A Unified AI, Embedded, Simulation, and Mechanical Design Approach to an Autonomous Delivery Robot

TL;DR

This paper describes a comprehensive approach to designing an autonomous delivery robot by integrating mechanical, embedded, and AI systems, demonstrating a reliable, real-world capable platform with optimized hardware and software solutions.

Contribution

It introduces a unified multidisciplinary methodology combining mechanical design, embedded systems, and AI for autonomous delivery robots, addressing computational and communication challenges.

Findings

Deterministic motor control achieved with PID and task management.

Enhanced system reliability through AWS IoT monitoring and failsafe mechanisms.

Successful real-world deployment of the autonomous delivery platform.

Abstract

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.