Navigation Variable-based Multi-objective Particle Swarm Optimization for UAV Path Planning with Kinematic Constraints

TL;DR

This paper introduces NMOPSO, a novel multi-objective particle swarm optimization algorithm utilizing navigation variables for UAV path planning, effectively handling kinematic constraints and outperforming existing algorithms in simulations and real UAV tests.

Contribution

The paper presents a new path representation based on navigation variables and an adaptive mutation mechanism, improving multi-objective UAV path planning with kinematic constraints.

Findings

NMOPSO outperforms other PSO variants and metaheuristics.

The approach is validated with real UAV experiments.

Navigation variables effectively incorporate kinematic constraints.

Abstract

Path planning is essential for unmanned aerial vehicles (UAVs) as it determines the path that the UAV needs to follow to complete a task. This work addresses this problem by introducing a new algorithm called navigation variable-based multi-objective particle swarm optimization (NMOPSO). It first models path planning as an optimization problem via the definition of a set of objective functions that include optimality and safety requirements for UAV operation. The NMOPSO is then used to minimize those functions through Pareto optimal solutions. The algorithm features a new path representation based on navigation variables to include kinematic constraints and exploit the maneuverable characteristics of the UAV. It also includes an adaptive mutation mechanism to enhance the diversity of the swarm for better solutions. Comparisons with various algorithms have been carried out to benchmark the proposed approach. The results indicate that the NMOPSO performs better than not only other particle swarm optimization variants but also other state-of-the-art multi-objective and metaheuristic optimization algorithms. Experiments have also been conducted with real UAVs to confirm the validity of the approach for practical flights. The source code of the algorithm is available at https://github.com/ngandng/NMOPSO.