多水下滑翔机海洋采样路径规划

提出了以时间为优化准则的多水下滑翔机海洋采样路径规划算法.首先,根据水下滑翔机的个数,通过空间聚类算法,把多水下滑翔机的路径规划问题转化为单水下滑翔机的路径规划问题;然后,利用2步CLK(chained Lin-Kernighan)算法,优化每一个采样子空间中的采样路径;设计了扩散算法,调节各个采样子空间的采样点,使得完成整个采样任务需要的时间最短;最后,以Sea-wing水下滑翔机为例,对上述方法进行了仿真验证,结果显示所提出的方法能够有效地降低采样作业的采样时间.     

多圆碟形水下滑翔机的双层协调三维路径规划

本文研究了欠驱动圆碟形水下滑翔机集群在海流干扰和水下碍航物影响下的三维路径规划问题. 具体地: 第一, 根据圆碟形水下滑翔机的航行特点, 建立了相应的航行时间模型, 设计了三维路径规划的优化目标; 第二, 提 出了一种基于双层协调的多水下滑翔机三维路径规划结构, 采用基于三维离散空间的全局路径规划和基于人工势 场法的局部路径规划, 避免了滑翔机与碍航物以及不同优先级的滑翔机之间发生碰撞; 第三, 基于双层协调路径规 划结构, 采用基于量子行为的自适应粒子群优化方法完成了时间最优目标下多圆碟形水下滑翔机的三维路径规划. 仿真结果验证了所提多圆碟形水下滑翔机三维路径规划方法的有效性.     

能耗最优的水下滑翔机采样路径规划

研究了以能耗为优化准则的水下滑翔机海洋环境参数采样路径规划方法.首先,根据水下滑翔机的运动特点,建立了水下滑翔机采样作业过程的能耗模型;其次,基于能耗模型提出了一种能耗最小的滑翔运动参数优化方法,该方法避免了求解复杂的混合整数非线性规划问题;之后,在能耗最优的滑翔运动参数优化基础上,提出了一种基于两步链式Lin-Ker...     

基于探索-利用权衡优化的Q学习路径规划

针对移动智能体在未知环境下的路径规划问题,提出了基于探索-利用权衡优化的Q学习路径规划.对强化学习方法中固有的探索-利用权衡问题,提出了探索贪婪系数ε值随学习幕数平滑衰减的εDBE(ε-decreasing based episodes)方法和根据Q表中的状态动作值判断到达状态的陌生/熟悉程度、做出探索或利用选择的Aε...     

水下滑翔机总体设计与运动分析

水下滑翔机是一种浮力驱动的新型水下无人运载器,但是一直以来较低的巡航速度是制约其广泛应用的一个关键因素。为提高水下滑翔机的巡航速度,首先应通过对水下滑翔机的壳体和滑翔翼进行优化设计,并且从做功的角度规划出最佳滑翔路径,设计了一种新型的高速、高效水下滑翔机。最后,通过运动弹道的仿真分析,验证了水下滑翔机设计方法的有效性和总体性能的优越性。     

帆船比赛航行路径优化与仿真

针对帆船在海上行驶所具有的模糊性和不定性的复杂环境特点,提出基于分区段优化和进化规划的帆船航行路径优化方法。该方法在比赛区域建立二维平面坐标系,将整个航程划分为若干区段,每个区段以帆船无约束的航向决策综合评价函数为基础,利用进化规划全局优化搜索进行路径优化,综合得到全航程最优路径。在此理论基础上,进行优化路径的仿真。仿真结果表明,该路径优化方法能够取得较好的规划结果,对于帆船科学训练具有很好的指导作用。     

基于前视声纳信息的AUV局部路径规划研究

针对水下环境的不确定性,建立了前视声纳的视域模型。主要采用强化学习的方法对自治水下机器人(AUV)进行控制和决策,综合Q学习算法、BP神经网络法、人工势场法对AUV进行局部路径规划。在AUV与环境的试错交互中,借助于来自成功与失败经验的奖励和惩罚值,不断改进水下机器人的自治能力。并设计了AUV局部路径规划器,实现AUV在不确定环境下的避障任务。半实物仿真证明了算法的可行性与可靠性。     

基于强化学习的多Agent路径规划方法研究

以复杂任务下多个智能体路径规划问题为研究对象,提出一种基于强化学习的多Agent路径规划方法。该方法采用无模型的在线Q学习算法,多个Agent不断重复"探索-学习-利用"过程,积累历史经验评估动作策略并优化决策,完成未知环境下的多Agent的路径规划任务。仿真结果表明,与基于强化学习的单Agent路径规划方法相比,该方法在多Agent避免了相碰并成功躲避障碍物的前提下,减少了17.4%的总探索步数,形成了到达目标点的最短路径。     

