analiza aiqbench
This study presents a rigorous simulation of hospital delivery robots that explicitly models shared constraints like elevators, charging stations, and human handoffs, revealing how resource bottlenecks can invalidate standard capacity metrics. By comparing different dispatch strategies against 33,079 real-world requests, the research demonstrates that optimizing for admission deadlines alone often fails to improve actual service completion times when infrastructure limits are ignored. The work offers a reproducible framework for evaluating coupled robotic workflows rather than estimating direct clinical benefits.
A State Based Dispatch Controller for Hospital Delivery Robots with Shared Human and Infrastructure Resources
arXiv:2610.05971v1 Announce Type: new Abstract: Robot delivery studies can overstate transport capacity when travel to pickups and human support fall outside the modeled schedule. We formulate a location aware dispatch model that couples robot admission to transporter support and shared elevators, charging, and cleaning. The model replays 33,079 observed hospital requests; missing contents, deadlines, staffing, and completion times remain explicit scenario assumptions. Two full grids compare human dispatch, a resource aware controller, and a proximity and workload benchmark in 30 paired replications per scenario. An exploratory extension tests a simpler deadline admission rule under the same operating model. Pickup travel increases demand on a pooled elevator bank. In one high staffing case, raising assumed elevator capacity from two to four reduces human only lateness from 55.11\% to 3.18\%, exceeding the dispatch differences. Direct policy comparisons and robot coverage distinguish admission selectivity from system service. The analysis explains why a robot can pass a deadline admission test yet delay service through staff handoffs and shared facilities. Its contribution is a reproducible evaluation of coupled dispatch workflows, with a clear separation between admission, completion, and return to availability. The results are conditional comparisons, not estimates of hospital benefit or released clinical capacity.
Abstract:Robot delivery studies can overstate transport capacity when travel to pickups and human support fall outside the modeled schedule. We formulate a location aware dispatch model that couples robot admission to transporter support and shared elevators, charging, and cleaning. The model replays 33,079 observed hospital requests; missing contents, deadlines, staffing, and completion times remain explicit scenario assumptions. Two full grids compare human dispatch, a resource aware controller, and a proximity and workload benchmark in 30 paired replications per scenario. An exploratory extension tests a simpler deadline admission rule under the same operating model. Pickup travel increases demand on a pooled elevator bank. In one high staffing case, raising assumed elevator capacity from two to four reduces human only lateness from 55.11\% to 3.18\%, exceeding the dispatch differences. Direct policy comparisons and robot coverage distinguish admission selectivity from system service. The analysis explains why a robot can pass a deadline admission test yet delay service through staff handoffs and shared facilities. Its contribution is a reproducible evaluation of coupled dispatch workflows, with a clear separation between admission, completion, and return to availability. The results are conditional comparisons, not estimates of hospital benefit or released clinical capacity.
Robotics (cs.RO); Human-Computer Interaction (cs.HC)
arXiv:2610.05971 [cs.RO]
(or arXiv:2610.05971v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.05971 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 291 cuvinte
Subiecte conexe

Forbes Asia 100 To Watch 2026
forbes.com · 24 aug. 2026
Problems of Proliferation
LessWrong · 29 sept. 2026

Amazon Research Awards recipients announced
Amazon Science · 27 mai 2026
mmHRI: Towards Privacy-Preserving Human-Robot Interaction with Millimeter-Wave Radar
arXiv · 29 sept. 2026