Predictive tracking with improved motion models for optical belt sorting

Optical belt sorters are a versatile means to sort bulk materials. In previous work, we presented a novel design of an optical belt sorter, which includes an area scan camera instead of a line scan camera. Line scan cameras, which are well-established in optical belt sorting, only allow for a single observation of each particle. Using multitarget tracking, the data of the area scan camera can be used to derive a part of the trajectory of each particle. The knowledge of the trajectories can be used to generate accurate predictions as to when and where each particle passes the separation mechanism. Accurate predictions are key to achieve high quality sorting results. The accuracy of the trajectories and the predictions heavily depends on the motion model used. In an evaluation based on a simulation that provides us with ground truth trajectories, we previously identified a bias in the temporal component of the prediction. In this paper, we analyze the simulation-based ground truth data of the motion of different bulk materials and derive models specifically tailored to the generation of accurate predictions for particles traveling on a conveyor belt. The derived models are evaluated using simulation data involving three different bulk materials. The evaluation shows that the constant velocity model and constant acceleration model can be outperformed by utilizing the similarities in the motion behavior of particles of the same type

The Role of Roles: Physical Cooperation between Humans and Robots

Since the strict separation of working spaces of humans and robots has experienced a softening due to recent robotics research achievements, close interaction of humans and robots comes rapidly into reach. In this context, physical human–robot interaction raises a number of questions regarding a desired intuitive robot behavior. The continuous bilateral information and energy exchange requires an appropriate continuous robot feedback. Investigating a cooperative manipulation task, the desired behavior is a combination of an urge to fulfill the task, a smooth instant reactive behavior to human force inputs and an assignment of the task effort to the cooperating agents. In this paper, a formal analysis of human–robot cooperative load transport is presented. Three different possibilities for the assignment of task effort are proposed. Two proposed dynamic role exchange mechanisms adjust the robot’s urge to complete the task based on the human feedback. For comparison, a static role allocation strategy not relying on the human agreement feedback is investigated as well. All three role allocation mechanisms are evaluated in a user study that involves large-scale kinesthetic interaction and full-body human motion. Results show tradeoffs between subjective and objective performance measures stating a clear objective advantage of the proposed dynamic role allocation scheme

Moment-Based Prediction Step for Nonlinear Discrete-Time Dynamic Systems Using Exponential Densities

In this paper, an efficient approach for a momentbased Bayesian prediction step for both linear and nonlinear discrete-time dynamic systems using exponential densities with polynomial exponents is proposed. The exact solution of the prediction step is approximated by an exponential density which minimizes the Kullback-Leibler distance. Compared to other approaches, the user of this procedure can specify the approximation quality by controlling the deviation between the moments of the exact and the approximated solution. Furthermore, this algorithm can also be used for the adaptation of the order of the exponential densities either to improve the approximation quality or to reduce the computational effort

A Model-Based Framework for Optimal Measurements in Machine Tool Calibration

Calibration is the procedure of quantifying mechanical deficiencies of machines and compensating them by appropriate adjustment. This paper introduces a modelbased measurement framework for improving calibration procedures of machine tools. The goal is to precisely estimate the mechanical deficiencies based on a minimal number of measurements. For that purpose, the mechanical deficiencies of linear and rotary joints are modeled using splines. Uncertainties of the deficiency model are formulated stochastically, which allows incorporation of imprecise measurement data and prediction of optimal measurement parameters. We derive a method for optimally estimating a set of splines, i.e., joint errors, based on a set of measurements and for predicting the optimal joint configuration for new measurements

Closed-Form Range-Based Posture Estimation Based on Decoupling Translation and Orientation

For estimating the posture, i.e., position and orientation, of an extended target based on range measurements, a new closed-form solution is proposed, which is based on decoupling position and orientation. For decoupling, any procedure for range-based localization of point targets, i.e., for mere position estimation, can be used. The new solution is suboptimal, but nevertheless provides good accuracy and is very practical from an application point of view. 1