Mean_Based Approach to Omnidirectional Pathfinding Algorithm Using Single Ultrasonic Sensor for Robotic Car

Abstract

For design and testing the smart pathfinding system for a robotic car using ultrasonic sensor. In this project we are using Q learning algorithm which means it can be created own path itself. The proposed algorithm works in two phases: normal detection and intensive detection. In the normal detection phase, the sensor scans for obstacles directly in front of the car. In the intensive detection phase, a servo motor moves the sensor to scan a wider area, acting like multiple sensors combined. This method improves obstacle detection and Accuracy. The system is built on an Arduino UNO platform and controls DC motors using an H-W 130 motor shield. The ultrasonic sensor gathers distance data, and the algorithm processes this information to determine the safest path. A mean based approach is used to reduce false detections and improve accuracy. Unlike traditional robotic systems that require multiple sensors, our proposed model achieves 360-degree obstacle detection with single sensor, reducing cost hardware complexity and Energy Consumption. The robotic car was tested in real world conditions, and obstacles were successfully detected and avoided with high accuracy.

Introduction

Pathfinding algorithms are crucial for robotics and automation, especially for autonomous vehicles navigating complex environments. Traditional ultrasonic sensor systems typically make simple left or right decisions, which can be inaccurate and inefficient. This research improves navigation by combining an ultrasonic sensor mounted on a servo motor with a novel omnidirectional pathfinding algorithm, enabling better obstacle detection and route selection.

The proposed system uses a single ultrasonic sensor that scans the environment by rotating on a servo motor, increasing the sensor’s field of view and accuracy. The robotic car analyzes distance data from multiple angles, identifies the safest path with the most open space, and uses omni-directional wheels to maneuver without needing to turn fully. If no clear path is found, the car rotates 180° and re-scans. This approach reduces errors, avoids unnecessary stops, and keeps hardware simple and cost-effective.

Key components include the Arduino Uno microcontroller, ultrasonic sensor, servo motor, L293D motor driver, buzzer for alerts, HC-05 Bluetooth module for remote control, and a camera module for visual input. The system’s effectiveness is demonstrated by the robotic car’s smooth, intelligent movement and real-time obstacle avoidance, with direction feedback displayed on an LCD.

Conclusion

After thorough development and testing, we have successfully implemented an enhanced pathfinding and obstacle-avoidance algorithm on a test RC car. Practical trials have shown that this algorithm allows the vehicle to navigate with high accuracy. The dual-phase approach optimizes power consumption while ensuring reliable and precise path selection. This technology holds significant potential for applications in military operations, research projects, and hazardous environments where human presence is risky.

References

[1] Tandon, A. et al. (2024) – Proposes a mean-based approach for omnidirectional pathfinding using a single ultrasonic sensor in robotic cars. Presented at IEEE SCEECS 2024. [2] Khojasteh, M. S., &Salimi-Badr, A. (2024) – Introduces a deep reinforcement learning-based autonomous quadrotor path-planning approach incorporating monocular depth estimation. [3] Mahmud, T. et al. (2023) – Designs and implements an Arduino-based ultrasonic sensor system for obstacle avoidance in robots. Focuses on sustainable technology applications. [4] Borenstein, J., &Koren, Y. (1988) – A foundational study on obstacle avoidance using ultrasonic sensors, widely cited in robotics research. [5] Massoud, M. M. et al. (2022) – Compares various indoor path-planning techniques for omni-wheeled mobile robots, evaluating performance and optimization. [6] Mu, W. Y. et al. (2016) – Proposes an omni-directional scanning localization method for mobile robots based on ultrasonic sensors. [7] Massoud, M. M. et al. (2022) – Duplicate of Reference [8] Hsu, C. C. et al. (2011) – Discusses localization techniques for mobile robots using omnidirectional ultrasonic sensing. [9] Chen, L. et al. (2018) – Describes a wireless car control system using Arduino UNO R3, integrating remote communication. [10] Sissodia, R. et al. (2023) – Develops an Arduino-based Bluetooth voice-controlled robot car with obstacle detection. [11] Akilan, T. et al. (2020) – Proposes a surveillance robot for hazardous environments using IoT technology.

Copyright

Copyright © 2025 Mrs. A.S Lavanya, B. Madhusudhan, D. Saketh, K. Shabrin, S. Sajid. 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.