Autonomous Indoor Delivery Robot

Abstract

The robot actualizes the concept of autonomous last mile delivery in indoor environments. Designed to transport small goods, mail, and groceries efficiently, it employs a real‐time obstacle detection system, a dynamic path planning algorithm, and SLAM-based mapping using a RPLIDAR. An Nvidia Jetson Nano handles intensive computations, ensuring precise navigation and effective obstacle avoidance.

Introduction

Autonomous indoor delivery addresses the critical need for efficient last-mile logistics within confined spaces. This project report presents the design and implementation of a delivery robot that seamlessly navigates indoor environments. By integrating advanced sensors, SLAM algorithms for mapping, and real-time processing capabilities, the system ensures that delivery tasks are carried out with high accuracy and minimal human intervention.

Methodology

The core methodology is centered on real-time obstacle detection and dynamic path planning:

• Sensor Integration: A RPLIDAR captures 360° scan data that is processed through SLAM to create an accurate map.
• Path Planning & Navigation: The robot employs an adaptive algorithm to compute optimal routes and avoid obstacles in real time.
• Computational Unit: An Nvidia Jetson Nano processes sensor data rapidly, enabling timely decision-making.
• Localization: Sensor fusion from the RPLIDAR, IMU, and other inputs provides precise position and orientation data.

Experimental Setup & Results

Tested in a controlled indoor environment, the robot demonstrated efficient navigation and reliable obstacle avoidance. The integration of real-time sensor data with advanced path planning resulted in a robust system capable of executing autonomous delivery tasks with high precision.

Conclusion

The Autonomous Indoor Delivery Robot offers a promising solution for last-mile delivery challenges within indoor settings. Its ability to autonomously navigate, detect obstacles, and compute optimal routes paves the way for cost-effective and reliable delivery systems. Future enhancements may include further refinement of sensor fusion algorithms and extended operational capabilities.

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