The real-time search algorithm, while effective in certain scenarios, has some potential drawbacks that can impact its practicability and performance. One drawback is the linear search trajectory, which limits the algorithm's ability to explore complex terrain efficiently. To address this limitation, introducing adaptive search strategies that dynamically adjust the search trajectory based on real-time feedback can enhance the algorithm's flexibility and coverage optimization. Another drawback is the trade-off between update frequency and power consumption. To mitigate this, optimizing the algorithm's update frequency based on the terrain complexity and communication requirements can help strike a balance between performance and power efficiency. Additionally, incorporating predictive modeling techniques to anticipate user movements and terrain changes can improve the algorithm's responsiveness and decision-making capabilities. By addressing these drawbacks through adaptive strategies and predictive modeling, the real-time search algorithm can be enhanced to achieve better practicability and performance.