Martin Buss

Dextrous hand grasping force optimization

A key goal in dextrous robotic hand grasping is to balance external forces and at the same time achieve grasp stability and minimum grasping energy by choosing an appropriate set of internal grasping forces. Since it appears that there is no direct algebraic optimization approach, a recursive optimization, which is adaptive for application in a dynamic environment, is required. One key observation in this paper is that friction force limit constraints and force balancing constraints are equivalent to the positive definiteness of a certain matrix subject to linear constraints. Based on this observation, we formulate the task of grasping force optimization as an optimization problem on the smooth manifold of linearly constrained positive definite matrices for which there are known globally exponentially convergent solutions via gradient flows. There are a number of versions depending on the Riemannian metric chosen, each with its advantages, Schemes involving second derivative information for quadratic convergence are also studied. Several forms of constrained gradient flows are developed for point contact and soft-finger contact friction models. The physical meaning of the cost index used for the gradient flows is discussed in the context of grasping force optimization. A discretized version for real-time applicability is presented. Numerical examples demonstrate the simplicity, the good numerical properties, and optimality of the approach.

Compliant actuation of rehabilitation robots

This article discusses the pros and cons of compliant actuation for rehabilitation robots on the example of LOPES, focusing on the cons. After illustrating the bandwidth limitations, a new result has been derived: if stability in terms of passivity of the haptic device is desired, the renderable stiffness is bounded by the stiffness of the SEAs elastic component. In practical experiments with the VMC, the aforementioned limitations affected the control performance. Desired gait modifications were not tracked exactly, because the subjects were able to deviate from the prescribed pattern even in the stiffest possible configuration. Despite the limitations, the practical experiments also demonstrated the general effectiveness of the realization. Manipulation of selected gait parameters is possible, whereby other parameters are left unaffected. This high selectivity is made possible by the low level of undesired interaction torques, which is achieved by elastic decoupling of motor mass and a lightweight exoskeleton. The discrepancy between theoretical bounds and rendered stiffness indicated that healthy subjects might represent a stabilizing component of the coupled system, which could be different for patients. In light of the theoretical stability analysis and with the focus on patients, the LOPES actuation was slightly modified. The robot was equipped with stiffer springs to obtain sufficient stiffness and to ensure stability without relying on stabilizing effects of the human. For this application, the disadvantages of compliant actuation can thus be tolerated or dealt with, and they are small compared with the advantages. Given that a rehabilitation robot, in the first place, is supposed to imitate therapist action, the limitations of bandwidth and stiffness do not pose severe problems. In contrast, safety and backdrivability are highly relevant, and they can be ensured easier with a compliant actuator. Therefore, we conclude that compliant actuation and a lightweight exoskeleton provide effective means to accomplish the desired AAN behavior of a rehabilitation robot. The next step is to evaluate the robot behavior, control performance, and therapeutic effectiveness in patient studies.

Bilateral teleoperation over the internet: the time varying delay problem

This paper addresses the problem of time-varying communication delay in force reflecting bilateral teleoperation. The problem is motivated by the increasing use of the Internet as a communication medium where the time delay is variable depending on factors such as congestion, bandwidth, or distance. The well-known scattering formalism introduced in 111 preserves passivity of the communication channel in general only for constant transmission delay. We demonstrate how to recover both passivity and tracking performance using a modified control architecture that incorporates timevarying gains into the scattering transformatioil and feedforward position control. Experimental results using a single-degree of freedom master/slave system are presented

Comparison of surface normal estimation methods for range sensing applications

As mobile robotics is gradually moving towards a level of semantic environment understanding, robust 3D object recognition plays an increasingly important role. One of the most crucial prerequisites for object recognition is a set of fast algorithms for geometry segmentation and extraction, which in turn rely on surface normal vectors as a fundamental feature. Although there exists a plethora of different approaches for estimating normal vectors from 3D point clouds, it is largely unclear which methods are preferable for online processing on a mobile robot. This paper presents a detailed analysis and comparison of existing methods for surface normal estimation with a special emphasis on the trade-off between quality and speed. The study sheds light on the computational complexity as well as the qualitative differences between methods and provides guidelines on choosing the ‘right’ algorithm for the robotics practitioner. The robustness of the methods with respect to noise and neighborhood size is analyzed. All algorithms are benchmarked with simulated as well as real 3D laser data obtained from a mobile robot.

