Safety-enhanced UAV Path Planning with Spherical Vector-based Particle Swarm Optimization
This work addresses safety-critical path planning for UAV operations, but it is incremental as it builds on existing PSO methods with a new encoding approach.
The paper tackles UAV path planning in complex environments with multiple threats by proposing a spherical vector-based particle swarm optimization (SPSO) algorithm, which outperforms other PSO variants and metaheuristic algorithms like GA, ABC, and DE in most of eight benchmarking scenarios.
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.