This paper presents the development of a fully autonomous delivery robot integrating mechanical engineering, embedded systems, and artificial intelligence. The platform employs a heterogeneous computing architecture, with RPi 5 and ROS 2 handling AI-based perception and path planning, while ESP32 running FreeRTOS ensures real-time motor control. The mechanical design was optimized for payload capacity and mobility through precise motor selection and material engineering. Key technical challenges addressed include optimizing computationally intensive AI algorithms on a resource-constrained platform and implementing a low-latency, reliable communication link between the ROS 2 host and embedded controller. Results demonstrate deterministic, PID-based motor control through rigorous memory and task management, and enhanced system reliability via AWS IoT monitoring and a firmware-level motor shutdown failsafe. This work highlights a unified, multi-disciplinary methodology, resulting in a robust and operational autonomous delivery system capable of real-world deployment.

翻译：本文介绍了一种集机械工程、嵌入式系统与人工智能于一体的全自主配送机器人的开发。该平台采用异构计算架构，其中RPi 5与ROS 2负责基于人工智能的感知与路径规划，而运行FreeRTOS的ESP32则确保实时电机控制。通过精确的电机选型与材料工程，机械设计在有效载荷能力与机动性方面得到了优化。所解决的关键技术挑战包括：在资源受限的平台上优化计算密集的人工智能算法，以及在ROS 2主机与嵌入式控制器之间实现低延迟、可靠的通信链路。结果表明，通过严格的内存与任务管理实现了基于PID的确定性电机控制，并借助AWS IoT监控与固件级电机紧急停机安全机制提升了系统可靠性。这项工作展示了一种统一的多学科方法，最终构建出一个鲁棒且可实际部署运行的自主配送系统。