本文關(guān)鍵詞： 類腦導(dǎo)航 空間認(rèn)知 位置細(xì)胞 多尺度表征 Q學(xué)習(xí)　出處：《電子與信息學(xué)報(bào)》2017年06期 　論文類型：期刊論文

【摘要】：為實(shí)現(xiàn)運(yùn)行體空間認(rèn)知和自主導(dǎo)航,借鑒生物導(dǎo)航機(jī)理,該文提出基于多尺度空間表征的生物啟發(fā)目標(biāo)指引導(dǎo)航模型。首先構(gòu)建不同尺度位置細(xì)胞圖編碼空間環(huán)境,采用高斯模型模擬位置細(xì)胞放電率,并將其作為Q學(xué)習(xí)的狀態(tài)輸入,然后采用模擬退火方法完成行為選擇,通過多次探索學(xué)習(xí)使運(yùn)行體能夠正確規(guī)劃出一條從起始點(diǎn)到目標(biāo)點(diǎn)的最短路徑。仿真結(jié)果表明,該方法用于目標(biāo)指引導(dǎo)航是可行的,相對(duì)于單尺度位置細(xì)胞空間認(rèn)知模型,該方法不但符合多尺度空間表征的生物學(xué)依據(jù),而且學(xué)習(xí)速度更快。在存在障礙物的環(huán)境中,能夠順利完成目標(biāo)指引導(dǎo)航任務(wù),并且當(dāng)障礙物發(fā)生變化時(shí)具有較好的適應(yīng)性。
[Abstract]:In order to realize the spatial cognition and autonomous navigation of moving volume and to learn from the mechanism of biological navigation, this paper proposes a biologically inspired target guidance navigation model based on multi-scale spatial representation. Firstly, the coding spatial environment of cell graph at different scales is constructed. Gao Si model was used to simulate the discharge rate of position cells, which was used as the input of Q-learning state, and then simulated annealing was used to complete the behavior selection. Through multiple explorations, the operator can correctly plan the shortest path from the starting point to the target point. The simulation results show that this method is feasible for target guidance and navigation, and can be compared with the single scale position cell space cognitive model. This method not only accords with the biological basis of multi-scale spatial representation, but also has a faster learning speed. In the environment with obstacles, the target guidance navigation task can be successfully completed, and it has better adaptability when obstacles change.
【作者單位】： 空軍工程大學(xué)信息與導(dǎo)航學(xué)院;西安通信學(xué)院;
【基金】：國家自然科學(xué)基金(61273048,61473308,61603409)~~
【分類號(hào)】：Q42;TP18
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本文編號(hào)：1515165