| 引用本文: | 汪琴,张波涛,宋士吉.期望时间下的移动机器人目标搜索路径规划[J].控制理论与应用,2020,37(7):1451~1460.[点击复制] |
| WANG Qin,ZHANG Bo-tao,SONG Shi-ji.Path planning of target search for mobile robot with expected time[J].Control Theory and Technology,2020,37(7):1451~1460.[点击复制] |
|
| 期望时间下的移动机器人目标搜索路径规划 |
| Path planning of target search for mobile robot with expected time |
| 摘要点击 2737 全文点击 904 投稿时间：2019-06-18 修订日期：2020-05-10 |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| DOI编号 10.7641/CTA.2020.90462 |
| 2020,37(7):1451-1460 |
| 中文关键词 移动机器人 运动规划 期望时间优化 RRT 多目标搜索 |
| 英文关键词 mobile robot motion planning optimal expected-time RRT multi-target search |
| 基金项目 浙江省重点研发计划项目(2019C04018), 国家自然科学基金项目(61503108), 东莞市引进创新科技团队计划项目(2018607202007)资助. |
|
| 中文摘要 |
| 不确定环境下移动机器人目标搜索问题中, 目标在观测点被发现的概率常被设为理想的均匀分布, 其路径
优化指标通常为最短距离, 但最短距离路径不等同于最优期望时间路径. 针对此问题, 本文提出了一种以期望时间
为优化指标的概率多目标搜索算法. 针对观测点的访问顺序不同会导致期望时间不同的现象, 采用分层式路径优
化策略. 首先, 构造一个新的非均匀目标分布概率测算模型; 然后, 在上层序列规划中, 采用改进的改良圈算法生成
期望观测点序列; 最后, 在下层特征地图的观测点间可行路径规划中, 采用改进的快速随机生成树算法(GBC–RRT).
实验结果表明: 本文所提方法可显著缩短移动机器人目标搜索的期望时间, 且能在目标不确定、非均匀分布的工作
空间中得到最优期望时间的搜索路径. |
| 英文摘要 |
| In the target search problem of mobile robots under uncertain environment, the probability that a target is
found at an observation point is often set to an ideal uniform distribution, the path optimization index is usually the shortest
distance, and the shortest distance path is not equivalent to the optimal expected time path. For this issue, a probabilistic
multi-target search algorithm with expected time as index is proposed in this paper. Aiming at the phenomenon different
access order of nodes will lead to different expected-time, a hierarchical path optimization strategy is applied in path
planning. First, a new probability estimation model for non-uniform target distribution is constructed; and then an improved
circle modification algorithm is employed to generate the optimal expected observation point sequence at top level planning;
Finally, the goal biasing collision rapid-exploration random tree (GBC–RRT) algorithm is used in observation points at
lower level planning to realize a feasible path under feature maps. Simulation experiment results show that the method
proposed significantly reduces the expected-time for the mobile robot to search the target, and can obtain the optimal
expected-time path planning under uncertain and non-uniformly distributed working environments. |
|
|
|
|
|