| 引用本文: | 欧林林,陈浩,肖云涛,程诚,俞立.满足复杂要求的机器人最优巡回控制系统设计与实现[J].控制理论与应用,2016,33(2):172~180.[点击复制] |
| OU Lin-lin,CHEN Hao,XIAO Yun-tao,CHENG Cheng,YU Li.Design and implement of optimal patrolling control system to satisfy the complex requirements[J].Control Theory and Technology,2016,33(2):172~180.[点击复制] |
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| 满足复杂要求的机器人最优巡回控制系统设计与实现 |
| Design and implement of optimal patrolling control system to satisfy the complex requirements |
| 摘要点击 3228 全文点击 2016 投稿时间：2014-12-02 修订日期：2015-08-06 |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| DOI编号 10.7641/CTA.2016.41119 |
| 2016,33(2):172-180 |
| 中文关键词 路径规划 巡回控制 线性时序逻辑 模糊逻辑 |
| 英文关键词 motion planning patrolling control linear temporal logic fuzzy logic |
| 基金项目 国家自然科学基金项目(61273116), 浙江省自然科学基金项目(LY15F030015), 国家高新技术研究发展计划项目(2014AA041601–05), 机器人技术 与系统国家重点实验室开放基金项目(SKLRS–2013–MS–06)资助. |
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| 中文摘要 |
| 本文结合线性时序逻辑理论与模糊控制方法, 设计并实现了一种满足复杂任务需求的移动机器人巡回控
制系统, 它既能够针对复杂时序任务进行路径规划, 又能够对机器人进行模糊控制实现路径跟踪. 首先, 基于线性
时序逻辑理论, 确定能够满足复杂巡回任务需求的全局最优路径. 接着, 根据所获得的最优路径, 采用模糊控制方
法设计轨迹跟踪控制器, 使其通过实时位姿反馈对机器人进行路径跟踪控制. 仿真结果验证了移动机器人巡回控制
系统的有效性. 最后, 基于E-Puck移动机器人构建了能够满足复杂任务需求的移动机器人巡回控制实验系统. 基于
所提出的最优巡回路径规划算法和模糊控制器设计方法, 通过图像处理、数据通信、算法加载等软件模块的实现完
成了满足复杂任务需求的移动机器人巡回控制. |
| 英文摘要 |
| By combining the linear temporal logic theory and the fuzzy control method, a patrolling control system is
presented in this paper, which enables the robot to perform complex temporal tasks. It can not only search the optimal path
for complex sequential tasks, but also can realize the path tracking by using the fuzzy control method. Firstly, based on the
theory of linear temporal logic (LTL), the global optimal path satisfying the demand of complex task is determined. Then,
the fuzzy logic controller is designed for the trajectory tracking. It can actualize the optimal path tracking according to the
feedback of the real-time position and orientation of the robot. Additionally, the simulation results show the effectiveness
of the proposed patrolling control system. Finally, a patrolling control system satisfying the demand of complex task is
established on the basis of the E-Puck robot, which includes image processing, data communications, algorithm loading
and other software modules. The experiment shows that the complex patrolling control task is accomplished by using the
proposed optimal patrolling path planning algorithm and the fuzzy control method. |
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