Efficient Path Planning for Complex Post-Disaster Environments with Terrain Variations and Obstacles

Effective path-finding in complex environments is pivotal for numerous domains, such as robotics and gaming. The
$A^{\ast}$
algorithm has been widely employed to balance completeness and optimality. However, in complex post-disaster environments, the traditional
$A^{\ast}$
algorithm often fails to successfully perform path planning due to many obstacles and altitude changes. This paper presents an
$A^{\ast}$
implementation tailored for navigating environments with obstacles and varying terrain elevations, aiming to deal with this problem. The algorithm could efficiently find optimal paths by considering obstacle avoidance and height differences. The results shows that compare to
$A^{\ast}$
algorithm, the enhanced
$A^{\ast}$
algorithm has better performance in terms of path length and success rate. Especially in high-complexity environments, the enhanced program can still maintain a search success rate of more than 95% with an ideal path length. The algorithm's adaptability and effectiveness in diverse environments make it a valuable tool for real-world navigation challenges. This paper contributes to understanding intelligent navigation techniques and their applications in complex post-disaster environments.