Swarm Robotics for Automated Inventory and Delivery Systems in Hospital Pharmacies

Abstract

The increasing complexity of hospital pharmacy operations characterized by high-volume dispensing, multidimensional inventory management, and stringent safety requirements necessitates intelligent automation capable of adaptive, real-time coordination. This study proposes an integrative framework for deploying swarm robotics systems in automated hospital pharmacy environments, emphasizing collective intelligence, dynamic path optimization, and task allocation driven by bio-inspired algorithms. Unlike centralized robotic systems, swarm architectures leverage distributed autonomy and emergent behaviors to enhance reliability, scalability, and resilience against single-point failures. The paper explores how multi-agent reinforcement learning (MARL), deep neural coordination, and haptic-assisted delivery control can be unified under a robust cyber-physical infrastructure to manage medication storage, transport, and real-time delivery. Drawing on prior innovations in robotics-driven healthcare (Fatunmbi, 2022; Fatunmbi et al., 2022; Ozdemir & Fatunmbi, 2024; Fatunmbi, 2023), this manuscript provides a holistic synthesis of technical, operational, and ethical dimensions of swarm robotics in healthcare logistics. Empirical and simulated findings reveal that swarm-based pharmacy logistics can reduce retrieval times by up to 40%, optimize space utilization, and significantly minimize medication dispensing errors. The study concludes with a critical reflection on interoperability challenges, ethical AI governance, and the future trajectory of swarm robotic healthcare ecosystems.