Landing Path Planning for Obstacle Avoidance of Civil Aircraft

Runway incursions present a significant safety risk during aircraft landing. To mitigate collision hazards, auxiliary systems must plan safe obstacle-avoidance paths based on environmental perception and risk assessment, while satisfying the aircraft’s kinematic and performance constraints. This paper focuses on emergency avoidance scenarios in the landing phase and proposes a path exploration mechanism with dynamically allocated search strategies. Extending the Rapidly-exploring Random Tree* (RRT*) framework, two additional search strategies are introduced and adaptively assigned according to obstacle complexity, thus improving search efficiency. Compared to RRT * and Bidirectional Rapidly-exploring Random Tree* (Bi-RRT*), the proposed approach reduces planning time and enhances search performance and success rate. Furthermore, the generated paths are refined via greedy optimization to meet kinematic constraints, increasing their feasibility.

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Publication Details

Journal
Guidance Navigation and Control
Published
2025-12-20
DOI
https://doi.org/10.1142/s2737480725500438
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.00
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article

Landing Path Planning for Obstacle Avoidance of Civil Aircraft

Haixiang Huang, Zhangjun Sun, Weiping Yang, Pengyuan Qi
Guidance Navigation and Control
Robotic Path Planning Algorithms
article

Landing Path Planning for Obstacle Avoidance of Civil Aircraft

Haixiang Huang, Zhangjun Sun, Weiping Yang, Pengyuan Qi
article en

Abstract

Runway incursions present a significant safety risk during aircraft landing. To mitigate collision hazards, auxiliary systems must plan safe obstacle-avoidance paths based on environmental perception and risk assessment, while satisfying the aircraft’s kinematic and performance constraints. This paper focuses on emergency avoidance scenarios in the landing phase and proposes a path exploration mechanism with dynamically allocated search strategies. Extending the Rapidly-exploring Random Tree* (RRT*) framework, two additional search strategies are introduced and adaptively assigned according to obstacle complexity, thus improving search efficiency. Compared to RRT * and Bidirectional Rapidly-exploring Random Tree* (Bi-RRT*), the proposed approach reduces planning time and enhances search performance and success rate. Furthermore, the generated paths are refined via greedy optimization to meet kinematic constraints, increasing their feasibility.

Guidance Navigation and ControlVol. 06(01)
Craft Group (China) (CN), Beihang University (CN)
Openalex Percentile: Top 57%
Robotic Path Planning Algorithms
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