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唐钧跃, 全齐全, 姜生元, 侯绪研, 邓宗全. 一种基于可钻性在线辨识的月面钻进控制方法研究[J]. 深空探测学报(中英文), 2015, 2(4): 325-332. DOI: 10.15982/j.issn.2095-7777.2015.04.005
引用本文: 唐钧跃, 全齐全, 姜生元, 侯绪研, 邓宗全. 一种基于可钻性在线辨识的月面钻进控制方法研究[J]. 深空探测学报(中英文), 2015, 2(4): 325-332. DOI: 10.15982/j.issn.2095-7777.2015.04.005
TANG Junyue, QUAN Qiquan, JIANG Shengyuan, HOU Xuyan, DENG Zongquan. Control Method of Lunar Drilling Based on Online Identification of Drilling Ability[J]. Journal of Deep Space Exploration, 2015, 2(4): 325-332. DOI: 10.15982/j.issn.2095-7777.2015.04.005
Citation: TANG Junyue, QUAN Qiquan, JIANG Shengyuan, HOU Xuyan, DENG Zongquan. Control Method of Lunar Drilling Based on Online Identification of Drilling Ability[J]. Journal of Deep Space Exploration, 2015, 2(4): 325-332. DOI: 10.15982/j.issn.2095-7777.2015.04.005

一种基于可钻性在线辨识的月面钻进控制方法研究

Control Method of Lunar Drilling Based on Online Identification of Drilling Ability

  • 摘要: 钻取采样作为一种获取深层月壤的有效方式被应用于地外天体采样任务。不同于地面钻探,无人月面钻取采样面临诸多技术难点,例如遥操作信号延迟、探测器传感资源有限、缺乏采样点地质信息以及月壤力学特性复杂等。为保证采样任务高效可靠地执行,采样装置需充分利用有限的探测器硬件资源,依据钻进工况实时调整钻进工艺参数,对未知的钻进环境具有适应能力。提出一种基于可钻性在线辨识的月面钻进控制方法。利用可钻性指标综合评价当前对象的钻进难易程度,采用模式识别方法辨识钻进对象的可钻性等级并实时匹配最优的钻进工艺参数,从而实现钻进过程的智能控制。为验证所提出控制方法的有效性,开展了模拟月壤-月岩交替布置的钻进试验研究。试验结果表明:该方法能够有效控制钻进负载。

     

    Abstract: Drilling and coring, as an effective method of acquiring deep lunar regolith, has been widely applied in extraterrestrial sampling missions. Different from drilling on Earth, unmanned lunar drilling & coring may meet several technical problems, such as time delays in remote control, limited sensor resources, lack of geological information on sampling site, complicated mechanical properties of lunar regolith and so on. To realize high efficient drilling process with high reliability and have adaptability on unknown drilling environment, sampling device should adjust drilling parameters online depending on the real-time drilling conditions by limited hardware resources on the probe. This paper proposed a control method of lunar drilling based on online identification of drilling ability. The intelligent drilling control method has been realized by using drilling ability index to describe the drilling difficulty level, adopting pattern recognition method to identify the drilling ability levels and matching the optimized drilling parameters online. In order to verify the proposed control method, the drilling experiment in a multi-layered simulation mixed with granular soil and hard rocks has been conducted. Experimental results showed that drilling load under this control method could be controlled effectively.

     

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