# Comparative Analysis of UAV Path Planning Algorithms for Efficient Navigation in Urban 3D Environments

arXiv: https://arxiv.org/abs/2508.16515 · HTML: https://mlsanity.com/paper/2508.16515/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 48 references that are in the map.
- First posted on arXiv 22 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [Graph-Based Topological Exploration Planning in Large-Scale 3D Environments](https://mlsanity.com/paper/2103.16829/) (2021)
- [An Autonomous Path Planning Method for Unmanned Aerial Vehicle based on A Tangent Intersection and Target Guidance Strategy](https://mlsanity.com/paper/2006.04103/) (2020)
- [Time-Optimal Handover Trajectory Planning for Aerial Manipulators based on Discrete Mechanics and Complementarity Constraints](https://mlsanity.com/paper/2209.00533/) (2022)

## Abstract (arXiv)

The most crucial challenges for UAVs are planning paths and avoiding obstacles in their way. In recent years, a wide variety of path-planning algorithms have been developed. These algorithms have successfully solved path-planning problems; however, they suffer from multiple challenges and limitations. To test the effectiveness and efficiency of three widely used algorithms, namely A*, RRT*, and Particle Swarm Optimization (PSO), this paper conducts extensive experiments in 3D urban city environments cluttered with obstacles. Three experiments were designed with two scenarios each to test the aforementioned algorithms. These experiments consider different city map sizes, different altitudes, and varying obstacle densities and sizes in the environment. According to the experimental results, the A* algorithm outperforms the others in both computation efficiency and path quality. PSO is especially suitable for tight turns and dense environments, and RRT* offers a balance and works well across all experiments due to its randomized approach to finding solutions.