| 引用本文: | 朱大奇,曹翔.多个水下机器人动态任务分配和路径规划的信度自组织算法[J].控制理论与应用,2015,32(6):762~769.[点击复制] |
| ZHU Da-qi,CAO Xiang.An improved self-organizing map method for multiple autonomous underwater vehicle teams in dynamic task assignment and path planning[J].Control Theory and Technology,2015,32(6):762~769.[点击复制] |
|
| 多个水下机器人动态任务分配和路径规划的信度自组织算法 |
| An improved self-organizing map method for multiple autonomous underwater vehicle teams in dynamic task assignment and path planning |
| 摘要点击 2945 全文点击 1033 投稿时间：2014-10-26 修订日期：2015-03-07 |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| DOI编号 10.7641/CTA.2015.40996 |
| 2015,32(6):762-769 |
| 中文关键词 多AUV系统 自组织神经网络 动态任务分配 信度函数 避障 速度跳变 |
| 英文关键词 multi-AUV system self-organizing map method dynamic task assignment belief function obstacle avoidance speed jump |
| 基金项目 国家自然科学基金项目(51279098), 上海市科委创新行动计划项目(14JC1402800, 13510721400)资助. |
|
| 中文摘要 |
| 针对多个水下机器人(autonomous underwater vehicles, AUVs)动态任务分配和路径规划速度跳变问题,引入栅格信度函数概念, 给出一种改进的栅格信度自组织(belief function self-organizing map, BFSOM)算法. 目的是控制一组AUV有效地到达所有指定的目标位置, 同时保证AUV能够自动的避开障碍物. 首先, 自组织神经网络\linebreak(self-organizing map, SOM)算法对多AUV系统进行任务分配, 使得每个目标位置都有一个AUV去访问. 整个分配过程包括定义SOM神经网络的初始权值、获胜者选择、邻域函数的计算3个步骤; 其次, 根据栅格信度函数和环境信息更新SOM获胜神经元的权值, 使得每个AUV在访问对应目标的过程中能够自动避障并且克服速度跳变, 实现AUV自动有效路径规划. 最后, 通过仿真实验证明了本文提及算法的有效性. |
| 英文摘要 |
| For the speed jump problem of AUV (autonomous underwater vehicles) in task assignment and path planning, an improved self-organizing map (SOM) method is proposed by using the grid belief function. The purpose is to control a team of AUV to reach all appointed target locations for the premise of workload balance and energy sufficiency, while ensuring automatic obstacle avoidance. Firstly, the SOM neuron network is developed for assigning tasks to a team of AUV, so that each target location will be visited by an AUV. The working process includes specifically defining the initial weights of the SOM neural network, selecting the rule for the winner, and computing the neighborhood function. Then, to avoid the obstacle autonomously and to get rid of the navigation speed jump for each AUV in visiting the corresponding target, the belief function of location and direction about environmental information is used to update weights of the winner of SOM in realizing AUV path planning autonomously. Finally, to demonstrate the efficacy of the proposed approach, simulation results are given in this paper. |
|
|
|
|
|