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.
A system coordinator for the intelligent forwarding of transport orders enables the combination of several fleets of intralogistic transport vehicles such as robots in one material flow system without them having to exchange information with each other. Evaluating the operation of this coordinator requires a simulation environment that represents all adjacent systems, creating a Software-in-the-Loop (SiL) environment. This includes upstream systems, e.g. ERP or ME systems, several fleet controllers as well as a material flow system with all vehicles including their interactions and possible failures. In this article, the conceptual design of such an SiL system is presented. Additionally, a proof-of-concept demonstrates the fundamental functionality. Python is used for the system coordinator and the analysis, openTCS for the fleet controllers and Siemens Plant Simulation for the material flow simulation. The communication in between uses HTTP and raw TCP packets, respectively.
Purpose In current clinical practice, intraoperative repositioning of mobile C-arms is challenging due to a lack of visual cues and efficient guiding tools. This can be detrimental to the surgical workflow and lead to additional radiation burdens for both patient and personnel. To overcome this problem, we present our novel approach Lidar-based X-ray Positioning for Mobile C-arms (RAY-POS) for assisting circulating nurses during intraoperative C-arm repositioning without requiring external aids. Methods RAY-POS consists of a localization module and a graphical user interface for guiding the user back to a previously recorded C-Arm position. We conducted a systematic comparison of simultaneous localization and mapping (SLAM) algorithms using different attachment positions of light detection and ranging (LIDAR) sensors to benchmark localization performance within the operating room (OR). For two promising combinations, we conducted further end-to-end repositioning tests within a realistic OR setup. Results SLAM algorithm gmapping with a LIDAR sensor mounted 40 cm above the C-arm’s horizontal unit performed best regarding localization accuracy and long-term stability. The distribution of the repositioning error yielded an effective standard deviation of 7.61 mm. Conclusion We conclude that a proof-of-concept for LIDAR-based C-arm repositioning without external aids has been achieved. In future work, we mainly aim at extending the capabilities of our system and evaluating the usability together with clinicians.
The productivity of groups can be increased by enabling group members to share their perceptions of the environment. We adapt this concept for mobile robots by presenting an object-oriented approach to a shared environmental model. The objects are stored in a graph, which saves memory and computing power and allows the representation of hierarchical and topological relationships. Each object can contain geometric and semantic data as well as information about its current, past, and planned or estimated future movements. An example application shows that modeling future motion can prevent collisions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.