Abstract: Multi-unmanned aerial vehicle path planning encounters challenges with effective obstacle avoidance and collaborative operation. The study proposes a swarm planning technique for unmanned aerial vehicles, based on an improved jump point algorithm. It introduces a geometric collision detection strategy to optimize path search and employs the dynamic window method to constrain the flight range. Additionally, the study presents conflict avoidance strategies for multi-unmanned aerial vehicle path planning and establishes collision fields for unmanned aerial vehicles to achieve collaborative path planning. In single unmanned aerial vehicle path planning, the research model exhibits the lowest control errors in the X, Y, and Z axes, with the Y-axis error being 0.05m. In static planning, the model boasts the shortest planning time and length, with 1002ms and 17.85m in multi-obstacle planning, respectively. In multi-unmanned aerial vehicle path planning, the research model effectively avoids local optimal problems in local conflict scenarios and re-plans the route. During testing on a 29m×29m grid map, the research technology successfully avoids obstacles and re-plans routes. However, similar technological obstacles can cause interference and traps in local convergence, preventing re-planning. The research technology demonstrates good application effects in the path planning of unmanned aerial vehicle swarms and will provide technical support for multi-machine collaborative path planning.

Haizhou Zhang and Shengnan Xu, “Path Planning Technology for Unmanned Aerial Vehicle Swarm Based on Improved Jump Point Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 16(4), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160426

@article{Zhang2025,
title = {Path Planning Technology for Unmanned Aerial Vehicle Swarm Based on Improved Jump Point Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160426},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160426},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {4},
author = {Haizhou Zhang and Shengnan Xu}
}