Current Capabilities and Development Potential in Surgical Robotics

Commercial surgical robots have been in clinical use since the mid-1990s, supporting surgeons in various tasks. In the past decades, many systems emerged as research platforms, and a few entered the global market. This paper summarizes the currently available surgical systems and research directions in the broader field of surgical robotics. The widely deployed teleoperated manipulators aim to enhance human cognitive and physical skills and provide smart tools for surgeons, while image-guided robotics focus on surpassing human limitations by introducing automated targeting and treatment delivery methods. Both concepts are discussed based on prototypes and commercial systems. Through concrete examples the possible future development paths of surgical robots are illustrated. While research efforts are taking different approaches to improve the capacity of such systems, the aim of this survey is to assess their maturity from the commercialization point of view.

Über die Desorption von positiven und negativen Ionen durch starke elektrische Felder

An Hand von Beobachtungen über die Feldelektronenemission wird gezeigt, daß sich aus dünnen, auf Wolframeinkristallspitzen aufgedampften Kaliumchloridschichten, deren Schichtdicke größer ist, als dem Minimum der Elektronenaustrittsarbeit entspricht, durch positive Felder positive Kaliumionen, durch negative Felder negative Chlorionen „abreißen“ lassen; im ersten Fall erscheint die effektive Austrittsarbeit der Elektronen nach dem Abreißvorgang vergrößert, im zweiten Fall vermindert. Aus den dünnsten, vermutlich monomolekularen Kaliumchloridschichten, gelingt es dagegen mit den hier verwendeten Feldstärken nur, positive Ionen, aber keine negativen Ionen abzureißen; dies wird verständlich, wenn man beachtet, daß aus energetischen Gründen das Chlor in unmittelbarer Berührung mit der Wolframoberfläche nicht als Ion, sondern nur als neutrales Atom gebunden sein kann, weil seine Elektronenaffinität (3,79 eV) kleiner als die Austrittsarbeit des Wolframs (4,5 eV) ist.

Zur Deutung von früher untersuchten Abreißerscheinungen an gasbeladenen Wolframspitzen, die einer thermischen Nachbehandlung unterworfen waren, wird die Annahme vorgeschlagen, daß es sich bei den „Zentren“, die für die starken Intensitätsunterschiede im Feldelektronenemissionsbild solcher Schichten und für die Abreißerscheinungen verantwortlich sind, um überschüssige Metallatome handelt, die an bestimmten Stellen der Oberfläche der dünnen Oxyd- bzw. Carbidschicht eingebaut sind, mit der die gasbeladene Wolframoberfläche nach ihrer thermischen Nachbehandlung bedeckt ist.

Integration of fiber-optic sensor arrays into a multi-modal tactile sensor processing system for robotic end-effectors

With the increasing complexity of robotic missions and the development towards long-term autonomous systems, the need for multi-modal sensing of the environment increases. Until now, the use of tactile sensor systems has been mostly based on sensing one modality of forces in the robotic end-effector. The use of a multi-modal tactile sensory system is motivated, which combines static and dynamic force sensor arrays together with an absolute force measurement system. This publication is focused on the development of a compact sensor interface for a fiber-optic sensor array, as optic measurement principles tend to have a bulky interface. Mechanical, electrical and software approaches are combined to realize an integrated structure that provides decentralized data pre-processing of the tactile measurements. Local behaviors are implemented using this setup to show the effectiveness of this approach.

A Validation Process for Underwater Localization Algorithms

This paper describes the validation process of a localization algorithm for underwater vehicles. In order to develop new localization algorithms, it is essential to characterize them with regard to their accuracy, long-term stability and robustness to external sources of noise. This is only possible if a gold-standard reference localization (GSRL) is available against which any new localization algorithm (NLA) can be tested. This process requires a vehicle which carries all the required sensor and processing systems for both the GSRL and the NLA. This paper will show the necessity of such a validation process, briefly sketch the test vehicle and its capabilities, describe the challenges in computing the localizations of both the GSRL and the NLA simultaneously for comparison, and conclude with experimental data of real-world trials.

Distributed Computation in a Quadrupedal Robotic System

Today's and future space missions (will) have to deal with increasing requirements regarding autonomy and flexibility in the locomotor system. To cope with these requirements, a higher bandwidth for sensor information is needed. In this paper, a robotic system is presented that is equipped with artificial feet and a spine incorporating increased sensing capabilities for walking robots. In the proposed quadrupedal robotic system, the front and rear parts are connected via an actuated spinal structure with six degrees of freedom. In order to increase the robustness of the system's locomotion in terms of traction and stability, a foot-like structure equipped with various sensors has been developed. In terms of distributed local control, both structures are as self-contained as possible with regard to sensing, sensor preprocessing, control and communication. This allows the robot to respond rapidly to occurring events with only minor latency.

pySPACE-a signal processing and classification environment in Python

In neuroscience large amounts of data are recorded to provide insights into cerebral information processing and function. The successful extraction of the relevant signals becomes more and more challenging due to increasing complexities in acquisition techniques and questions addressed. Here, automated signal processing and machine learning tools can help to process the data, e.g., to separate signal and noise. With the presented software pySPACE (http://pyspace.github.io/pyspace), signal processing algorithms can be compared and applied automatically on time series data, either with the aim of finding a suitable preprocessing, or of training supervised algorithms to classify the data. pySPACE originally has been built to process multi-sensor windowed time series data, like event-related potentials from the electroencephalogram (EEG). The software provides automated data handling, distributed processing, modular build-up of signal processing chains and tools for visualization and performance evaluation. Included in the software are various algorithms like temporal and spatial filters, feature generation and selection, classification algorithms, and evaluation schemes. Further, interfaces to other signal processing tools are provided and, since pySPACE is a modular framework, it can be extended with new algorithms according to individual needs. In the presented work, the structural hierarchies are described. It is illustrated how users and developers can interface the software and execute offline and online modes. Configuration of pySPACE is realized with the YAML format, so that programming skills are not mandatory for usage. The concept of pySPACE is to have one comprehensive tool that can be used to perform complete signal processing and classification tasks. It further allows to define own algorithms, or to integrate and use already existing libraries.

Incremental learning of skill collections based on intrinsic motivation

Life-long learning of reusable, versatile skills is a key prerequisite for embodied agents that act in a complex, dynamic environment and are faced with different tasks over their lifetime. We address the question of how an agent can learn useful skills efficiently during a developmental period, i.e., when no task is imposed on him and no external reward signal is provided. Learning of skills in a developmental period needs to be incremental and self-motivated. We propose a new incremental, task-independent skill discovery approach that is suited for continuous domains. Furthermore, the agent learns specific skills based on intrinsic motivation mechanisms that determine on which skills learning is focused at a given point in time. We evaluate the approach in a reinforcement learning setup in two continuous domains with complex dynamics. We show that an intrinsically motivated, skill learning agent outperforms an agent which learns task solutions from scratch. Furthermore, we compare different intrinsic motivation mechanisms and how efficiently they make use of the agent's developmental period.