基于蚁群信息素辅助的Q学习路径规划算法

当Q学习应用于路径规划问题时,由于动作选择的随机性,以及Q表更新幅度的有限性,智能体会反复探索次优状态和路径,导致算法收敛速度减缓.针对该问题,引入蚁群算法的信息素机制,提出一种寻优范围优化方法,减少智能体的无效探索次数.此外,为提升算法初期迭代的目的性,结合当前栅格与终点位置关系的特点以及智能体动作选择的特性,设计Q表的初始化方法;为使算法在运行的前中后期有合适的探索概率,结合信息素浓度,设计动态调整探索因子的方法.最后,在不同规格不同特点的多种环境中,通过仿真实验验证所提出算法的有效性和可行性.     

海洋中尺度涡旋观测任务中的水下滑翔机协同控制策略

为了实现以水下滑翔机为平台,组队进行海洋中尺度涡旋的采样观测任务,提出了一种水下滑翔机队形协同控制方法．该控制方法可以使滑翔机能够完成以涡旋中心为圆心的预定圆形轨迹的循迹采样,同时在采样过程中能够使滑翔机保持任意预定的相对位置关系．首先,利用极坐标系对滑翔机的队形参数进行建模;然后根据队形参数提出对应的能量方程;最后通过最小化能量方程的导函数,制订相应的水下滑翔机航向控制律．通过建立仿真系统并对不同的轨迹参数进行仿真,在涡旋中心恒速运动和变速运动的情况下,载体均实现了对涡旋区域内不同半径圆形轨迹的跟踪采样观测任务,证明了该方法能够满足圆形预定轨迹上滑翔机队形保持的要求,为进行海洋中尺度涡旋区域组队观测采样提供了一种有效的队形控制方法．     

Differential evolution and underwater glider path planning applied to the short-term opportunistic sampling of dynamic mesoscale ocean structures

This paper presents an approach where differential evolution is applied to underwater glider path planning. The objective of a glider is to reach a target location and gather research data along its path by propelling itself underwater and returning periodically to the surface. The main hypothesis of this work is that gliders operational capabilities will benefit from improved path planning, especially when dealing with opportunistic short-term missions focused on the sampling of dynamic structures. To model a glider trajectory, we evolve a global underwater glider path based on the local kinematic simulation of an underwater glider, considering the daily and hourly sea currents predictions. The global path is represented by control points where the glider is expected to resurface for communication with a satellite and to receive further navigation instructions. Some well known differential evolution instance algorithms are then assessed and compared on 12 test scenarios using the proposed approach. Finally, a real case glider vessel mission was commanded using this approach.     

Toward Optimal Rendezvous of Multiple Underwater Gliders: 3D Path Planning with Combined Sawtooth and Spiral Motion

In this paper, a path planning system is proposed for optimal rendezvous of multiple underwater gliders in three-dimensional (3D) space. Inspired by the Dubins Paths consisting of straight lines and circular arcs, this paper presents the first attempt to extend the 3D Dubins curve to accommodate the characteristic glider motions include upwards and downwards straight glides in a sawtooth pattern and gliding in a vertical spiral. This modified 3D Dubins scheme is combined with genetic algorithm (GA), together with a rendezvous position selection scheme to find rendezvous trajectories for multiple gliders with minimal energy consumption over all participating vehicles. The properties and capabilities of the proposed path planning methodology are illustrated for several rendezvous mission scenarios. First, a simple application was performed for a single glider to rendezvous with a fix dock. Simulation results show the proposed planner is able to obtain more optimized trajectories when compared with the typical Dubins trajectory with nominal velocity. Additional representative simulations were run to analyse the performance of this path planner for multiple gliders rendezvous. The results demonstrate that the proposed path planner identifies the optimal rendezvous location and generates the corresponding rendezvous trajectories for multiple gliders that ensures they reach their destination with optimized energy consumption.     

