Mobile service robots for the operating room wing: balancing cost and performance by optimizing robotic fleet size and composition.

PURPOSE: Integrating fleets of mobile service robots into the operating room wing (OR wing) has the potential to help overcome staff shortages and reduce the amount of dull or unhealthy tasks for humans. However, the OR wing has been little studied in this regard and the requirements for realizing this vision have not yet been fully identified. This includes fundamental aspects such as fleet size and composition, which we have now studied comprehensively for the first time. METHODS: Using simulation, 150 different scenarios with varying fleet compositions, robot speeds and workloads were studied for a setup based on a real-life OR wing. The simulation included battery recharging cycles and queueing due to shared resources. RESULTS: For all simulated scenarios we report results regarding total duration of execution, average task response times and fleet utilization. The relationship between these performance measures and global scenario parameters-such as fleet size, fleet composition, robot velocity and the number of operating rooms to be served-is visualized. CONCLUSION: Our simulation-based studies have proven to be a valuable tool for individualized dimensioning of mobile robotic fleets, based on realistic workflows and environmental models. Thereby, important implications for future developments of mobile robots have been identified and a basis of decision-making regarding fleet size, fleet composition, robot capabilities and robot velocities can be provided. Due to costs, space limitations and safety requirements, these aspects must be carefully considered to successfully integrate mobile robotic technology into real-world OR wing environments.

Intuitive teaching of medical device operation to clinical assistance robots.

PURPOSE: The adjustment of medical devices in the operating room is currently done by the circulating nurses. As digital interfaces for the devices are not foreseeable in the near future and to incorporate legacy devices, the robotic operation of medical devices is an open topic. METHODS: We propose a teleoperated learning from demonstration process to acquire the high-level device functionality with given motion primitives. The proposed system is validated using an insufflator as an exemplary medical device. RESULTS: At the beginning of the proposed learning period, the teacher annotates the user interface to obtain the outline of the medical device. During the demonstrated interactions, the system observes the state change of the device to generalize logical rules describing the internal functionality. The combination of the internal logics with the interface annotations enable the robotic system to adjust the medical device autonomously. To interact with the device, a robotic manipulator with a finger-like end-effector is used while relying on haptic feedback from torque sensors. CONCLUSION: The proposed approach is a first step towards teaching a robotic system to operate medical devices. We aim at validating the system in an extensive user study with clinical personnel. The logical rule generalization and the logical rule inference based on computer vision methods will be focused in the future.