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.     

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...     

不确定海流环境下水下机器人最优时间路径规划

为解决海流预测不精确条件下,现有基于确定性海流路径规划算法鲁棒性差和规划的路径有可能为不可行路径的问题,本文提出一种基于区间优化的水下机器人(AUV)最优时间路径规划算法.该算法采用双层架构,外层用蚁群系统算法(ACS)寻找由起点至终点的候选路径;内层以区间海流为环境模型,计算候选路径航行时间上下限,并分别通过区间序关系和基于可靠性的区间可能度模型将航行时间区间转换为确定性评价函数,并将评价函数值作为候选路径适应度值返回到外层算法.仿真结果表明,相对于确定海流场路径规划方案,提出的方案增强了路径规划器的鲁棒性并解决了结果路径不可行问题.     

Underwater glider design based on dynamic model analysis and prototype development

Underwater gliders are efficient mobile sensor platforms that can be deployed for months at a time, traveling thousands of kilometers. Here, we describe our development of a coastal 200 m deep underwater glider, which can serve as an ocean observatory platform operating in the East China Sea. Our glider is developed based on dynamic model analysis: steady flight equilibrium analysis gives the varied range of moving mass location for pitch control and the varied vehicle volume for buoyancy control; a stability analysis is made to discuss the relationship between the stability of glider motion and the location of glider wings and rudder by root locus investigation of glider longitudinal- and lateral-directional dynamics, respectively. There is a tradeoff between glider motion stability and control authority according to the specific glider mission requirements. The theoretical analysis provides guidelines for vehicle design, based on which we present the development progress of the Zhejiang University (ZJU) glider. The mechanical, electrical, and software design of the glider is discussed in detail. The performances of glider key functional modules are validated by pressure tests individually; preliminary pool trials of the ZJU glider are also introduced, indicating that our glider functions well in water and can serve as a sensor platform for ocean sampling.     

Analysis of adaptive sampling techniques for underwater vehicles

A critical problem in planning sampling paths for autonomous underwater vehicles (AUVs) is correctly balancing two issues. First, obtaining an accurate scalar field estimation and second, efficiently utilizing the stored energy capacity of the sampling vehicle. Adaptive sampling approaches can only provide solutions when real time and a priori environmental data is available. In this paper we present an analysis of adaptive sampling methodologies for AUVs. In particular, we analyze various sampling path strategies including systematic and stratified random patterns within a wide range of sampling densities and their impact in the energy consumption of the vehicle through a cost-evaluation function. Our study demonstrates that a systematic spiral sampling path strategy is optimal for high-variance scalar fields for all sampling densities and low-variance scalar fields when sampling is sparse. In addition, our results show that the random spiral sampling path strategy is found to be optimal for low-variance scalar fields when sampling is dense.     

浮力驱动式水下滑翔机运动仿真

浮力驱动式水下滑翔机对我国海洋勘探和国防建设有着重要应用前景,根据水下滑翔机三维运动模型,以MATLAB/Simulink为平台完成了滑翔机建模和图形界面仿真工具的设计。并以此仿真软件进行算例计算。该仿真成本低廉,可广泛应用于一般的水下滑翔机设计方案验证。     

Data‐driven learning and planning for environmental sampling

Robots such as autonomous underwater vehicles (AUVs) and autonomous surface vehicles (ASVs) have been used for sensing and monitoring aquatic environments such as oceans and lakes. Environmental sampling is a challenging task because the environmental attributes to be observed can vary both spatially and temporally, and the target environment is usually a large and continuous domain whereas the sampling data are typically sparse and limited. The challenges require that the sampling method must be informative and efficient enough to catch up with the environmental dynamics. In this paper, we present a planning and learning method that enables a sampling robot to perform persistent monitoring tasks by learning and refining a dynamic “data map” that models a spatiotemporal environment attribute such as ocean salinity content. Our environmental sampling framework consists of two components: To maximize the information collected, we propose an informative planning component that efficiently generates sampling waypoints that contain the maximal information; to alleviate the computational bottleneck caused by large‐scale data accumulated, we develop a component based on a sparse Gaussian process whose hyperparameters are learned online by taking advantage of only a subset of data that provides the greatest contribution. We validate our method with both simulations running on real ocean data and field trials with an ASV in a lake environment. Our experiments show that the proposed framework is both accurate in learning the environmental data map and efficient in catching up with the dynamic environmental changes.     

