**4. Recognition based drilling strategy**

Intelligent drilling control algorithm needs to be able to effectively identify the drilling formation, and timely adjust appropriate drilling parameters according to the recognition result. As an effective pattern recognition method, support vector machine (SVM) has been widely applied for several linear and nonlinear separable problems because of its high generalization ability [46, 47]. In previous works, a drillability classification covering from granular soil to hard rocks has been established based on the mechanical penetrating tests [48]. Herein, both rotary torque and penetrating force are selected as the drilling states monitoring signals *x* to imported into the proposed support vector machine recognition model to predict the corresponding drillability level *y*, as shown in **Figure 5**. Once the current formation's drillability level recognized, control algorithm will switch the optimized drilling parameters to drive the rotary motor and penetrate motor.

Actually, traditional SVM algorithm is based on the two classification mode, which is not suitable for multiple patterns of drillability classification. Compared with other classification methods, the decision directed acyclic graph (DDAG) based on the decision tree has a better training speed and a higher classification accuracy on the normal scale separation problems [49]. Considering that for covering potential drilling formations on the planets there are Intelligent Drilling and Coring Technologies for Unmanned Interplanetary Exploration http://dx.doi.org/10.5772/intechopen.75712 27

**Figure 5.** Scheme of drillability recognition based on SVM.

seen that during above drilling and coring process, the penetrating velocity is kept constant (80 mm/min), while the rotary speed will be adjusted (50 rev/min → 150 rev/min → 250 rev/min → 150 rev/min → 50 rev/min). Meanwhile, the monitored volume *V*acc can be divided into

During the AB stage of the first 20 s, since drill bit constantly cuts the in situ soil simulant without spiral auger's participant, there is almost no soil accumulated upon the surface. After then, the drill bit is buried in the soil, the auger starts to remove soil from the borehole bottom with a low removal speed during the BC stage. At the 40 s moment (C point), the rotary speed is suddenly switched to 150 rev/min with the result of the sudden increase of *V*acc. It can obviously be seen that the removal speed during CD stage is higher than that during BC stage. Above phenomenon is almost same with that in conditions between DF stage and CD stage. At the 85 s moment (F point), the corresponding PPR is regulated back to 0.53 mm/rev, which results in a slow increase trend of the *V*acc. After about 5 s, the removal speed becomes normal. This slow increase trend of the *V*acc also exists in the sudden change on G point. Based on above experimental results, it can be concluded that the monitored volume of accumulated soil can reflect the online removal states well and the PPR index has a great effect on the

According to preliminary experiments, the proposed non-contact drilling and coring characteristics monitoring method has been validated well. Next, to provide suitable drilling parameters database for the following intelligent drilling strategy, more drilling and coring experiments taken the drilling loads and core's quality into account will be conducted in sev-

Intelligent drilling control algorithm needs to be able to effectively identify the drilling formation, and timely adjust appropriate drilling parameters according to the recognition result. As an effective pattern recognition method, support vector machine (SVM) has been widely applied for several linear and nonlinear separable problems because of its high generalization ability [46, 47]. In previous works, a drillability classification covering from granular soil to hard rocks has been established based on the mechanical penetrating tests [48]. Herein, both rotary torque and penetrating force are selected as the drilling states monitoring signals *x* to imported into the proposed support vector machine recognition model to predict the corresponding drillability level *y*, as shown in **Figure 5**. Once the current formation's drillability level recognized, control algorithm will switch the optimized drilling parameters to drive the

Actually, traditional SVM algorithm is based on the two classification mode, which is not suitable for multiple patterns of drillability classification. Compared with other classification methods, the decision directed acyclic graph (DDAG) based on the decision tree has a better training speed and a higher classification accuracy on the normal scale separation problems [49]. Considering that for covering potential drilling formations on the planets there are

eral different drilling formations, such as limestone, sandstone, compacted soil, etc.

seven stages (AB → BC → CD → DF → FG → GH → HI).

26 Drilling

removal states and should be optimized further.

**4. Recognition based drilling strategy**

rotary motor and penetrate motor.

**Figure 6.** Drillability recognition algorithm based on DDAG.

at least three different formations for validation. Herein, DDAG is adopted to conduct the drillability recognition. The classification's structure diagram for four levels of lunar regolith simulants' drillability is shown in **Figure 6**.

As can be seen from the above algorithm structure, this method constructs a classifier with a two-way directed acyclic graph. Among them, the classifier 1 is located at the top of the root node to complete the first and second levels of drillability level 1–4 drillability comparison. By comparing the drillability level of 1 and drillability level of 4, the most samples may not belong to drillability level 1 (drillability level 4) can be excluded. After 3 times of excluding, the remaining category will be the drillability 1. Experiments indicated that by successive comparison this classification algorithm can guarantee a higher recognition accuracy.

In fact, model parameters in SVM play an important role in affecting recognition's accuracy. In the kernel function of SVM, scale parameter *g* and penalty coefficient *C* have the most significant effect on recognition's accuracy. When the two parameters do not match well, SVM will be overtraining or overfitting, which is an unstable situation in recognition. Herein, based on a grid search method, these two SVM model are optimized. To verify the optimized SVM model's generalization ability, drilling characteristics of different drillability samples under constant drilling parameters should be imported to conduct recognition training. Herein, a combination of rotary speed *n* = 100 rev/min and penetrating velocity *v*<sup>p</sup> = 10 mm/min is used as recognized drilling parameters. Typical simulants of drillability level 1, 3, 5 and 6 are selected as drilling media. Recognition results of un-optimized and optimized are shown in **Figure 7**. It can be found that the recognition accuracy of optimized SVM model is about 94.37%, which is obviously higher than the 78.15% of un-optimized model. When recognizing the closed drillability level 5 and level 6, the un-optimization model identifies 109 samples in 160 test samples and the recognition accuracy is just 68.13% in total. However, under the same conditions, the optimized model identifies 150 samples and the accuracy reaches roughly 93.75% in total. Therefore, it indicated that the optimized SVM recognition model indeed improves its recognition accuracy and becomes more practical in recognizing multilayered drilling media's drillability.

