基于已知地形信息的海底机器人路径规划
作者:
作者单位:

1.中国科学院大学;2.中国科学院沈阳自动化研究所

作者简介:

通讯作者:

中图分类号:

TP273

基金项目:

中国科学院海洋信息技术创新研究院前沿基础研究项目;十三五预研项目(2020107/2002).


Seabed Robot Path Planning Based on Priori Terrain Information
Author:
Affiliation:

1.University of Chinese Academy of Sciences;2.Shenyang Institute of Automation, Chinese Academy of Sciences

Fund Project:

Frontier Basic Research Project of Institute of Marine Information Technology Innovation, Chinese Academy of Sciences

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    摘要:

    路径规划是实现机器人智能化的重要组成部分,规划路径的优劣,很大程度上决定了机器人执行任务的效果. 传统的路径规划算法,例如基于图搜索的dijkstra算法和其改进后的A^*算法,以及基于采样的RRT算法和其改进后的RRT*算法,仅仅考虑避障问题;基于插值曲线的算法,可以产生较为光滑的轨迹,基于数值优化的算法可以将机器人速度、加速度等加入损失函数,通过优化求解,产生动力学特性较好的轨迹. 然而,面对当前越来越精确、丰富的先验地形信息,鲜有算法可以充分利用他们.本文基于海底数字高程地图(DEM),提出了扩展A*算法及FM算法改进算法,能够利用先验地形信息,提高路径规划的效果.通过仿真分析,对比了三种算法:扩展A*算法、TC FM和TC FM*算法,仿真表明,扩展A*算法求解速度更快、局部规划能力更强. TC FM、TC FM*算法求得路径更短、更光滑.

    Abstract:

    Path planning is an important part of realizing robot autonomous movement. The planned path largely determine the performance of the robot in work. Traditional path planning algorithms, such as the dijkstra algorithm which is based on graph search and it"s improved version the A^* algorithm, as well as the sampling-based RRT algorithm and it"s improved version RRT* algorithm, only consider obstacle avoidance problem; Algorithms which based on interpolation curves can generate smoother trajectories. Algorithms which based on numerical optimization can add robot speed, acceleration, etc. to the loss function, can generate trajectories with better dynamic properties through optimial solving. In current, topographic information are richer and more accurate, few algorithms can make full use of them. Based on the digital elevation map (DEM) of the seabed, this paper proposed extended A* algorithm and improved FM algorithm, which can use prior geographic information to improve the effect of path planning. Through simulation analysis, three algorithms are compared: extended A* algorithm, TC FM and TC FM* algorithm, simulations show that extended A* algorithm solves faster, the local planning ability is stronger, and the TC FM and TC FM* algorithm find the path shorter and smoother.

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  • 收稿日期:2021-03-01
  • 最后修改日期:2021-06-15
  • 录用日期:2021-06-17
  • 在线发布日期: 2021-07-01
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