Adaptive Multi-Agent Coordination for Urban Medicine Delivery in Elderly Care Facilities

In senior care facilities, where staff availability is limited and patient needs are timesensitive, effective and dependable medication delivery systems are essential. These issues are successfully addressed by multi-robot systems that use adaptive Multi-Agent Reinforcement Learning (MARL) to coordinate Autonomous Mobile Robots (AMRs) for on-time delivery. These systems improve operational efficiency in care facilities by enabling navigation of complex environments with both static and dynamic obstacles by utilizing powerful simulations (e.g., Webots). By integrating sophisticated MARL algorithms like Factored Centralized Actor-Critic (FACMAC) and Counterfactual Multi-Agent (COMA) into the Robot Operating System 2 (ROS 2) framework and the PyTorch library, it is possible to allocate tasks adaptively and enhance the coordination of multiple robots in real-time scenarios with varying human interactions. These algorithms improve the robots’ capacity to anticipate and react to people’s movements within the assisted living facility. Extensive training results show that MARLcoordinated robots achieve 92-95% completion rates with fewer collisions and improved routing efficiency, significantly outperforming classical multi-agent path finding(MAPF) systems. Insights also highlight the potential for deploying multiple physical robots and emphasize the significance of using digital twin simulations for safe training environments. The results corroborate the expanding body of research that argues for the use of intelligent robotic systems in eldercare to improve care quality and alleviate caregiver shortages. To sum up, the use of sophisticated algorithms in robotics creates the foundation for more effective and efficient drug delivery systems in senior care settings. Future research should concentrate on applying learned policies from simulation to real-world applications while maintaining ethical considerations, especially with regard to the dynamics of the relationship between elderly patients and robots.