Steering Control of Ackermann Architecture Weed Managing Mobile Robot

Abstract

A robot designed to identify and remove weeds from crops is known as a weed control robot. Weeds compete with primary crops for moisture, hinder their growth, and may harm both human and animal health, leading to reduced crop yields. Traditionally, herbicides and other chemicals have been used to eliminate weeds, but these methods can damage crops and pollute the environment. In this work, we propose a new semantic weed detection method based on the PC/BC-DIM network, which demonstrates superior performance and classification accuracy compared to existing approaches. We developed an autonomous weed control robot incorporating Ackermann Architecture and a delta robot. The delta robot is equipped with a camera at its base to detect weeds in real-time. First, the robot captures images using the camera, and through image processing techniques, it differentiates weeds from crops. Detected weeds are then eliminated using an electrical discharge method, where electrodes attached to the robot’s end effector burn the targeted weeds. Additionally, we developed a path-planning and obstacle-avoidance system to help the mobile robot navigate the field. This system uses stereo vision to capture stereo images of the environment and calculate their disparity. By extracting depth information, the robot can detect obstacles, avoid them, and follow the shortest path using the A* algorithm. The results from this work are simulation-based, demonstrating effective weed detection in field images and efficient robot navigation using stereo images. The system achieved an overall accuracy of 81.25%. Although the system performs moderately well, the relatively high False Positive Rate and Root Mean Square (RMS) Error indicate the need for further improvements to reduce errors and false positives. Future work will focus on enhancing weed removal and implementing the simulated results on hardware.

Authors and Affiliations

Faryal Naeem Mehmood, Syeda Ambreen Zahra, Syed Muhammad Wasif, Zubair Mehmood, Muhammad Jehanzeb Irshad, Nazam Siddique

Keywords

Related Articles

https://journal.50sea.com/index.php/IJIST/article/view/1078/1629

The cryptocurrency market has evolved in unprecedented ways over the past decade. However, due to the high price volatility associated with cryptocurrencies, predicting their prices remains an attractive research topic...

A Computational Studyof Ichthyofaunal Diversity of River Kabul

Mcclelland initiated the scientific study of the fish species of the River Kabul in 1842, and many researchers have continued this work since then. The primary goal of these studies has been to do a computational study...

An Efficient Read and Mark Mechanism for Multiple-choice Questions Using Optical Character Recognition

This research paper focuses on modifying the grading of multiple-choice questions (MCQs) to better the efficiency and incorrectness of educational tests. Conventional grading systems, such as optical...

Current-Injected Mode Control for Coupled-Inductor (Ci) Based Boost Converter

With the increasing demand for electrical energy, there is a need to replace conventional energy resources with renewable energy resources. To properly implement renewable resources at a larger scale, DC/DC converters...

Tablet Guard: Load Cell based Quality Assurance with Image Processing

Every week, the pharmaceutical business manufactures thousands of pills, each of which must be thoroughly checked before being distributed to customers. The proposed TabletGuard project addresses this issue through i...

Download PDF file
  • EP ID EP764472
  • DOI -
  • Views 25
  • Downloads 0

How To Cite

Faryal Naeem Mehmood, Syeda Ambreen Zahra, Syed Muhammad Wasif, Zubair Mehmood, Muhammad Jehanzeb Irshad, Nazam Siddique (2025). Steering Control of Ackermann Architecture Weed Managing Mobile Robot. International Journal of Innovations in Science and Technology, 7(5), -. https://europub.co.uk/articles/-A-764472