Multiclass Common Spatial Patterns and Information Theoretic Feature Extraction

We address two shortcomings of the common spatial patterns (CSP) algorithm for spatial filtering in the context of brain-computer interfaces (BCIs) based on electroencephalography/magnetoencephalography (EEG/MEG): First, the question of optimality of CSP in terms of the minimal achievable classification error remains unsolved. Second, CSP has been initially proposed for two-class paradigms. Extensions to multiclass paradigms have been suggested, but are based on heuristics. We address these shortcomings in the framework of information theoretic feature extraction (ITFE). We show that for two-class paradigms, CSP maximizes an approximation of mutual information of extracted EEG/MEG components and class labels. This establishes a link between CSP and the minimal classification error. For multiclass paradigms, we point out that CSP by joint approximate diagonalization (JAD) is equivalent to independent component analysis (ICA), and provide a method to choose those independent components (ICs) that approximately maximize mutual information of ICs and class labels. This eliminates the need for heuristics in multiclass CSP, and allows incorporating prior class probabilities. The proposed method is applied to the dataset IIIa of the third BCI competition, and is shown to increase the mean classification accuracy by 23.4% in comparison to multiclass CSP.

Feature Extraction and Selection for Emotion Recognition from EEG

Emotion recognition from EEG signals allows the direct assessment of the “inner” state of a user, which is considered an important factor in human-machine-interaction. Many methods for feature extraction have been studied and the selection of both appropriate features and electrode locations is usually based on neuro-scientific findings. Their suitability for emotion recognition, however, has been tested using a small amount of distinct feature sets and on different, usually small data sets. A major limitation is that no systematic comparison of features exists. Therefore, we review feature extraction methods for emotion recognition from EEG based on 33 studies. An experiment is conducted comparing these features using machine learning techniques for feature selection on a self recorded data set. Results are presented with respect to performance of different feature selection methods, usage of selected feature types, and selection of electrode locations. Features selected by multivariate methods slightly outperform univariate methods. Advanced feature extraction techniques are found to have advantages over commonly used spectral power bands. Results also suggest preference to locations over parietal and centro-parietal lobes.

Perception-Based Data Reduction and Transmission of Haptic Data in Telepresence and Teleaction Systems

We present a novel approach for the transmission of haptic data in telepresence and teleaction systems. The goal of this work is to reduce the packet rate between an operator and a teleoperator without impairing the immersiveness of the system. Our approach exploits the properties of human haptic perception and is, more specifically, based on the concept of just noticeable differences. In our scheme, updates of the haptic amplitude values are signaled across the network only if the change of a haptic stimulus is detectable by the human operator. We investigate haptic data communication for a 1 degree-of-freedom (DoF) and a 3 DoF teleaction system. Our experimental results show that the presented approach is able to reduce the packet rate between the operator and teleoperator by up to 90% of the original rate without affecting the performance of the system.

Model-Based Probabilistic Collision Detection in Autonomous Driving

The safety of the planned paths of autonomous cars with respect to the movement of other traffic participants is considered. Therefore, the stochastic occupancy of the road by other vehicles is predicted. The prediction considers uncertainties originating from the measurements and the possible behaviors of other traffic participants. In addition, the interaction of traffic participants, as well as the limitation of driving maneuvers due to the road geometry, is considered. The result of the presented approach is the probability of a crash for a specific trajectory of the autonomous car. The presented approach is efficient as most of the intensive computations are performed offline, which results in a lean online algorithm for real-time application.

Real-time 3D hand gesture interaction with a robot for understanding directions from humans

This paper implements a real-time hand gesture recognition algorithm based on the inexpensive Kinect sensor. The use of a depth sensor allows for complex 3D gestures where the system is robust to disturbing objects or persons in the background. A Haarlet-based hand gesture recognition system is implemented to detect hand gestures in any orientation, and more in particular pointing gestures while extracting the 3D pointing direction. The system is integrated on an interactive robot (based on ROS), allowing for real-time hand gesture interaction with the robot. Pointing gestures are translated into goals for the robot, telling him where to go. A demo scenario is presented where the robot looks for persons to interact with, asks for directions, and then detects a 3D pointing direction. The robot then explores his vicinity in the given direction and looks for a new person to interact with.

Reachability analysis of nonlinear systems with uncertain parameters using conservative linearization

Given an initial set of a nonlinear system with uncertain parameters and inputs, the set of states that can possibly be reached is computed. The approach is based on local linearizations of the nonlinear system, while linearization errors are considered by Lagrange remainders. These errors are added as uncertain inputs, such that the reachable set of the locally linearized system encloses the one of the original system. The linearization error is controlled by splitting of reachable sets. Reachable sets are represented by zonotopes, allowing an efficient computation in relatively high-dimensional space.