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摘要:
针对移动机器人环境认知问题, 受老鼠海马体位置细胞在特定位置放电的启发, 构建动态增减位置细胞认知地图模型(Dynamic growing and pruning place cells-based cognitive map model, DGP-PCCMM), 使机器人在与环境交互的过程中自组织构建认知地图, 进行环境认知. 初始时刻, 认知地图由初始点处激活的位置细胞构成; 随着与环境的交互, 逐渐得到不同位置点处激活的位置细胞, 并建立其之间的连接关系, 实现认知地图的动态增长; 如果机器人在已访问区域发现新的障碍物, 利用动态缩减机制对认知地图进行更新. 此外, 提出一种位置细胞序列规划算法, 该算法以所构建的认知地图作为输入, 进行位置细胞序列规划, 实现机器人导航. 为验证模型的正确性和有效性, 对Tolman的经典老鼠绕道实验进行再现. 实验结果表明, 本文模型能使机器人在与环境交互的过程中动态构建并更新认知地图, 能初步完成对Tolman老鼠绕道实验的再现. 此外, 进行了与四叉树栅格地图、动态窗口法的对比实验和与其他认知地图模型的讨论分析. 结果表明了本文方法在所构建地图的简洁性、完整性和对动态障碍适应性方面的优势.
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关键词:
- 移动机器人 /
- 环境认知 /
- 海马体位置细胞 /
- 导航 /
- Tolman老鼠绕道实验
Abstract:Aiming at environmental cognition problem of mobile robot, inspired by the activation of hippocampal place cells in particular regions, a dynamic growing and pruning place cells-based cognitive map model (DGP-PCCMM) is established, which enables robot to construct the cognitive map self-organizingly by interacting with the environment and to implement environmental cognition. In the beginning, cognitive map consists of the activated place cell responding to current region; With the interaction with the environment, the responding activated place cells at different regions are gradually obtained, and the relationship among them is established, thus realizing the dynamic growing of the cognitive map; If new obstacles are discovered in the visited area, the cognitive map is updated using dynamic pruning mechanism. Besides, a sequence planning algorithm of place cells is proposed to realize robot navigation, which uses the constructed cognitive map as input. To verify the correctness and validity of the model, the classical Tolman detour task was reproduced. Results show that the model can enable robot to construct and update the cognitive map dynamically in the process of interacting with the environment, and to complete the reproduction of the Tolman detour task substantially. In addition, comparative experiments with occupancy grids, dynamic window approach and discussion about other cognitive map models are carried out, and results show the advantages of the proposed methods in the aspects of simplicity, completeness of the constructed cognitive maps and adaptability to dynamic obstacles.
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Key words:
- Mobile robot /
- environmental cognition /
- hippocampal place cells /
- navigation /
- Tolman detour task
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图 9 认知地图构建过程(
$ n_m $ = 1 000)Fig. 9 The formation process of cognitive map (
$ n_m $ = 1 000)
图 10 认知地图构建过程(
$ n_m $ = 2 000)Fig. 10 The formation process of cognitive map (
$ n_m $ = 2 000)
图 11 位置细胞个数随学习次数变化情况
Fig. 11 The number of place cells changing with the number of learning times
图 18 动态窗口法和本文方法导航结果对比图
Fig. 18 Comparation of navigation results between dynamic window approach and DGP-PCCMM
表 1 DGP-PCCMM初始参数设置
Table 1 Initial simulation parameters for DGP-PCCMM
参数 值 参数 值 参数 值 $ t $ 0 $ n_2 $ 3.2 $ T_{\rm{RP}} $ 3 $ N $ 0 $ n_{m} $ 1 000 $ n_{\rm{init}} $ 1 $ V_{\rm{GT}} $ 4.5 $ \sigma_0 $ 0.01 $ r $ 0.025 m $ n_1 $ 1.8 $ \alpha_0 $ 0.01 $ d_{\rm{step}} $ 0.05 m 
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表 2 本文与四叉树栅格对比
Table 2 Comparison between occupancy grids based on quadtree and DGP-PCCMM
性能指标 占用栅格或位置
细胞个数有无仿生性 对动态变化
的适应性四叉树栅格 > 256 (Hairpin);
> 196 (Tolman)无 较弱 本文认知地图 65 (Hairpin);
90 (Tolman)有 较好
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表 3 本文与动态窗口法对比
Table 3 Comparison between dynamic window approach and our method
性能指标环境 运行时间 (s) 导航路线长度 (cm) T1 T2 T3 T1 T2 T3 动态窗口法 141.4 188.6 717.9 121.3 171.2 418.6 本文认知地图 24.5 35.7 60.2 122.6 178.6 301.2
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