Gain-based Green Ant Colony Optimization for 3D Path Planning on Remote Sensing Images

Sangeetha Viswanathan; Kattur Soundarapandian Ravichandran

doi:10.31181/sor21202510

Authors

Sangeetha Viswanathan School of Computer Science, BITS Pilani University, Karnataka, India Author https://orcid.org/0000-0002-9568-5000
Kattur Soundarapandian Ravichandran Department of Mathematics, Amrita School of Physical Sciences, Amrita Vishwa Vidyapeetham, Coimbatore, India Author https://orcid.org/0000-0003-2397-461X

DOI:

https://doi.org/10.31181/sor21202510

Keywords:

3D path planning, Unmanned ground vehicle, Ant colony optimization, Pheromone enhancement, Remote sensing images

Abstract

Metaheuristic algorithms are powerful methods for handling complexities in 3D environments because of their adaptability property. This paper proposes a gain-based, green-ant colony optimization (GGACO) method for 3D path planning on remote sensing images. Shortest paths do not always imply minimum energy consumption. Moreover, computational complexity tends to increase in the case of higher-dimensional data. A novel method is proposed to alleviate this issue, one that provides an efficient path with minimum energy consumption by adding a gain quantity during its search. The results are validated using performance measures, viz., path length, time, and energy cost. Real-time images, along with their corresponding ground truth and “digital surface models (DSM)”, have been sourced from the “International Society for Photogrammetry and Remote Sensing (ISPRS)”. Comparisons have been made against state-of-the-art algorithms and analyzed. Finally, the convergence and stability of the proposed method have been verified; it has been found that the proposed method outperforms the existing method by 6%, 11% and 5% regarding length, computation time, and energy, respectively.

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References

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