# Safety-enhanced UAV Path Planning with Spherical Vector-based Particle Swarm Optimization

arXiv: https://arxiv.org/abs/2104.10033 · HTML: https://mlsanity.com/paper/2104.10033/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 49 references that are in the map.
- First posted on arXiv 13 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [Enhanced discrete particle swarm optimization path planning for UAV vision-based surface inspection](https://mlsanity.com/paper/1706.04399/) (2017)
- [Influence of Initialization on the Performance of Metaheuristic Optimizers](https://mlsanity.com/paper/2003.03789/) (2020)
- [Angle-Encoded Swarm Optimization for UAV Formation Path Planning](https://mlsanity.com/paper/1812.07873/) (2018)
- [System Architecture for Real-time Surface Inspection Using Multiple UAVs](https://mlsanity.com/paper/1907.03305/) (2019)
- [Motion-Encoded Particle Swarm Optimization for Moving Target Search Using UAVs](https://mlsanity.com/paper/2010.02039/) (2020)

## Built on by (most cited first, 9 of 9)

- [UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility](https://mlsanity.com/paper/2501.02341/) (2025)
- [An LLM-based Framework for Human-Swarm Teaming Cognition in Disaster Search and Rescue](https://mlsanity.com/paper/2511.04042/) (2025)
- [Synthetic Aperture Sensing for Occlusion Removal with Drone Swarms](https://mlsanity.com/paper/2212.14692/) (2022)
- [Navigation Variable-based Multi-objective Particle Swarm Optimization for UAV Path Planning with Kinematic Constraints](https://mlsanity.com/paper/2501.03261/) (2025)
- [Enhanced Teaching-Learning-based Optimization for 3D Path Planning of Multicopter UAVs](https://mlsanity.com/paper/2205.15913/) (2022)
- [Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems](https://mlsanity.com/paper/2206.07655/) (2022)
- [UAV 3-D path planning based on MOEA/D with adaptive areal weight adjustment](https://mlsanity.com/paper/2308.10307/) (2023)
- [ERA*: Enhanced Relaxed A* algorithm for Solving the Shortest Path Problem in Regular Grid Maps](https://mlsanity.com/paper/2308.10988/) (2023)
- [Safe Path Planning and Observation Quality Enhancement Strategy for Unmanned Aerial Vehicles in Water Quality Monitoring Tasks](https://mlsanity.com/paper/2512.21375/) (2025)

## Abstract (arXiv)

This paper presents a new algorithm named spherical vector-based particle swarm optimization (SPSO) to deal with the problem of path planning for unmanned aerial vehicles (UAVs) in complicated environments subjected to multiple threats. A cost function is first formulated to convert the path planning into an optimization problem that incorporates requirements and constraints for the feasible and safe operation of the UAV. SPSO is then used to find the optimal path that minimizes the cost function by efficiently searching the configuration space of the UAV via the correspondence between the particle position and the speed, turn angle and climb/dive angle of the UAV. To evaluate the performance of SPSO, eight benchmarking scenarios have been generated from real digital elevation model maps. The results show that the proposed SPSO outperforms not only other particle swarm optimization (PSO) variants including the classic PSO, phase angle-encoded PSO and quantum-behave PSO but also other state-of-the-art metaheuristic optimization algorithms including the genetic algorithm (GA), artificial bee colony (ABC), and differential evolution (DE) in most scenarios. In addition, experiments have been conducted to demonstrate the validity of the generated paths for real UAV operations. Source code of the algorithm can be found at https://github.com/duongpm/SPSO.