Yuanchang Liu

Efficient multi-task allocation and path planning for unmanned surface vehicle in support of ocean operations

Abstract Presently, there is an increasing interest in the deployment of unmanned surface vehicles (USVs) to support complex ocean operations. In order to carry out these missions in a more efficient way, an intelligent hybrid multi-task allocation and path planning algorithm is required and has been proposed in this paper. In terms of the multi-task allocation, a novel algorithm based upon a self-organising map (SOM) has been designed and developed. The main contribution is that an adaptive artificial repulsive force field has been constructed and integrated into the SOM to achieve collision avoidance capability. The new algorithm is able to fast and effectively generate a sequence for executing multiple tasks in a cluttered maritime environment involving numerous obstacles. After generating an optimised task execution sequence, a path planning algorithm based upon fast marching square (FMS) is utilised to calculate the trajectories. Because of the introduction of a safety parameter, the FMS is able to adaptively adjust the dimensional influence of an obstacle and accordingly generate the paths to ensure the safety of the USV. The algorithms have been verified and evaluated through a number of computer based simulations and has been proven to work effectively in both simulated and practical maritime environments.

Aspects of a reliable autonomous navigation and guidance system for an unmanned surface vehicle

This paper describes a novel navigation and guidance (NG) system designed to address the issue of receiving unreliable navigational data considering an unmanned surface vehicles (USVs). In the NG system, a confidence rate determination method has been designed to identify the uncertainty of the acquired data. According to the confidence rate, the risks from inaccurate data can be properly analysed facilitating the system generating a more reliable guidance route. The route is calculated using a newly developed algorithm named the constrained FM*. The new NG system has been verified in simulation environments with results proving the effectiveness and capabilities of the system.