Unmanned surface vehicles (USVs) are of increasing importance to a growing number of sectors in the maritime industry, including offshore exploration, marine transportation and defence operations. A major factor in the growth in use and deployment of USVs is the increased operational flexibility that is offered through use of autonomous navigation systems that generate optimised trajectories. Unlike path planning in terrestrial environments, planning in the maritime environment is more demanding as there is need to assure mitigating action is taken against the significant, random and often unpredictable environmental influences from winds and ocean currents. With the focus of these necessary requirements as the main basis of motivation, this paper proposes a novel motion planner, denoted as GPMP2*, extending the application scope of the fundamental GP-based motion planner, GPMP2, into complex maritime environments. An interpolation strategy based on Monte-Carlo stochasticity has been innovatively added to GPMP2* to produce a new algorithm named GPMP2* with Monte-Carlo stochasticity (MC-GPMP2*), which can increase the diversity of the paths generated. In parallel with algorithm design, a ROS based fully-autonomous framework for an advanced unmanned surface vehicle, the WAM-V 20 USV, has been proposed. The practicability of the proposed motion planner as well as the fully-autonomous framework have been functionally validated in a simulated inspection missions for an offshore wind farm in ROS.
翻译:对海运业越来越多的部门来说,无人驾驶表面车辆(USVs)对海运业中越来越多的部门,包括近海勘探、海洋运输和国防行动,越来越重要。使用和部署USV2* 增长的一个主要因素是,通过使用能产生优化轨迹的自主导航系统,提高了业务灵活性,这增加了使用基于GP-机动规划器(GPMP2)的基本应用范围,进入复杂的海洋环境。与陆地环境的航道规划不同,海洋环境规划要求更高,因为需要确保对风流和洋流造成的重要、随机和往往不可预测的环境影响采取缓解行动。鉴于这些必要需求的重点是作为动力的主要基础,本文件提议建立一个新的运动规划器,称为GPMP2*,将基于GP-运动规划器(GPMP2)的基本应用范围扩大到复杂的海洋环境中。基于蒙特-卡罗的互异性的相互调战略已经创新地添加到GPMP2* 框架之中,以产生一种名为GPMP2** 的新算法,与蒙特-卡洛风流流流热(MC-GMP2* ),这可以增加道路的多样性。