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Goal-Seeking and Obstacle Avoidance Behaviour Using ORB Feature Extraction Approach for Improved Localization of Landmine Detection Mobile Robot

International Journal of Electrical and Electronics Engineering
© 2025 by SSRG - IJEEE Journal
Volume 12 Issue 10
Year of Publication : 2025
Authors : C.N. Naga Priya, S. Denis Ashok
10.14445/23488379/IJEEE-V12I10P108
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How to Cite?

C.N. Naga Priya, S. Denis Ashok, "Goal-Seeking and Obstacle Avoidance Behaviour Using ORB Feature Extraction Approach for Improved Localization of Landmine Detection Mobile Robot," SSRG International Journal of Electrical and Electronics Engineering, vol. 12,  no. 10, pp. 94-109, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I10P108

Abstract:

Unexploded buried landmines continue to pose a severe threat to human safety and the environment, while conventional manual detection methods remain slow, hazardous, and resource-intensive. Autonomous mobile robots equipped with advanced sensing capabilities provide a safer and more efficient alternative; however, achieving robust mine localization in open outdoor environments remains difficult due to sparse features, irregular terrain, and natural obstacles that complicate navigation. This work introduces a novel approach that integrates thermal–visual perception with adaptive path planning to address these challenges. Binary occupancy grid maps are generated from thermal imagery to provide a reliable environmental representation under varying lighting conditions. An Artificial Neural Network (ANN) is employed for hotspot detection, while ORB-based feature extraction and feature matching enable accurate camera pose estimation for visual localization. The proposed system fuses thermal and visual data to detect potential landmine signatures and natural obstacles in real time. A Rapidly-Exploring Random Tree (RRT) path planning algorithm is incorporated to ensure safe navigation across uneven terrain while avoiding hazards such as rocks and stones. Real-time decision-making is facilitated by thermal imaging, which enhances the localization of potential mine concentrations. Simulation results demonstrate the effectiveness of this integrated framework in simultaneously identifying buried landmines and planning safe traversable paths. By combining ANN-based classification, ORB feature matching, and RRT-based planning, the system achieves consistent classification of hazardous hotspots while dynamically adapting navigation strategies. The proposed approach represents a significant advancement toward reliable, autonomous landmine detection and contributes to enhancing safety in mine-contaminated regions.

Keywords:

ORB feature extraction, ANN classification, Multi-goal seeking RRT, Landmine detection, Mobile robot.

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