Uninterrupted path planning system for Multi-USV sampling mission in a cluttered ocean environment

USV path planning method with velocity variation and global optimisation based on AIS service platform

Unmanned aerial vehicles (UAVs) are considered a promising example of an automatic emergency task in a dynamic marine environment. However, the maritime communication performance between UAVs and offshore platforms has become a severe challenge. Due to the complex marine environment, the task allocation and route planning efficiency of multiple UAVs in an intelligent ocean are not satisfactory. To address these challenges, this paper proposes an intelligent marine task allocation and route planning scheme for multiple UAVs based on improved particle swarm optimization combined with a genetic algorithm (GA-PSO). Based on the simulation of an intelligent marine control system, the traditional particle swarm optimization (PSO) algorithm is improved by introducing partial matching crossover and secondary transposition mutation. The improved GA-PSO is used to solve the random task allocation problem of multiple UAVs and the two-dimensional route planning of a single UAV. The simulation results show that compared with the traditional scheme, the proposed scheme can significantly improve the task allocation efficiency, and the navigation path planned by the proposed scheme is also optimal.

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Task allocation and route planning of multiple UAVs in a marine environment based on an improved particle swarm optimization algorithm

ABSTRACT Path planning is one of the key technologies in the research of autonomous surface vessels (ASVs). In this paper, an improved artificial fish swarm algorithm (IAFSA) is proposed. The algorithm is modified from four perspectives: (1) A directional operator is introduced to improve the efficiency. (2) To avoid local optimum, a probability weight factor is proposed to adjust the frequency of executing random behaviour. (3) An adaptive operator has been applied aims at better convergence performance. (4) The waypoint modifying path smoother is used to improve the path quality. A comparative study has been carried out between IAFSA and the other state-of-the-art algorithms, and the results indicate that the proposed algorithm outperforms the others in both efficiency and path quality. Finally, IAFSA is integrated into the GNC system in a model ship. A computer-based sea trial around the Nan Hai area has been conducted, and environmental disturbances including wind, waves, and currents are considered. The results have shown that IAFSA is suitable for practical application.

Route planning for autonomous vessels based on improved artificial fish swarm algorithm

Identification of Hammerstein systems finds extensive applications in solving the stability and design issues of non-linear dynamic systems. Hence, in this paper, a first attempt is made to solve the parameter estimation problem of Hammerstein non-linear systems by using the evolutionary optimisation algorithm (EOA) coupled Kalman filter (KF). The standard KF produces the best possible optimal states for the particular state estimation problem based on its properly tuned parameters θ^0−, P0−, φ, Q and R termed as initial state, initial error covariance, state transition, process noise covariance and measurement noise covariance, respectively. These parameters must be concurrently tuned to achieve a stable filtering operation. The manual and existing adaptive tuning methods do not guarantee optimality, convergence and also require a large amount of data. Moreover, the adaptive methods tune only a few statistics (Q and R) simultaneously. To defeat these foreknown problems, in the proposed method, initially, a recently established EOA named social mimic optimisation (SMO) algorithm is employed to get the global optimised KF parameters along with an efficient cost function that has been derived based on the model output for the concurrent achievement of all the KF parameters. Finally, the standard KF technique identifies the estimated parameters of the Hammerstein model with those optimised KF parameters. Various numerical and experimental test results demonstrate that the proposed SMO-based KF approach offers high accuracy in estimating the parameters along with the good convergence speed compared to the standard KF, other presented EOA-based KF approaches and recently reported techniques. 

Improving the modelling efficiency of Hammerstein system using Kalman filter and its parameters optimised using social mimic algorithm: Application to heating and cascade water tanks

The hybrid path planning algorithm based on improved A* and artificial potential field for unmanned surface vehicle formations

A new multi-sensor hierarchical data fusion algorithm based on unscented Kalman filter for the attitude observation of the wave glider

Turning Maneuverability is an important hydrodynamic performance of the wave glider, which can realize the position keeping, path tracking and steering obstacle avoidance in a reliable way. The inf...

Fen Liu

Papers

The hybrid path planning algorithm based on improved A* and artificial potential field for unmanned surface vehicle formations

A new multi-sensor hierarchical data fusion algorithm based on unscented Kalman filter for the attitude observation of the wave glider

Research on turning maneuverability of submerged glider of the wave glider