Comparative Evaluation of Reinforcement Learning Algorithms for Multi-Agent Unmanned Aerial Vehicle Path Planning in 2D and 3D Environments
Path planning in multi-agent UAV swarms is a crucial issue that involves avoiding collisions in dynamic, obstacle-filled environments while consuming the least amount of time and energy possible. This work comprehensively evaluates reinforcement learning (RL) algorithms for multi-agent UAV path plan...
Saved in:
| Main Authors: | Mirza Aqib Ali, Adnan Maqsood, Usama Athar, Hasan Raza Khanzada |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/9/6/438 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Research on Impact of Planned Path Length and Yaw Cost on Collaborative Search of Unmanned Aerial Vehicle Swarms
by: Heng Zhang, et al.
Published: (2025-05-01) -
Collaborative Integrated Navigation for Unmanned Aerial Vehicle Swarms Under Multiple Uncertainties
by: Le Zhang, et al.
Published: (2025-01-01) -
Vision-Based Deep Reinforcement Learning of Unmanned Aerial Vehicle (UAV) Autonomous Navigation Using Privileged Information
by: Junqiao Wang, et al.
Published: (2024-12-01) -
PROBLEM ANALYSIS USING NAVIGATION SYSTEMS OF UNMANNED AERIAL VEHICLES AT HIGH LATITUDES
by: S. V. Korevanov, et al.
Published: (2016-11-01) -
A Continuous Space Path Planning Method for Unmanned Aerial Vehicle Based on Particle Swarm Optimization-Enhanced Deep Q-Network
by: Le Han, et al.
Published: (2025-02-01)