software

Calibration of coordinate systems between cameras and robots, i.e. hand-eye-calibration, is of key importance in sensor based robotics. We present a method which is based on using a very simple, conic calibration object, and results in fast and simple calculations. It originates from the pose estimation algorithms and contributes to earlier methods robustness for the location of the calibration object and simplicity from the point of view of image processing. Our method is also based on the Bayesian model, thus it also provides estimates for the spatial uncertainties, in the form of covariances of the pose parameters. The algorithm has been tested by simulations and practical experiments.

A holonic multi-robot cell is a cell specifying a system based on autonomous and cooperative units called holons. This paper presents a method using Bayesian estimation to model uncertainties in a multi-robot cell. Using sensor fusion with several robots and combining all the information together gives new possibilities for planning the sensing operations, e.g. to minimize the size of locating measurements. If the robots and work object are accurately modelled (noise model and surface forms), an iterative planner can be used to plan a minimal task sequence for the treatment of the work object. Further analysis of the remaining uncertainties is based on observing the direction of the eigenvectors and the respective eigenvalues of the error covariance matrix.