Once the optimized drillability SVM recognition model has been acquired, a multi-layered simulant mixed with granular soil and rocks has been constructed for conducting closed-loop validation experiments. There are five layers of three different compositions including granular soil (level 1), limestone (level 5) and marble (level 6) along the depth. As shown in **Figure 8**, signals acquired in the closed-loop drillability real-time recognition experiment are the drilling

state signals such as rotary torque, penetrating force, rotary speed, penetrating velocity, drilling power, and drilling energy. Among these signals, rotary torque and penetrating force were chosen as the recognition signals to identify drillability, and rotary speed and penetrating velocity are the corresponding drilling parameters adjusted to adapt to different drilling formations. For granular soil, rotary and penetrating control mode is adopted while rotary

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**Figure 8.** Drilling states during the multi-layered simulant drilling process.

**Figure 7.** Comparison of drillability recognition before and after optimization.

**Figure 8.** Drilling states during the multi-layered simulant drilling process.

of rotary speed *n* = 100 rev/min and penetrating velocity *v*<sup>p</sup> = 10 mm/min is used as recognized drilling parameters. Typical simulants of drillability level 1, 3, 5 and 6 are selected as drilling media. Recognition results of un-optimized and optimized are shown in **Figure 7**. It can be found that the recognition accuracy of optimized SVM model is about 94.37%, which is obviously higher than the 78.15% of un-optimized model. When recognizing the closed drillability level 5 and level 6, the un-optimization model identifies 109 samples in 160 test samples and the recognition accuracy is just 68.13% in total. However, under the same conditions, the optimized model identifies 150 samples and the accuracy reaches roughly 93.75% in total. Therefore, it indicated that the optimized SVM recognition model indeed improves its recognition accuracy

28 Drilling

and becomes more practical in recognizing multilayered drilling media's drillability.

**Figure 7.** Comparison of drillability recognition before and after optimization.

Once the optimized drillability SVM recognition model has been acquired, a multi-layered simulant mixed with granular soil and rocks has been constructed for conducting closed-loop validation experiments. There are five layers of three different compositions including granular soil (level 1), limestone (level 5) and marble (level 6) along the depth. As shown in **Figure 8**, signals acquired in the closed-loop drillability real-time recognition experiment are the drilling

> state signals such as rotary torque, penetrating force, rotary speed, penetrating velocity, drilling power, and drilling energy. Among these signals, rotary torque and penetrating force were chosen as the recognition signals to identify drillability, and rotary speed and penetrating velocity are the corresponding drilling parameters adjusted to adapt to different drilling formations. For granular soil, rotary and penetrating control mode is adopted while rotary

and percussive control mode are employed for rocks. When penetrating the granular soil from 0 to 22 s, the rotary motor keeps a constant rotary speed 80 r/min and penetrating motor exerts a constant velocity 80 mm/min. In this period, penetrating force is less than 50 N, rotary torque is no more than 0.6 Nm and drilling power is less than 10 W. When penetrating to the formation of limestone, penetrating force booms up meanwhile recognition drilling parameters are adopted to start real-time recognition. When recognizing limestone's drillability level, rotary motor switches rotary speed to 100 r/min and penetrating velocity is maintained a constant value 10 mm/min. In this period, penetrating force maintains a low level of less than 650 N, rotary torque is also no more than 10 Nm and drilling power is controlled no more than 90 W.

application. Therefore, the proposed drilling and coring characteristics monitoring method may be applied to further experiments. Moreover, considering the increasing costs of human resources in the future, the unmanned oil and gas drilling is being more popular than before. The proposed drillability recognition based online drilling strategy is exactly developed for this issue. By only required some basic force sensor resources, it can be simply applied to recognize different drillability levels of uncertain drilling formations in practice. However, it should be noted that for future application, more considerations should be taken into optimizing the fluid system's disturbance on the recognition and the longer depth's coupling influence on the mechanical system.

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This chapter elaborates the unique challenges in interplanetary drilling and coring mission. To comprehend the specific drilling and coring characteristics, a non-contact drilling and coring characteristics monitoring method has been proposed and verified. By establishing a drillability classification model, different types of drilling formations are evaluated by a combined index. Based on the SVM pattern recognition method, a drillability recognition model has been built up that can accurately identify four different drillability levels after optimization. Experiments under a multi-layered drilling simulant revealed that this intelligent drilling strategy can effectively reduce the drilling loads and can be applied to future interplanetary

The authors greatly thank to the financial support by the fundamental research fund from the National Natural Science Foundation (Nos. 61403106, 51575122) and the Program of China

Junyue Tang, Qiquan Quan, Shengyuan Jiang\*, Jieneng Liang and Zongquan Deng

State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin,

[1] Harvey B. Soviet and Russian Lunar Exploration. Chichester: Praxis; 2007. DOI:

**6. Conclusions**

unmanned drilling and coring exploration.

Scholarship Council (No. 201706120153).

10.1007/978-0-387-73976-2

\*Address all correspondence to: jiangshy@hit.edu.cn

**Acknowledgements**

**Author details**

PR China

**References**

According to the monitored drilling states, by matching the appropriate drilling parameters with corresponding drillability level, the drilling loads in penetrating five formations keep relatively stable and do not surpass drill tool's load limits. As a result, it takes only 600 s and 10 Wh drilling energy in the 0.5 m drilling process. Overall, this drillability real-time recognition drilling strategy has been verified by this multi-layered drilling experiments.
