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

TL;DR

This paper introduces a spherical vector-based particle swarm optimization (SPSO) algorithm for UAV path planning in complex environments, demonstrating superior performance over existing algorithms through extensive benchmarking and real-world validation.

Contribution

The paper proposes a novel SPSO algorithm tailored for UAV path planning, integrating environment constraints and outperforming existing metaheuristics in multiple scenarios.

Findings

SPSO outperforms classic PSO and other variants in most scenarios.

The generated paths are validated for real UAV operations.

Source code is publicly available for replication.

Abstract

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.