| 引用本文: | 仲朝亮,刘士荣,张波涛.基于区域空间知识模型的在线快速路径规划[J].控制理论与应用,2015,32(3):357~365.[点击复制] |
| ZHONG Chao-liang,LIU Shi-rong,ZHANG Bo-tao.Online fast path-planning based on regionalized spatial knowledge model[J].Control Theory and Technology,2015,32(3):357~365.[点击复制] |
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| 基于区域空间知识模型的在线快速路径规划 |
| Online fast path-planning based on regionalized spatial knowledge model |
| 摘要点击 3050 全文点击 2467 投稿时间：2014-07-06 修订日期：2014-11-11 |
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| DOI编号 10.7641/CTA.2015.40631 |
| 2015,32(3):357-365 |
| 中文关键词 导航 由精到粗的寻路策略 区域空间知识模型 路径规划 |
| 英文关键词 navigation fine-to-coarse way-finding strategy regionalized spatial knowledge model path planning |
| 基金项目 国家自然科学基金项目(61175093, 61375104), 浙江省自然科学基金项目(LQ14F030012)资助. |
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| 中文摘要 |
| 人类在其导航过程中运用了区域化的空间知识模型并采取了“由精到粗”的寻路策略. 受此启发本文首先 提出一种区域化的空间知识模型. 在该模型中, 多个小尺度的区域组合在一起形成上一层级的区域, 构成一种层次 化的空间表示结构. 在此基础上提出一种基于该空间知识模型的在线路径规划算法FTC–A*(fine-to-coarse A*). FTC–A*能够根据环境信息的远近采取不同的规划策略. 在机器人所在的区域中, 进行路径的精细规划, 而对远处 空间进行粗糙规划. 该策略利用环境描述的区域化特性, 降低了搜索空间的大小, 从而显著地降低了规划时间和内 存负载, 减少了机器人的运动响应延迟. 本算法能适应环境规模巨大以及目标点经常改变的应用场合. 通过 在MobileSim平台的仿真实验以及与A*和HA*算法的对比分析, 验证了该方法的可行性与有效性. |
| 英文摘要 |
| Human beings use the regionalized spatial knowledge and adopt the ”fine-to-coarse” way-finding strategy in the process of navigation. Inspired by this, we put forward a regionalized spatial knowledge model. In this model, small scale regions are grouped together to form the bigger regions at the next hierarchy level which leads to a hierarchical spatial representation structure. Based on the spatial knowledge model, we develop a kind of online route-planning algorithm FTC– A*(fine-to-coarse A*) which can take different planning strategies according to the distance of environmental information. In the area where the robot stays, a fine route-planning will be conducted while for the distant space a coarse planning will be done. Taking advantage of regionalization feature of environment description, this strategy can shrink the search space; thus, remarkably reducing the planning time and the memory loading as well as lowering the motion response lags of the robot. The algorithm FTC–A* can be applied to occasions with huge number of environments or target-points change frequently Through the simulation experiment on MobileSim platform and the contrastive analysis of algorithms A* and HA*, we find the proposed method is feasible and effective. |
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