Optimization-Driven Design of a Kinesthetic Haptic Interface With Human-Like Capabilities.

Developing manipulators for kinesthetic haptic interfaces is challenging due to a large number of design parameters. We propose a novel optimization-driven design approach taking into account the properties of the entire workspace of the human arm instead of a specific task. To achieve this, models of both the human arm and the haptic manipulator are derived and deployed in a suitable objective function, which simultaneously considers poses, velocities, accelerations, as well as displayed forces and torques.

The State Space Subdivision Filter for Estimation on SE(2).

The SE(2) domain can be used to describe the position and orientation of objects in planar scenarios and is inherently nonlinear due to the periodicity of the angle. We present a novel filter that involves splitting up the joint density into a (marginalized) density for the periodic part and a conditional density for the linear part. We subdivide the state space along the periodic dimension and describe each part of the state space using the parameters of a Gaussian and a grid value, which is the function value of the marginalized density for the periodic part at the center of the respective area.

Conservative Quantization of Covariance Matrices with Applications to Decentralized Information Fusion.

Information fusion in networked systems poses challenges with respect to both theory and implementation. Limited available bandwidth can become a bottleneck when high-dimensional estimates and associated error covariance matrices need to be transmitted. Compression of estimates and covariance matrices can endanger desirable properties like unbiasedness and may lead to unreliable fusion results.

Progressive von Mises-Fisher Filtering Using Isotropic Sample Sets for Nonlinear Hyperspherical Estimation.

In this work, we present a novel scheme for nonlinear hyperspherical estimation using the von Mises-Fisher distribution. Deterministic sample sets with an isotropic layout are exploited for the efficient and informative representation of the underlying distribution in a geometrically adaptive manner. The proposed deterministic sampling approach allows manually configurable sample sizes, considerably enhancing the filtering performance under strong nonlinearity.

Optimally Distributed Kalman Filtering with Data-Driven Communication.

For multisensor data fusion, distributed state estimation techniques that enable a local processing of sensor data are the means of choice in order to minimize storage and communication costs. In particular, a distributed implementation of the optimal Kalman filter has recently been developed. A significant disadvantage of this algorithm is that the fusion center needs access to each node so as to compute a consistent state estimate, which requires full communication each time an estimate is requested.

Efficient physics-based tracking of heart surface motion for beating heart surgery robotic systems.

Purpose: Tracking of beating heart motion in a robotic surgery system is required for complex cardiovascular interventions.

Methods: A heart surface motion tracking method is developed, including a stochastic physics-based heart surface model and an efficient reconstruction algorithm. The algorithm uses the constraints provided by the model that exploits the physical characteristics of the heart.

Nonlinear visual mapping model for 3-D visual tracking with uncalibrated eye-in-hand robotic system.

A new control scheme for uncalibrated robotic visual tracking problem is proposed that compromises the computational expenses of overall system with offline modeling and online control. A nonlinear visual mapping model for the uncalibrated hand-eye coordination is first proposed with an artificial neural network implementation. An online visual tracking controller is then developed together with a real-time motion planner.