Path Planning for Autonomous Underwater Vehicles Based on an Improved Artificial Jellyfish Search Algorithm in Multi-Obstacle Ocean Current Environment

Abstract

Path planning for autonomous underwater vehicles (AUVs) is a key research focus in the marine domain, requiring consideration of the underwater environment's complexity and the efficiency of the planning algorithms. Firstly, this paper uses the grid method to construct a realistic ocean terrain for the underwater environment and incorporates obstacles and ocean currents. Also, the AUV's movement strategy and posture are illustrated and constrained by a visual search strategy. Secondly, an improved artificial jellyfish search (IJS) algorithm is to overcome the drawbacks of low convergence accuracy and adaptability of the basic algorithm. The IJS algorithm determines the optimal path point by optimizing the objective function. This objective function not only takes into account factors such as path navigation time and path safety but also designs an ocean current disturbance model, which facilitates the AUV to successfully reach the target point while avoiding obstacles and strong side currents. Finally, the optimal smoothed path is output using a cubic spline midpoint interpolation method. Multiple simulation experiments are carried out on the constructed environment model. The comparison results show that the IJS algorithm with a short running time has the optimal time cost and ocean current penalty cost for the planned path. In addition, the IJS algorithm is also shown to be adaptable in the field of multi-AUV movements.

A Low-Cost and High-Precision Underwater Integrated Navigation System

“…For the search mission for underwater targets, the collaborative operation mode of USV range coarse scanning and AUV approach reconnaissance has the advantages of a high search efficiency, flexible deployment and real-time information transmission. The underwater environment has characteristics such as strong closure and complexity, so the underwater navigation system is the key to AUVs executing underwater navigation tasks [5][6][7]. The AUV navigation method can be subdivided into five main branches: dead reckoning, vision-aided navigation, sonar-aided navigation, map matching navigation and acoustic navigation.…”

An Optimal-Path-Planning Method for Unmanned Surface Vehicles Based on a Novel Group Intelligence Algorithm

“…To solve the problem of two-dimensional autonomous path planning for unmanned underwater vehicles (UUVs) in environments influenced by both ocean currents and obstacles, an improved Fireworks-Ant Colony Hybrid algorithm was proposed to establish a two-dimensional Lamb vortex ocean current environment model with randomly distributed obstacles, in which circular obstacles were equivalent to square grid cells [18], while considering energy consumption, travel time, and distance as reference factors in the cost function. Considering the complexity of the underwater environment and the efficiency of the planning algorithm, an improved artificial jellyfish search algorithm (IJS) was proposed, which integrated memory functions with multiple strategies to establish a target function that included ocean current disturbance models to avoid the impact of obstacles and strong lateral flow on AUV movement [19]. However, the actual energy loss, size, and attitude changes of AUVs were not considered.…”

Three-Dimensional Marine Ranching Cage Inspection Path Planning Integrating the Differential Evolution and Particle Swarm Optimization Algorithms

“…In recent years, many intelligent algorithms have been widely applied to underwater path planning for AUVs. These methods include the ant colony optimization (ACO) algorithm [9][10] [11] [12], tuna algorithm [13], whale algorithm [14][15] [16], grey wolf optimization algorithm [17] [18], artificial jellyfish search algorithm [19], water wave optimization algorithm [20], genetic algorithm (GA) [21] [22], and other methods [23].…”