ハイブリッド・ゴールデン・シャカールとモット・フラーム・オプティマイゼーション・アルゴリズムに基づく,異質なUAVネットワークにおけるカバーパスの計画
K Karthik1, C Balasubramanian2, R Praveen3
1Department of Electronics and Communication Engineering, P.S.R.R. College of Engineering, Sivakasi, Tamil Nadu, 626140, India. karthikkesavan@psrr.edu.in.
Scientific reports
|August 23, 2025
まとめ
この研究は,無人航空機 (UAV) のカバーパスの計画のためのハイブリッド最適化アルゴリズムを導入します. この新しいアプローチは効率を高め,タスクの完了と実行時間を短縮し,地域を包括的にカバーします.
科学分野:
- ロボット
- 人工知能
- 運用研究
背景:
- カバレッジ・パス・プランニング (CPP) は,無人航空機 (UAV) がターゲット地域を効率的に調査するために不可欠です.
- 異なる能力を持つ異質なUAVの飛行経路を最適化することは大きな課題です.
- エネルギー消費とタスクの完了時間を最小限に抑えることは,UAVベースのカバーミッションの重要な目標です.
研究 の 目的:
- 異質なUAVを使用した効果的なCoverage Path Planning (CPP) のための新しいハイブリッド最適化アルゴリズムを提案する.
- 指定されたターゲット地域を包括的にカバーするUAVの効率と有効性を高める.
- エネルギー消費を削減し,飛行性能を向上させるための飛行経路の最適化.
主な方法:
- CPPのためのハイブリッドのゴールデン・ジャカールとモット・フラーム・オプティマイゼーション・アルゴリズム (HGJMFOA) の開発
- 各UAVの最適な点から点への飛行経路を特定するための線形プログラミングモデルの統合.
- HGJMFOAを使用して可能な経路を探索し,飛行時間を最小限に抑える.
主要な成果:
- HGJMFOAのアプローチは,パフォーマンスメトリックの有意な改善を示しました.
- 作業の完了時間を 21.34% 短縮しました
- 24.56%の偏差率と34.19%の実行時間を削減しました.
結論:
- 提案されたHGJMFOA戦略は,異質なUAVのカバーパスの計画を効果的に最適化します.
- 多様なUAV間の協力により,最適なカバー,センサー範囲の利用,優れた飛行性能を達成しました.
- シミュレーションの結果は,時間を最小限に抑え,全体のミッション効率を改善するアルゴリズムの有効性を検証します.
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