Terrain-Informed UAV Path Planning for Mountain Search: A Slope-Based Probabilistic Approach

Wang, Xi; Wang, Xing; Zhao, Pengliang; Tan, Weihua; Zhang, Hongqiang; Chen, Lihuang; Zhou, Longhua

doi:10.3390/s26010062

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Open AccessArticle

Terrain-Informed UAV Path Planning for Mountain Search: A Slope-Based Probabilistic Approach

by

Xi Wang

^1,*

,

Xing Wang

¹

,

Pengliang Zhao

¹

,

Weihua Tan

^1,2

,

Hongqiang Zhang

¹

,

Lihuang Chen

¹

and

Longhua Zhou

¹

The School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411100, China

²

Sanya Institute, Hunan University of Science and Technology, Sanya 572024, China

^*

Author to whom correspondence should be addressed.

Sensors 2026, 26(1), 62; https://doi.org/10.3390/s26010062 (registering DOI)

Submission received: 20 November 2025 / Revised: 12 December 2025 / Accepted: 19 December 2025 / Published: 21 December 2025

(This article belongs to the Section Vehicular Sensing)

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Abstract

To enhance the efficiency of locating dynamic missing persons in complex mountain terrain, this study introduces an innovative Slope Probability Search (SPS) algorithm based on a modified A* framework. The algorithm’s core is a dynamic global probability map, constructed by linking terrain slope to the behavioral tendencies of missing persons. This fundamentally shifts the unmanned aerial vehicle (UAV) search paradigm from conventional coverage patterns to intelligent, guided exploration. To ensure a realistic evaluation, we designed three representative dynamic models for the missing persons: Terrain Constrained, Path Following, and Random Walk. The SPS algorithm, through its unique heuristic function, achieves an optimal balance between exploiting high probability areas and exploring new regions to maximize search efficiency. Simulation experiments using real-world geographic data demonstrated that even under severe constraints of limited search duration and sensor range, the algorithm achieved a success rate of 88.9% achieving an average search time substantially lower than that of conventional methods. This research provides a solid theoretical basis and a practical algorithmic framework for developing next generation intelligent search and rescue systems.

Keywords: UAV; path planning; dynamic target search; slope information; probability map

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MDPI and ACS Style

Wang, X.; Wang, X.; Zhao, P.; Tan, W.; Zhang, H.; Chen, L.; Zhou, L. Terrain-Informed UAV Path Planning for Mountain Search: A Slope-Based Probabilistic Approach. Sensors 2026, 26, 62. https://doi.org/10.3390/s26010062

AMA Style

Wang X, Wang X, Zhao P, Tan W, Zhang H, Chen L, Zhou L. Terrain-Informed UAV Path Planning for Mountain Search: A Slope-Based Probabilistic Approach. Sensors. 2026; 26(1):62. https://doi.org/10.3390/s26010062

Chicago/Turabian Style

Wang, Xi, Xing Wang, Pengliang Zhao, Weihua Tan, Hongqiang Zhang, Lihuang Chen, and Longhua Zhou. 2026. "Terrain-Informed UAV Path Planning for Mountain Search: A Slope-Based Probabilistic Approach" Sensors 26, no. 1: 62. https://doi.org/10.3390/s26010062

APA Style

Wang, X., Wang, X., Zhao, P., Tan, W., Zhang, H., Chen, L., & Zhou, L. (2026). Terrain-Informed UAV Path Planning for Mountain Search: A Slope-Based Probabilistic Approach. Sensors, 26(1), 62. https://doi.org/10.3390/s26010062

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Terrain-Informed UAV Path Planning for Mountain Search: A Slope-Based Probabilistic Approach

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