基于GPRS的水下滑翔机器人监控系统设计

水下滑翔机器人具有续航能力强、作业时间长等特点，适合于大范围海洋环境监测应用，可用于构建近海海洋环境立体监测网络。利用近海较好的GPRS网络覆盖条件，设计了基于GPRS网络的近海水下滑翔机器人监控系统。该监控系统不仅实现了在现场监控中心对水下滑翔机器人的监控，还可以通过Internet网络对水下滑翔机器人进行远程监控。文章详细介绍了监控系统的总体架构、GPRS终端硬件设计和系统软件设计。     

A twofold-interpolation-based path planning algorithm and its path following based on improved virtual vehicle method

In this paper, an interpolation-based path planning algorithm is employed for generating smooth paths on uniform resolution grid-maps. First, it starts at the goal node and propagates through four neighboring nodes, assigning monotonically increasing values to nodes using FMM (Fast Marching Method) interpolation. Consequently, we obtain a goal-propagation map that is zero-cost at the goal node and monotonically increasing along the wavefront propagation from the goal node. Subsequently, it begins from the robot’s position and uses a linear interpolation approach to generate nearoptimal paths. After obtaining the planned path, an improved path following algorithm based on an improved virtual vehicle method is employed to follow the path considering the robot’s dynamic and kinematic constraints. The experimental results demonstrate the performance of our approach.     

基于动态变采样区域RRT的无人车路径规划

针对无人车传统RRT路径规划算法节点搜索盲目性、随机性以及路径曲折不连续等问题,提出一种动态变采样区域RRT路径规划算法(dynamic variable sampling area RRT, DVSA-RRT).首先,初始化地图信息,根据动态变采样区域公式划分采样空间,进而选择采样区域;在此基础上,利用基于安全距离的碰撞检测、概率目标偏置策略和多级步长扩展完成初始路径规划;最后,利用考虑最大转角约束的逆向寻优和3次B样条曲线对初始路径进行拟合优化.仿真结果表明,该算法相较于原始RRT算法在不同地图环境下的搜索时间和采样次数均降低50%以上,大大降低了节点搜索的盲目性和随机性,相较于其他算法搜索时间也减少30%以上,且优化后的路径平滑满足车辆运动动力学约束.     

混合驱动水下滑翔器主从互转式应急控制技术研究

水下滑翔器是一种长续航新型水下机器人,利用其高效的驱动方式能够航行数月,因而相比其他无人水下自主航行器,滑翔器控制系统的可靠性显得尤为关键。根据水下滑翔器长续航的工作需求,结合其分布式控制系统的架构形式,设计了一种主从互转式应急控制技术。通过CPU互监控以及建立公共存储区等手段,实现了控制器异常情况下的非复位式热切换,保证了滑翔器重要动作部件的正常运行。试验结果表明,利用主从互转式控制方式,可以极大的减小了控制系统中主CPU宕机对滑翔器自主运行的影响,增加水下滑翔器的安全性能。     

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。     

基于Q学习的水下滑翔机路径规划方法

针对水下滑翔机路径规划问题，提出了一种基于Q学习的水下滑翔机路径规划方法。考虑到水下滑翔机在执行一些特定任务时会提前给定俯仰角及深度参数，且航向角选择范围通常是几个离散角度值，本文针对典型的几种俯仰角情况分别设计了航向动作选择集，这避免了Q学习方法“维数爆炸”问题。根据水下滑翔机航程最短的目标和障碍物外部约束条件，设计了奖励函数与动作选择策略。相较于传统路径规划方法，本文提出的方法不需要提前知道环境信息，而是在学习过程中根据环境的反馈选择最优动作，因此该方法在不同的环境条件下有优良的迁移能力。仿真结果表明，该方法能在未知环境中为水下滑翔机规划出规避障碍且航程短的路径。     

Multi-AUV SOM task allocation algorithm considering initial orientation and ocean current environment

There is an ocean current in the actual underwater working environment. An improved self-organizing neural network task allocation model of multiple autonomous underwater vehicles (AUVs) is proposed for a three-dimensional underwater workspace in the ocean current. Each AUV in the model will be competed, and the shortest path under an ocean current and different azimuths will be selected for task assignment and path planning while guaranteeing the least total consumption. First, the initial position and orientation of each AUV are determined. The velocity and azimuths of the constant ocean current are determined. Then the AUV task assignment problem in the constant ocean current environment is considered. The AUV that has the shortest path is selected for task assignment and path planning. Finally, to prove the effectiveness of the proposed method, simulation results are given.

