Autonomous Multi-Robot Infrastructure for AI-Enabled Healthcare Delivery and Diagnostics

• URL: http://arxiv.org/abs/2509.26106v1
• Date: Tue, 30 Sep 2025 11:27:33 GMT
• Title: Autonomous Multi-Robot Infrastructure for AI-Enabled Healthcare Delivery and Diagnostics
• Authors: Nakhul Kalaivanan, Senthil Arumugam Muthukumaraswamy, Girish Balasubramanian,
• Abstract summary: This research presents a multi-robot system for inpatient care incorporating wearable health sensors, RF-based communication, and AI-driven decision support.<n>Within a simulated hospital environment, the system adopts a leader-follower swarm configuration to perform patient monitoring, medicine delivery, and emergency assistance.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: This research presents a multi-robot system for inpatient care, designed using swarm intelligence principles and incorporating wearable health sensors, RF-based communication, and AI-driven decision support. Within a simulated hospital environment, the system adopts a leader-follower swarm configuration to perform patient monitoring, medicine delivery, and emergency assistance. Due to ethical constraints, live patient trials were not conducted; instead, validation was carried out through controlled self-testing with wearable sensors. The Leader Robot acquires key physiological parameters, including temperature, SpO2, heart rate, and fall detection, and coordinates other robots when required. The Assistant Robot patrols corridors for medicine delivery, while a robotic arm provides direct drug administration. The swarm-inspired leader-follower strategy enhanced communication reliability and ensured continuous monitoring, including automated email alerts to healthcare staff. The system hardware was implemented using Arduino, Raspberry Pi, NRF24L01 RF modules, and a HuskyLens AI camera. Experimental evaluation showed an overall sensor accuracy above 94%, a 92% task-level success rate, and a 96% communication reliability rate, demonstrating system robustness. Furthermore, the AI-enabled decision support was able to provide early warnings of abnormal health conditions, highlighting the potential of the system as a cost-effective solution for hospital automation and patient safety.

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