[1]曹翔,俞阿龙.移动机器人全覆盖信度函数路径规划算法[J].智能系统学报,2018,13(2):314-321.[doi:10.11992/tis.201610006]
CAO Xiang,YU Along.Complete-coverage path planning algorithm of mobile robot based on belief function[J].CAAI Transactions on Intelligent Systems,2018,13(2):314-321.[doi:10.11992/tis.201610006]
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CAO Xiang,YU Along.Complete-coverage path planning algorithm of mobile robot based on belief function[J].CAAI Transactions on Intelligent Systems,2018,13(2):314-321.[doi:10.11992/tis.201610006]
移动机器人全覆盖信度函数路径规划算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
13
期数:
2018年第2期
页码:
314-321
栏目:
学术论文—智能系统
出版日期:
2018-04-15
- Title:
- Complete-coverage path planning algorithm of mobile robot based on belief function
- Keywords:
- mobile robot; complete-coverage path planning; direction belief function; grid belief function; repetition rate
- 分类号:
- TP273
- 摘要:
- 针对移动机器人全覆盖路径规划问题，给出一种基于栅格信度函数的全覆盖路径规划算法。目的是为了控制移动机器人能够遍历工作区域中所有的可到达点，同时保证能够自动避开障碍物。首先，根据环境的信息对栅格地图进行赋值，使用不同的函数值表示障碍物、已覆盖栅格和未覆盖栅格；其次，判断机器人是否陷入死区引入不同方向信度函数，对栅格函数值进行调整；最后，机器人根据栅格信度函数值规划覆盖路径。本文所提及的算法不仅能够引导移动机器人实现工作区域的全覆盖而且能够快速逃离死区，实现覆盖路径的低重复率。仿真实验中，通过与生物启发神经网络算法的比较，证明本文提及算法有更高的覆盖效率。
- Abstract:
- For the complete-coverage path planning of mobile robot, an algorithm based on grid belief function was proposed. The goal is to control a mobile robot to traverse all reachable locations in work area, while guarantee automatic obstacle avoidance. Firstly, the grid map is assigned with values according to the information of the environment, the obstacles, covered grids and uncovered grids are represented by using different function values; secondly, judging if a mobile robot is caught in dead zone or not, different direction belief functions are introduced to adjust the grid function values; lastly, the robot programs the covered path according to the grid belief function value. The proposed algorithm can guide a robot to realize full coverage of work area, rapidly escape from dead zone and achieve a low repetition rate of the covered path. In the simulation experiments, by compared with bio-inspired neural network algorithm, the algorithm proposed in the paper was verified to own high coverage efficiency.
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备注/Memo
收稿日期:2016-10-09。
基金项目:江苏省高校自然科学研究重大项目（16KJA460003）.
作者简介:曹翔,男,1981年,讲师,博士,主要研究方向为机器人搜索、围捕、路径规划;俞阿龙,男,1964年,教授,博士,主要研究方向为测控技术、传感器技术、应用电子技术。
通讯作者:曹翔.E-mail:cxeffort@126.com.
更新日期/Last Update:
1900-01-01