Persistent ocean monitoring with underwater gliders: Adapting sampling resolution

Ocean processes are dynamic and complex and occur on multiple spatial and temporal scales. To obtain a synoptic view of such processes, ocean scientists collect data over long time periods. Historically, measurements were continually provided by fixed sensors, e.g., moorings, or gathered from ships. Recently, an increase in the utilization of autonomous underwater vehicles has enabled a more dynamic data acquisition approach. However, we still do not utilize the full capabilities of these vehicles. Here we present algorithms that produce persistent monitoring missions for underwater vehicles by balancing path following accuracy and sampling resolution for a given region of interest, which addresses a pressing need among ocean scientists to efficiently and effectively collect high‐value data. More specifically, this paper proposes a path planning algorithm and a speed control algorithm for underwater gliders, which together give informative trajectories for the glider to persistently monitor a patch of ocean. We optimize a cost function that blends two competing factors: maximize the information value along the path while minimizing deviation from the planned path due to ocean currents. Speed is controlled along the planned path by adjusting the pitch angle of the underwater glider, so that higher resolution samples are collected in areas of higher information value. The resulting paths are closed circuits that can be repeatedly traversed to collect long‐term ocean data in dynamic environments. The algorithms were tested during sea trials on an underwater glider operating off the coast of southern California, as well as in Monterey Bay, California. The experimental results show improvements in both data resolution and path reliability compared to previously executed sampling paths used in the respective regions. © 2011 Wiley Periodicals, Inc.     

构形可变移动机器人的构形评价、优选及变换规划

为提升移动机器人对环境的适应能力,提出了一种适用于车体构形、轮距和轮向可变的轮式移动机器人的构形评价、优选和变换规划方法。该方法以转弯所需半径、通过宽度和稳定角为指标评价不同构形下机器人的移动性和稳定性,然后构建权系数多目标模型从构形集合中优选与环境特征匹配的构形,再将任意两构形之间的变换规划转化为网络路径搜索问题来求解耗能最少的变换路径。最后,通过实验验证了所提评价指标、构形优选以及变换路径规划方法的合理性和有效性。研究结果可为此类构形可变轮式移动机器人的设计分析和运动规划提供指导或参考。     

A space-saving steering method for underwater gliders in lake monitoring

An increasing number of underwater gliders have been applied to lake monitoring. Lakes have a limited vertical space. Therefore, good space-saving capacity is required for underwater gliders to enlarge the spacing between monitoring waypoints. This paper presents a space-saving steering method under a small pitch angle (SPA) for appearance-fixed underwater gliders. Steering under an SPA increases the steering angle in per unit vertical space. An amended hydrodynamic model for both small and large attack angles is presented to help analyze the steering process. Analysis is conducted to find the optimal parameters of net buoyancy and roll angle for steering under an SPA. A lake trial with a prototype tiny underwater glider (TUG) is conducted to inspect the applicability of the presented model. The trial results show that steering under an SPA saves vertical space, unlike that under a large pitch angle. Simulation results of steering are consistent with the trial results. In addition, multiple-waypoint trial shows that monitoring with steering under an SPA covers a larger horizontal displacement than that without steering.     

基于局部流场构建的水下滑翔机路径规划

提出了一种基于局部流场构建的水下滑翔机路径规划方法．首先,基于历史剖面的深平均流对未来剖面的深平均流进行预测,并进行位置确定,然后将最前方若干周期的深平均流作为观测值,结合客观分析技术来构建局部流场．最后以构建的流场为基础,采用CTS-A^*（constant time surfacing A^*）迭代算法进行路径规划．在仿真环境下,分别利用该路径规划算法对单个流场和多个流场进行测试,并对结果进行了分析．实验结果表明,该路径规划算法适用于常规大小海流以及大海流情形．     

一种时变洋流场下AUV最优能耗路径规划方法

在时变洋流场环境下,洋流矢量增加了时间维度,在时间角度上可进一步利用洋流以节约自主水下机器人(AUV)能量消耗.此外,在该环境中无后效性不再成立,基于经典贪婪策略的路径规划算法不再适用.鉴于此,结合路径参数选择和双层规划算法,提出一种适用于时变洋流场环境的能耗最优路径规划算法.出发时间和AUV推进速度均可以在时间维度上等待有利洋流,且推进速度与其能量消耗直接相关,因此,引入出发时间和推进速度作为路径参数.在此基础上,针对无后效性不成立问题,使用双层规划作为路径规划算法,分析该算法在时变洋流场环境下的适用性.算法将路径规划任务分为路径规划与路径优化两部分,路径规划部分采用蚁群系统算法构建通道,路径优化部分由量子粒子群算法对路径参数进一步优化,在保证全局最优的同时能够解决传统基于栅格的路径规划算法中机器人运动方向受限的问题.最后以Kongsberg/Hydroid REMUS 600s型水下机器人为模型,对所提出的路径规划算法进行仿真验证.