# Path planning for UAVs in complex terrain based on the PGD model: Algorithmic improvements combining feature extraction and reinforcement learning

## TL;DR

This paper introduces a new model for UAV path planning in complex terrains using a combination of machine learning techniques to improve efficiency and adaptability.

## Contribution

The PGD model integrates Transformer, GAN, and DDPG for UAV path planning, offering a novel framework with improved adaptability and performance in complex environments.

## Key findings

- PGD outperforms baseline models in path length, collision rate, and computational efficiency on UAVDT and AirSim datasets.
- The model's multi-module synergy enhances feature correlation and physical path constraints for intelligent planning.
- PGD demonstrates significant improvements in path planning efficiency and adaptability in high-complexity terrains.

## Abstract

This paper proposes the PGD model for UAV path planning in complex terrain, addressing key challenges such as high-dimensional state processing, blind path exploration, and poor cross-scene adaptability. The PGD model integrates Transformer, GAN, and DDPG, forming a “compression-generation-optimization" closed-loop system. The Transformer module compresses high-dimensional terrain data, alleviating training bottlenecks, while the GAN module generates high-quality candidate paths, reducing ineffective exploration. DDPG then optimizes the path planning strategy efficiently. Experimental results demonstrate the superior performance of PGD on the UAVDT (suburban) and AirSim (canyon) datasets. In terms of path length (Pl), PGD achieves 20.0m/22.0m, compared to baseline models such as PPO-DRL (23.8m) and Soft Actor-Critic (24.0m). PGD also outperforms in collision rate (Cr) with 2.5%/3.0% and computational efficiency (Tc) with 13.5s/16.0s, respectively. The PGD model shows significant improvements in path planning efficiency and adaptability, particularly in high-complexity terrains. Compared to traditional models, PGD’s multi-module synergy enhances feature correlation and physical path constraints, offering a novel framework for intelligent planning in complex environments. Future work will focus on enhancing model adaptability to extreme weather and multi-agent collaborative scenarios.