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

## TL;DR

This paper introduces a new algorithm for UAVs to efficiently search for missing persons in mountainous areas by using terrain slope data to guide the search.

## Contribution

The novel Slope Probability Search (SPS) algorithm uses terrain slope to create a dynamic probability map for intelligent UAV path planning.

## Key findings

- The SPS algorithm achieved an 88.9% success rate in simulations using real-world geographic data.
- The algorithm significantly reduced average search time compared to conventional methods.
- Three dynamic missing person models were used to validate the algorithm's effectiveness.

## 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.