Abstract
This paper proposes an offline path planning method based on the Improved Quantum Particle Swarm Optimization (IQPSO) algorithm for Autonomous Underwater Vehicles (AUVs) in the underwater environment. The spherical modelling method is adopted to represent irregular underwater obstacles as spheres with a specified radius. Then, the IQPSO algorithm is developed to solve the problem of the limitations of the convergence and optimization ability of the traditional Quantum Particle Swarm Optimization (QPSO) algorithm and to identify the best path for AUVs. In this algorithm, to satisfy the three factors of path safety, path length and angle change of the path point, the fitness function is constructed to achieve multi-objective optimization. A smooth path is designed using the cubic spline interpolation algorithm. Different scenes or the same scene with different obstacles are designed to verify the effectiveness of the algorithm. The simulation results show that compared with PSO algorithm, QPSO algorithm, EGA algorithm and DENPSO algorithm, the path generated by IQPSO algorithm in various scenes is shorter, smoother and more stable.
Highlights
With the development of underwater applications, such as seabed exploration and underwater resource exploitation, Autonomous Underwater Vehicle (AUV) technology is increasingly common
The corresponding fitness functions are designed for the three factors of path length, angle change and path safety to achieve the multi-objective optimization of underwater AUV path planning
PARTICLE SWARM OPTIMIZATION Inspired by the foraging behaviour of birds, the Particle Swarm Optimization (PSO) algorithm was proposed by Eberhart and Kennedy [30] in 1995
Summary
With the development of underwater applications, such as seabed exploration and underwater resource exploitation, Autonomous Underwater Vehicle (AUV) technology is increasingly common. The A∗, D∗, Dijkstra and ACO algorithms are path planning methods based on graph theory This kind of method depends on building a grid model of the environment. The path planning method based on the intelligent evolutionary algorithm has been the most effective and fastest method This method can increase the convergence speed, avoid local optimal solutions and identify. The method achieves multi-objective optimization in path planning, it is only available in the 2D grid environment. Offline means non-real-time, that is, planning the route for an AUV prior to its operation This method does not require rasterized modelling of the underwater environment. The corresponding fitness functions are designed for the three factors of path length, angle change and path safety to achieve the multi-objective optimization of underwater AUV path planning.
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