Human Following Smart Shopping Trolley

Abstract

Helping people with physical disabilities to move independently is an important part of inclusive technology, but it is still not fully developed, especially for everyday personal use. This study focuses on a detailed design of a Smart Trolley created for users with mobility challenges, combining smart sensor-based systems and automation to reduce physical strain and improve self- reliance. The system is controlled by an Arduino Uno microcontroller, which manages signals from ultrasonic sensors, Bluetooth and IR tracking modules, and the L298N motor driver to enable semi- automatic motion. The trolley can be operated through several modes, such as voice commands, RFID tags, or a mobile application, allowing the user to move it, control its path, or let it follow automatically. Designed mainly for indoor environments like hospitals, shopping malls, and airports, it can detect and avoid obstacles for safety while staying close to the user. This paper explains the system design, working method, and expected benefits, showing how such assistive equipment can be a practical, affordable, and meaningful step toward improving independence and mobility for physically challenged people.

Introduction

The Smart Trolley for Physically Challenged People is designed to assist individuals with limited mobility in carrying and transporting personal items. Traditional carts require continuous manual effort, which can be difficult for such users. This smart trolley integrates automation, sensors, and embedded electronics to operate in a semi-autonomous manner, allowing it to follow the user or move based on commands while avoiding obstacles. The design focuses on simplicity, affordability, and reliability, using modern technologies like microcontrollers, sensors, rechargeable batteries, and wireless communication to provide an effective assistive solution that promotes independence and mobility.

The literature review shows that previous research has explored RFID-based smart shopping systems, autonomous navigation, and human-aware robotics. Some systems improved billing efficiency using RFID tags, while others focused on robotic navigation and obstacle avoidance. However, most solutions either lack autonomous movement or do not fully integrate retail-related functions. This highlights the need for a comprehensive system combining RFID billing, autonomous navigation, adaptive control, and human-aware interaction.

The methodology integrates hardware, software, and sensor-based modules to enable intelligent operation. The system uses an Arduino Uno microcontroller as the central processor, receiving input from ultrasonic sensors for obstacle detection and Bluetooth or IR sensors for user tracking. Users can control the trolley through voice commands, RFID identification, or a mobile application. The movement system uses DC motors with an L298N motor driver, allowing forward, backward, and directional movement. The trolley follows the user by estimating distance through Bluetooth signal strength or IR sensors, maintaining a safe distance of about 0.5–1.5 meters. Obstacle avoidance is achieved using ultrasonic sensors that detect objects and automatically stop or redirect the trolley.

The system architecture consists of four main layers:

1. Input Layer (Sensing Unit) – Collects environmental and user data using sensors such as ultrasonic, infrared, RFID, load sensors, and battery sensors.

2. Processing Layer (Control Unit) – Uses a microcontroller and algorithms to analyze sensor data, perform obstacle avoidance, manage power, and control navigation.

3. Output Layer (Actuation Unit) – Includes DC motors, servo motors, buzzers, LEDs, and display units to perform movement and provide feedback.

4. Communication Layer – Enables interaction with the user through Bluetooth modules, RFID billing systems, mobile applications, and optional voice assistance.

The trolley is powered by a 12V rechargeable lithium-ion battery, with voltage regulation for stable operation and possible future solar charging. Testing showed that the system can reliably follow the user, detect obstacles quickly, and operate efficiently with a single battery charge.

The expected outcomes include smooth autonomous navigation, accurate obstacle detection, improved user convenience, and enhanced mobility for physically challenged individuals. Overall, the Smart Trolley demonstrates how robotics and assistive technology can improve independence and accessibility in daily life.

Conclusion

The Smart Trolley marks a significant step forward in the domain of assistive technologies. Through the integration of automated motion control, intelligent sensing, and human- tracking mechanisms, it successfully minimizes physical strain while maintaining safety, comfort, and ease of use. The system’s adaptable and modular framework also makes it open to future upgrades and improvements, laying the groundwork for next-generation mobility assistance solutions. With continued research, testing, and optimization, the Smart Trolley can evolve into a reliable, cost- effective, and life-enhancing device that empowers individuals with physical challenges to move through their everyday environments with improved freedom, confidence, and independence.

References

[1] L. Zhao, L. Zhang, X. Li, H. Zhao and Y. Liu, “A Low-Cost RFID-Based Smart Shopping System,” in IEEE International Conference on Future Automation and IoT, 2021. Link: https://ieeexplore.ieee.org/document/92974 39 [2] P. Singh and V. Kumar, “Design and Implementation of a Smart Shopping Cart System,” IEEE International Conference on Advances in Computing, Communication and Automation (ICACCA), 2020. Link: https://ieeexplore.ieee.org/document/9 262350 [3] Autonomous Navigation and Mobile Robot Control Techniques for Dynamic Environments, IEEE Link: https://ieeexplore.ieee.org/document/0 088393 [4] Adaptive Control and Navigation Strategies for Autonomous Robots, IEEE Link: https://ieeexplore.ieee.org/document/1 0199259 [5] Human-Aware Motion Planning and Perception for Autonomous Platforms, IEEE Xplore. Link: https://ieeexplore.ieee.org/documen t/941804

Copyright

Copyright © 2026 Dr. P. M. Mankar, Rutu Bendre, Anjali Solanke, Chaitanya Meshram, Tejaswini Akotkar, Trupti Ugale, Tanvi Junghare. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.