用基础设施传感器实现全局定位，让机器人更安全高效地移动。
Intelligent Mobility System with Integrated Motion Planning and Control Utilizing Infrastructure Sensor Nodes
- 通过基础设施节点和云端计算实现全局感知与定位
- 结合MPC与人工势场法，可平稳避障并跟踪预定路径
- 适合智能自动驾驶车辆，如四轮独立驱动的运输车
本文提出一种室内自主移动系统框架，用于患者转运和物资搬运。与依赖车载感知传感器的传统系统不同，该方法利用基础设施传感器节点（ISNs）和云计算技术实现全局感知与定位（PL）。基于全局定位，设计了一种融合模型预测控制（MPC）与人工势场（APF）的集成式局部规划与跟踪控制器，具备可靠的运动规划与障碍物避让能力，并能精准跟踪预设参考轨迹。仿真结果表明，所提MPC控制器在绕行静态与动态障碍物时表现良好。该系统有望扩展至智能网联自动驾驶车辆，如四轮独立驱动/转向（4WID-4WIS）的电动或货运车辆。
原文摘要 · Abstract (English)
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.
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