Penetration detection with intention recognition for cooperatively controlled robotic needle insertion

In lumbar puncture surgeries, force and position information throughout the insertion procedure is vital for needle tip localization, because it reflects different tissue properties. Especially in pediatric cases, the changes are always insignificant for surgeons to sense the crucial feeling of loss of resistance. In this study, a robot system is developed to tackle the major clinical difficulties. Four different control algorithms with intention recognition ability are applied on a novel lumbar puncture robot system for better human–robot cooperation. Specific penetration detection based on force and position derivatives captures the feeling of loss of resistance, which is deemed crucial for needle tip location. Kinematic and actuation modeling provides a clear description of the hardware setup. The control algorithm experiment compares the human–robot cooperation performance of proposed algorithms. The experiment also dictates the clear role of designed penetration detection criteria in capturing the penetration, improving the success rate, and ensuring operational safety.

Adaptive Motion Control Middleware for Teleoperation Based on Pose Tracking and Trajectory Planning

Concurrent with autonomous robots, teleoperation gains importance in industrial applications. This includes human–robot cooperation during complex or harmful operations and remote intervention. A key role in teleoperation is the ability to translate operator inputs to robot movements. Therefore, providing different motion control types is a decisive aspect due to the variety of tasks to be expected. For a wide range of use-cases, a high degree of interoperability to a variety of robot systems is required. In addition, the control input should support up-to-date Human Machine Interfaces. To address the existing challenges, we present a middleware for teleoperation of industrial robots, which is adaptive regarding motion control types. Thereby the middleware relies on an open-source, robot meta-operating system and a standardized communication. Evaluation is performed within defined tasks utilizing different articulated robots, whereby performance and determinacy are quantified. An implementation sample of the method is available on: https://github.com/FAU-FAPS/adaptive_motion_control.

How Socio-Technical Factors Can Undermine Expectations of Human-Robot Cooperation in Hospitals

This research analysed human–robot cooperation and interaction in the basement of a Danish hospital, where kitchen staff and porters conducted their daily routines in an environment shared with mobile service robots. The robots were installed to ease the everyday routines of kitchen staff and carry out physically demanding tasks, such as transporting heavy cargo between destinations in the hospital basement. The cooperation and interaction were studied through ethnographic inspired fieldwork and the results highlighted how robots affect the real-life environments into which they are gradually moving. The analysis revealed how the great human expectations of robots clashed with reality and identified three key elements that influence human–robot cooperation in hospitals: 1) environmental factors, 2) behavioural factors and 3) factors related to human reliance on robots. We emphasise the importance of considering socio-technical factors when deploying robots to cooperate with humans in hospital environments.

A novel human-robot interface based on soft skin sensor designed for the upper-limb exoskeleton

The upper-limb exoskeleton is capable of enhancing human arm strength beyond normal levels, whereas deriving the operator’s desired action straightforward turns out to be one of the significant difficulties facing human-robot interaction research. In the study, the human-robot interface was presented to regulate the exoskeleton tracking human elbow motion trajectory that employed the contact force signals between the exoskeleton and its operator as the primary means of information transportation. The signals were recorded by adopting the novel soft skin sensors attached to the bracket on the exoskeleton linkage, which could reflect the human arm motion intention through the virtual admittance model and adaptive control. Subsequently, a 1-DOF upper-limb exoskeleton was designed to illustrate the performance of the proposed sensor and the interaction control method in the human-robot cooperation experiment.