Unscented Orientation Estimation Based on the Bingham Distribution

Orientation estimation for 3D objects is a common problem that is usually tackled with traditional nonlinear filtering techniques such as the extended Kalman filter (EKF) or the unscented Kalman filter (UKF). Most of these techniques assume Gaussian distributions to account for system noise and uncertain measurements. This distributional assumption does not consider the periodic nature of pose and orientation uncertainty. We propose a filter that considers the periodicity of the orientation estimation problem in its distributional assumption. This is achieved by making use of the Bingham distribution, which is defined on the hypersphere and thus inherently more suitable to periodic problems. Furthermore, handling of non-trivial system functions is done using deterministic sampling in an efficient way. A deterministic sampling scheme reminiscent of the UKF is proposed for the nonlinear manifold of orientations. It is the first deterministic sampling scheme that truly reflects the nonlinear manifold of the orientation

05381 Abstracts Collection -- Form and Content in Sensor Networks

From 18.09.05 to 23.09.05, the Dagstuhl Seminar 05381 ``Form and Content in Sensor Networks\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Towards Intuitive Human-Robot Cooperation

Human-robot cooperation calls for the treatment of human-machine communication channels, especially if humanoid robots are involved. In this paper, we consider implicit nonverbal channels given by recognizing the partner\u27s intention and proactive execution of tasks. We propose a method that keeps the human in the loop and allows for the systematic reduction of uncertainty inherent in implicit cooperation. We present a benchmark scenario as well as preliminary implementation results

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