     

Adaptive dynamic collision checking for single and multiple articulated robots in complex environments

Static collision checking amounts to testing a given configuration of objects for overlaps. In contrast, the goal of dynamic checking is to determine whether all configurations along a continuous path are collision-free. While there exist effective methods for static collision detection, dynamic checking still lacks methods that are both reliable and efficient. A common approach is to sample paths at some fixed, prespecified resolution and statically test each sampled configuration. But this approach is not guaranteed to detect collision whenever one occurs, and trying to increase its reliability by refining the sampling resolution along the entire path results in slow checking. This paper introduces a new method for testing path segments in c-space or collections of such segments, that is both reliable and efficient. This method locally adjusts the sampling resolution by comparing lower bounds on distances between objects in relative motion with upper bounds on lengths of curves traced by points of these moving objects. Several additional techniques and heuristics increase the checker's efficiency in scenarios with many moving objects (e.g., articulated arms and/or multiple robots) and high geometric complexity. The new method is general, but particularly well suited for use in probabilistic roadmap (PRM) planners, where it is critical to determine as quickly as possible whether given path segments collide, or not. Extensive tests, in particular on randomly generated path segments and on multisegment paths produced by PRM planners, show that the new method compares favorably with a fixed-resolution approach at "suitable" resolution, with the enormous advantage that it never fails to detect collision.     

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．     

Combustion-enabled underwater vehicles (CUVs) in dynamic fluid environment

Underwater vehicles have opened a unique path to multifunctionality and environmental adaptability. However, inadequate studies have been conducted to investigate the dynamic principle and performance of underwater vehicles in real applications with complex external conditions. Here, we propose a type of combustion-enabled underwater vehicles that can perform stable high-speed motions under dynamic fluid environment. Experiments are conducted to test the kinematic performance. Numerical simulations are developed to investigate the fluid–solid interaction phenomenon, and theoretical modeling is derived to study the dynamic principle of the combustion actuation process. The experimental, numerical, and theoretical results are compared with satisfactory agreements. The underwater vehicles perform ~3.4 body-length distance within 0.2 s and a maximum speed of ~30 body-length per second in horizontal direction. Parametric studies are conducted to investigate the sensitivity of the key factors to the kinematic performance of the reported underwater vehicles. In the end, we report the hybrid combustion-enabled underwater vehicles (CUVs) that combined with propeller to realize continuous driving for multi-mode operations. The experimental, numerical, and theoretical results indicate that hybrid CUVs can achieve more flexible and controllable motion performance.     

An Integral Framework of Task Assignment and Path Planning for Multiple Unmanned Aerial Vehicles in Dynamic Environments

In this paper, a hierarchical framework for task assignment and path planning of multiple unmanned aerial vehicles (UAVs) in a dynamic environment is presented. For multi-agent scenarios in dynamic environments, a candidate algorithm should be able to replan for a new path to perform the updated tasks without any collision with obstacles or other agents during the mission. In this paper, we propose an intersection-based algorithm for path generation and a negotiation-based algorithm for task assignment since these algorithms are able to generate admissible paths at a smaller computing cost. The path planning algorithm is also augmented with a potential field-based trajectory replanner, which solves for a detouring trajectory around other agents or pop-up obstacles. For validation, test scenarios for multiple UAVs to perform cooperative missions in dynamic environments are considered. The proposed algorithms are implemented on a fixed-wing UAVs testbed in outdoor environment and showed satisfactory performance to accomplish the mission in the presence of static and pop-up obstacles and other agents.     

基于模式转变蚁群算法的波浪滑翔机路径规划

随着陆地资源短缺,环境恶化等问题的日益严峻,海洋资源的开发成为缓解陆地资源匮乏的重要途径。这时基于海洋观测大尺度长时序以及恶劣环境等特点的波浪滑翔机应运而生,波浪滑翔机在海洋环境中的路径规划也至关重要。具有良好的正反馈机制和环境互动性的蚁群算法作为波浪滑翔机的路径规划算法,让个体在不同时期根据自身位置和环境的不同来执行不同的方案,并在状态转移公式中加入可以模拟实际环境的海洋环境因素及方向记忆因子,通过不同个体的自身情况来筛选出最佳个体并规划出高效合理的最佳航线。结果表明,与传统蚁群算法相比,给出的改进蚁群算法在规划路径方面效率更高、用时更短,而且可以根据海洋环境的不同及时调节并作出最合理的航线规划。     

基于改进Xmodem协议的水下滑翔机通信系统设计

水下滑翔机是一种依靠重心和自身净浮力变化驱动航行的新型水下机器人。为满足其长时间大尺度海洋调查的要求,设计了一种无线电和铱星卫星通信相结合的通信系统,并根据通信系统在海洋环境下通信链路不稳定的特点,设计了一种改进Xmodem通信协议,以保证水下滑翔机在海洋调查过程中通信的有效性和可靠性。通过海上实验表明:该通信系统和改进Xmodem协议可以满足水下滑翔机上传实验数据的应用需求。