Optimal search path planning of UUV in battlefeld ambush scene

Aiming at the practical application of Unmanned Underwater Vehicle(UUV)in underwater combat,this paper proposes a battlefield ambush scene with UUV considering ocean current.Firstly,by establishing these mathematical models of ocean current environment,target movement,and sonar detection,the probability calculation methods of single UUV searching target and multiple UUV cooperatively searching target are given respectively.Then,based on the Hybrid Quantum-behaved Particle Swarm Optimization(HQPSO)algorithm,the path with the highest target search probability is found.Finally,through simulation calculations,the influence of different UUV parameters and target parameters on the target search probability is analyzed,and the minimum number of UUVs that need to be deployed to complete the ambush task is demonstrated,and the optimal search path scheme is obtained.The method proposed in this paper provides a theoretical basis for the practical application of UUV in the future combat.

Generative Adversarial Network Based Heuristics for Sampling-Based Path Planning

Sampling-based path planning is a popular methodology for robot path planning.With a uniform sampling strategy to explore the state space,a feasible path can be found without the complex geometric modeling of the configuration space.However,the quality of the initial solution is not guaranteed,and the convergence speed to the optimal solution is slow.In this paper,we present a novel image-based path planning algorithm to overcome these limitations.Specifically,a generative adversarial network(GAN)is designed to take the environment map(denoted as RGB image)as the input without other preprocessing works.The output is also an RGB image where the promising region(where a feasible path probably exists)is segmented.This promising region is utilized as a heuristic to achieve non-uniform sampling for the path planner.We conduct a number of simulation experiments to validate the effectiveness of the proposed method,and the results demonstrate that our method performs much better in terms of the quality of the initial solution and the convergence speed to the optimal solution.Furthermore,apart from the environments similar to the training set,our method also works well on the environments which are very different from the training set.

Three-Layer Multi-UAVs Path Planning Based on ROBL-MFO

This paper proposes a new three-layer path planning method,where we fused two existing path planning methods(global path and local path)into a single problem for multi-unmanned aerial vehicles(UAVs)path planning for UAV.The global-path network layer contains the latest information and algorithms for global planning according to specific applications.The trajectory planning layer represents the kinematics and different motion characteristics,the planningexecution layer implements the local planning algorithm for obstacle avoidance.In the last layer,we propose a new swarm intelligence algorithm called the refraction principle and opposite-based-learning moth flame optimization(ROBL-MFO).In contrast to the classical MFO,the proposed algorithm addresses the shortcoming of the classical MFO algorithm.First,it adapts the moth position update formula to the notion of historical optimal flame average and improves the convergence speed of the algorithm.Second,it utilizes a random inverse learning strategy to narrow down the search space.Finally,the principle of refraction gives the algorithm the ability to jump out of local optima and helps the algorithm avoid premature convergence.The experimental results show that the performance of the proposed algorithm is versatile,robust,and stable.