Enhanced Path Planning by Repositioning the Starting Point

Drone power management poses ongoing challenges that significantly impact operational effectiveness across various applications. This research examines path planning optimization, particularly focusing on distance minimization to enhance efficiency and performance. When drones must visit static ground stations, analyzing the constituent elements of flight paths reveals that segments connecting the launch pad to initial and final stations emerge as a distinct area for further path optimization. Given scenarios where launch pad relocation remains feasible, this study proposes several alternative methodologies for adjusting launch positions to minimize total flight distances across multiple drone operations. The investigation employed extensive experimentation involving diverse configurations with varying station counts and available drone units. Results demonstrate that repositioning the launch pad to serve as an optimal center point for all drone routes yields substantial improvements in total distance minimization, ranging from 4% to 22% across different operational scenarios. The geometric median approach consistently outperformed alternative positioning strategies, achieving these improvements while maintaining computational efficiency. These findings contribute to sustainable drone operations by reducing energy consumption through optimized flight planning. The methodology proves particularly valuable for applications requiring flexible launch point positioning, offering practical solutions for enhancing operational efficiency in environmental monitoring, precision agriculture, and infrastructure inspection tasks where energy conservation directly impacts mission success and operational viability.

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Publication Details

Journal
Applied Sciences
Published
2025-08-08
DOI
https://doi.org/10.3390/app15168786
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.00
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article

Enhanced Path Planning by Repositioning the Starting Point

Ioannis Karydis, Gregory Gasteratos
Applied Sciences
Robotic Path Planning Algorithms
article

Enhanced Path Planning by Repositioning the Starting Point

Ioannis Karydis, Gregory Gasteratos
article en

Abstract

Drone power management poses ongoing challenges that significantly impact operational effectiveness across various applications. This research examines path planning optimization, particularly focusing on distance minimization to enhance efficiency and performance. When drones must visit static ground stations, analyzing the constituent elements of flight paths reveals that segments connecting the launch pad to initial and final stations emerge as a distinct area for further path optimization. Given scenarios where launch pad relocation remains feasible, this study proposes several alternative methodologies for adjusting launch positions to minimize total flight distances across multiple drone operations. The investigation employed extensive experimentation involving diverse configurations with varying station counts and available drone units. Results demonstrate that repositioning the launch pad to serve as an optimal center point for all drone routes yields substantial improvements in total distance minimization, ranging from 4% to 22% across different operational scenarios. The geometric median approach consistently outperformed alternative positioning strategies, achieving these improvements while maintaining computational efficiency. These findings contribute to sustainable drone operations by reducing energy consumption through optimized flight planning. The methodology proves particularly valuable for applications requiring flexible launch point positioning, offering practical solutions for enhancing operational efficiency in environmental monitoring, precision agriculture, and infrastructure inspection tasks where energy conservation directly impacts mission success and operational viability.

Applied SciencesVol. 15(16)
Ionian University (GR)
Openalex Percentile: Top 81%
Robotic Path Planning Algorithms
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