计算机应用 ›› 2019, Vol. 39 ›› Issue (9): 2529-2534.DOI: 10.11772/j.issn.1001-9081.2019030539

• 人工智能 • 上一篇    下一篇

基于能量约束的自主水下航行器任务规划算法

赵旭浩1,2,3,4, 王轶群1,2,3,4, 刘健1,2,3, 徐春晖1,2,3   

  1. 1. 中国科学院沈阳自动化研究所, 沈阳 110016;
    2. 机器人学国家重点实验室(中国科学院沈阳自动化研究所), 沈阳 110016;
    3. 中国科学院机器人与智能制造创新研究院, 沈阳 110016;
    4. 中国科学院大学, 北京 100049
  • 收稿日期:2019-04-03 修回日期:2019-04-30 出版日期:2019-09-10 发布日期:2019-05-21
  • 通讯作者: 王轶群
  • 作者简介:赵旭浩(1994-),男,山东济宁人,硕士研究生,主要研究方向:多AUV任务规划;王轶群(1985-),男,辽宁沈阳人,副研究员,硕士,主要研究方向:AUV导航及路径规划;刘健(1962-),男,辽宁沈阳人,研究员,硕士,主要研究方向:水下机器人导航与控制;徐春晖(1982-),男,辽宁沈阳人,副研究员,硕士,主要研究方向:水下机器人控制。
  • 基金资助:

    国家重点研发计划项目(2017YFC0306800)。

Task planning algorithm of multi-AUV based on energy constraint

ZHAO Xuhao<sup>1,2,3,4</sup>, WANG Yiqun<sup>1,2,3,4</sup>, LIU Jian<sup>1,2,3</sup>, XU Chunhui<sup>1,2,3</sup>   

  1. 1. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang Liaoning 110016, China;
    2. State Key Laboratory of Robotics(Shenyang Institute of Automation, Chinese Academy of Sciences), Shenyang Liaoning 110016, China;
    3. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang Liaoning 110016, China;
    4. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2019-04-03 Revised:2019-04-30 Online:2019-09-10 Published:2019-05-21
  • Supported by:

    This work is partially supported by the National Key Research and Development Program of China(2017YFC0306800).

摘要:

多水下自主航行器(AUV)任务规划是影响集群智能水平的关键技术。针对现有任务规划模型只考虑同构AUV集群和单潜次任务规划的问题,提出了适用于AUV异构集群的多潜次任务规划模型。首先,该模型考虑了AUV的能量约束、AUV多次往返母船充电的工程代价、异构集群个体间的效能差异、任务多样性等关键因素;然后,为提高问题模型的求解效率,提出了一种基于离散粒子群的优化算法,该算法引入用于描述粒子速度、位置的矩阵编码和用于评估粒子质量的任务损耗模型,改进粒子更新过程,实现了高效的目标寻优。仿真实验表明,该算法不仅解决了异构AUV集群的多潜次任务规划问题,而且与采用遗传算法的任务规划模型相比较,任务损耗降低了11%。

关键词: 自主水下航行器, 多AUV集群, 任务规划, 离散粒子群优化, 多样性任务

Abstract:

Autonomous Underwater Vehicle (AUV) task planning is the key technology that affects the level of cluster intelligence. In the existing task planning models, only the problem of homogeneous AUV cluster and single dive task planning are considered. Therefore, a multi-dive task planning model for AUV heterogeneous clusters was proposed. Firstly the model considered the energy constraints of AUV, the engineering cost of AUV multiple round-trip charging in mother ship, the efficiency difference between heterogeneous cluster individuals, and the diversity of tasks. Then in order to improve the efficiency of solving the problem model, an optimization algorithm based on discrete particle swarm was proposed. The algorithm introduced matrix coding for describing particle velocity and position and the task loss model for evaluating particle quality to improve the particle updating process, achieving efficient target optimization. Simulation experiments show that the algorithm not only solves the multi-dive task planning problem of heterogeneous AUV clusters, but also reduces the task loss by 11% compared with the task planning model using genetic algorithm.

Key words: Autonomous Underwater Vehicle (AUV), multi-AUV cluster, task planning, discrete particle swarm optimization, diverse task

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