基于多AUV智群协同的三维水下传感网移动数据采集方法

Collaborative teamwork by multi-AUV intelligent swarm can realize mobile data collecting for 3D underwater sensor networks, so as to meet the needs of large-scale and low-latency monitoring for three-dimensional oceans. In order to achieve efficient data gathering of underwater 3D sensor networks, cooperative teamwork allocation scheduling and smooth trajectory planning for AUV intelligent swarm in complicated underwater three-dimensional space with randomly distributed obstacles are optimized in this proposal. This proposal aims to establish a mobile data gathering model using multi-AUV group with such constraints as AUVs’ dynamics and three-dimensional topology characteristics of underwater sensor networks, and then designs multi-hop based transmission manner between underwater sensor nodes within AUV’s coverage area. The main topics of this proposal include complex task allocation and optimization mechanism, 3D-G1/G2 continuous path planning method, intelligent multi-AUV collision and multi-obstacle avoidance mechanism, and etc. Moreover, we will seek optimization mechanism based on swarm intelligence coordination method in this project. In a word, this project will propose multi-AUV task allocation and path planning optimization model, 3D-G1/G2 continuous path planning algorithm for underwater multi-AUV, combined and multidimensional obstacle avoidance mechanism considering inter-AUVs access conflict and underwater obstacles avoidance, so as to provide an appropriate solution for mobile data collection in three-dimensional underwater sensor networks using multi-AUV intelligent swarm. Finally, the performance of proposed algorithms will be verified with NS-3 simulation paltform and practical test in water tank.

多AUV智群协同作业可实现水下三维传感器网络移动数据采集，满足立体海洋大尺度与低延时监测的需求。项目研究障碍物环境的复杂水下三维空间内，如何优化AUV智群的任务均衡分配与平滑轨迹规划，实现对水下传感器网络数据的高效移动采集。项目构建基于多AUV动力学约束和三维空间拓扑特征的水下传感器网络移动数据采集模型，设计AUV覆盖区域内水下节点多跳路由传输模式，研究水下三维网络空间中多AUV智群任务调度方案、满足3D-G1/G2连续的全局优化路径规划方法、多AUV巡航路径冲突与水下多障碍物避碰机制等主要内容。项目利用群智能协同方法寻求优化方案，提出多AUV任务调度与路径规划联合优化模型、满足3D-G1/G2连续的多路径规划方法、综合多AUV访问冲突规避与水下障碍物躲避的组合避碰机制。项目拟采用NS-3网络仿真平台和实际水池测试进行方法验证，为三维水下传感网中多AUV集群高效数据收集提供一种合适的方案。

针对水下无线传感器网络中多水下机器人移动数据采集、协同决策与编队作业等难题，项目主要研究了多水下机器人集群在三维水下传感网中的移动数据采集、路径规划与编队控制等内容：（1）构建了一种基于多AUV协同进行水下传感网络移动数据采集的理论模型，采用自组织映射（SOM）方法对多AUV协同优化模型进行求解；提出了一种基于平滑Dubins曲线的水下传感网络移动数据采集机制，建立了联合考虑数据采集与能量供给的移动采集优化模型；基于现有的深度路由协议（DBR），综合考虑水下节点的剩余能量和数据发送的延时性，研究了能量均衡与时延优化的数据转发机制。（2）提出了基于反步法与滑膜面的AUV姿态优化与路径控制方法，推导了控制律并验证了收敛性，实现了水下机器人姿态与路径的有效控制；将流体力学中的传统流函数方法扩展到三维空间，提出了基于三维流函数的避障算法和基于双目视觉的障碍目标定位机制，利用全自由度水下四旋翼机器人进行水池试验验证。（3）针对多AUV在水下传感网络的协同编队作业问题，构建了多AUV集群主从编队的数学模型和优化目标；借鉴反步法与自抗扰控制（ARDC）思想设计了新型的控制律函数，提高编队场景下移动数据采集的协同性；基于水声通信的多AUV编队控制策略在水库环境中进行了实测，验证了多水下机器人协同编队与切换功能；在多智能体系统理论方向，将水下机器人集群作为一种典型的多智能体系统，研究了新型的迭代学习控制方法。项目的主要成果如下：发表学术论文29篇，其中SCI收录14篇，EI收录期刊2篇，一级期刊7篇，会议论文4篇，会议特邀报告2次，授权国家发明专利5项。在项目执行期间，项目负责人获浙江省科技进步三等奖1项，新立项国家自然科学基金面上项目1项、浙江省自然基金重点项目1项。本项目的完成为多水下机器人在水下传感器网络中的高效作业提供了理论依据与技术支撑。