This paper introduces a framework for an indoor autonomous mobility system that can perform patient transfers and materials handling. Unlike traditional systems that rely on onboard perception sensors, the proposed approach leverages a global perception and localization (PL) through Infrastructure Sensor Nodes (ISNs) and cloud computing technology. Using the global PL, an integrated Model Predictive Control (MPC)-based local planning and tracking controller augmented with Artificial Potential Field (APF) is developed, enabling reliable and efficient motion planning and obstacle avoidance ability while tracking predefined reference motions. Simulation results demonstrate the effectiveness of the proposed MPC controller in smoothly navigating around both static and dynamic obstacles. The proposed system has the potential to extend to intelligent connected autonomous vehicles, such as electric or cargo transport vehicles with four-wheel independent drive/steering (4WID-4WIS) configurations.

翻译：本文提出了一种室内自主移动系统框架，该系统能够执行患者转运与物料搬运任务。与传统依赖车载感知传感器的系统不同，所提出的方法通过基础设施传感器节点（ISNs）与云计算技术实现全局感知与定位（PL）。利用全局PL信息，我们开发了一种集成模型预测控制（MPC）的局部规划与跟踪控制器，并辅以人工势场（APF）增强，使其在跟踪预设参考运动的同时，具备可靠高效的运动规划与避障能力。仿真结果表明，所提出的MPC控制器能够有效实现静态与动态障碍物的平稳规避。该系统具有扩展到智能网联自动驾驶车辆的潜力，例如采用四轮独立驱动/转向（4WID-4WIS）配置的电动或货运车辆。