Multi-state autonomous drilling for lunar exploration

Chen Chongbin,Quan Qiquan,Shi Xiaomeng,Deng Zongquan,Tang Dewei,Jiang Shengyuan

State Key Laboratory of Robotics and System,Harbin Institute of Technology,Harbin 150080,China

Multi-state autonomous drilling for lunar exploration

Chen Chongbin,Quan Qiquan*,Shi Xiaomeng,Deng Zongquan,Tang Dewei,Jiang Shengyuan

State Key Laboratory of Robotics and System,Harbin Institute of Technology,Harbin 150080,China

Due to the lack of information of subsurface lunar regolith stratification which varies along depth,the drilling device may encounter lunar soil and lunar rock randomly in the drilling process.To meet the load safety requirements of unmanned sampling mission under limited orbital resources,the control strategy of autonomous drilling should adapt to the indeterminable lunar environments.Based on the analysis of two types of typical drilling media(i.e.,lunar soil and lunar rock),this paper proposes a multi-state control strategy for autonomous lunar drilling.To represent the working circumstances in the lunar subsurface and reduce the complexity of the control algorithm,lunar drilling process was categorized into three drilling states:the interface detection,initiation of drilling parameters for recognition and drilling medium recognition.Support vector machine (SVM) and continuous wavelet transform were employed for the online recognition of drilling media and interface,respectively.Finite state machine was utilized to control the transition among different drilling states.To verify the effectiveness of the multi-state control strategy,drilling experiments were implemented with multi-layered drilling media constructed by lunar soil simulant and lunar rock simulant.The results reveal that the multi-state control method is capable of detecting drilling state variation and adjusting drilling parameters timely under vibration interferences.The multi-state control method provides a feasible reference for the control of extraterrestrial autonomous drilling.

1.Introduction

The uncertainty of drilling media in lunar environment is a challenge for autonomous drilling devices because it is difficult to determine the geological structure of a landing site along longitudinal direction in lunar subsurface drilling and coring missions.1–3According to lunar samples,images and data obtained in the lunar exploration missions from the United States and the former Soviet Union,two types of typical drilling media distributed in lunar environment are loose granular lunar soil and massive lunar rocks.In the Chinese unmanned drilling and coring mission of Chang'e-5,drilling device should be designed to be adaptable to the indeterminable drilling environments.Unlike automatic ground drilling,lunar subsurface drilling is restricted by rocket delivering capacity and complex lunar environments.The lunar drilling devices only rely on an intelligent drilling controller equipped with limited sensor resources.4–7

The Soviet Union has achieved unmanned autonomous extraterrestrial subsurface drilling and sampling in last century.8LUNA 16 and LUNA 20 controlled drilling processes with fixed drilling parameters,which may have poor adaptation to the complex lunar environments.Once the monitoring signal of drilling condition exceeds a safe threshold,the system will alarm experts on the ground to perform troubleshooting.LUNA 24 is the last lunar exploration mission returning lunar soil samples to the earth,implemented by the former Soviet Union.The drilling device was equipped with an adaptive mechanism to achieve autonomous drilling through a mechanical way.Due to limited intelligence,the sampler paused several times during the drilling procedure for excessive drilling loads,failing to achieve expected drilling depth and sampling quantity.9

Currently,Honeybee Robotics focuses on the autonomous extraterrestrial drilling research and has developed DAME,MARTE and CRUX intelligent ground drilling devices for future Mars drilling and sampling missions.10The control algorithms of the ground drilling devices were based on empirical models,fuzzy rules,and vibration modals.Experiments indicated that the DAME can identify six types of drilling faults and subsequently tune the drilling strategy accordingly.However,additional online monitoring sensors are required in the drilling control.11,12Despite the limitation of the DAME,the Honeybee Robotics emphasized the importance of intelligent drilling control in space autonomous drilling and provided a promising way.

The core of the intelligent drilling is to identify the types of the current drilling media and the n tune corresponding drilling parameters.Owing to the existence of drilling states for different drilling media and the interfaces,a recognition method and a control algorithm should be developed for identification of each drilling media and transition among the drilling states,respectively.This paper presents a multi-state autonomous drilling method based on online recognition.Support vector machine (SVM) and continuous wavelet transform were employed for the online recognition of drilling media and interface,respectively.Finite state machine was utilized to control the transition among different drilling states.This method is capable of detecting drilling state variation and adjusting drilling parameters timely at different drilling loads under vibration interferences.

The remainder of the paper is organized in the following manner.The types of lunar drilling media and corresponding appropriate drilling parameters are presented firstly.Online recognition based autonomous drilling is the n introduced.The recognition of drilling media is presented subsequently.The autonomous drilling based on the finite state machine is stated thereafter.Finally,validation experiments with multilayered drilling media are conducted.

2.Lunar drilling media and drilling parameters

Since China has not acquired lunar regolith samples,research for drilling parameters is based on lunar regolith simulant which is produced according to images and data collected by telemetry,in situ and laboratory tests of lunar soil samples and activities of landers,rovers and astronauts on the lunar surface.As lunar regolith has a considerable amount of mechanical properties,it is rather difficult to identify all the parameters individually online.To be convenient to conduct the drilling parameter research of the lunar regolith,the lunar regolith was divided into different drillability grades based on planetary drillability in the authors' previous research.13Different types of drilling media should match the corresponding drilling parameters.13–15Since this paper focuses on the multistate drilling control algorithm,in the recognition of drilling media we chose two typical drilling media:lunar soil simulant and lunar rock simulant(marble),which represent two extreme drilling conditions during the lunar drilling process:lunar soil and lunar rock.16

2.1.Lunar soil

Lunar soil which is distributed widely on lunar surface is a type of loose granular material.As lunar soil has fine flow characteristics,appropriate drilling parameters should be selected to keep the coring rates under limited drilling power.Since the mechanical properties of the basaltic simulant bracket that of the actual lunar regolith,the basaltic simulant was used to mimic the lunar soil in this paper.The basaltic simulant was created using basaltic pozzolana collected from Nanjing,China.Main mineralogical compositions of the simulant are similar to the compositions of the lunar soil on Apollo 14 landing site.17The simulant particle size range is 0.1–1 mm;the minimum density is 1.63 g/cm3;the maximum density is 2.15 g/cm3;the internal friction angle is 30.53°(relative density=75%);the cohesion is 0.33 kPa.According to the authors' previous research,suitable drilling parameters for the basaltic simulant are:rotary speed n=100 r/min,and penetrating speed vp=100 mm/min.13–15

2.2.Lunar rock

Since the lunar rocks are widely distributed in the lunar regolith,lunar rock drilling is an inevitable drilling condition in lunar drilling process.According to the rock drillability of geology,the drillability grade of the marble is similar to the complex polymict breccia which is distributed widely in the lunar regolith.In terrestrial rock drilling,main drilling methodsare rotary drilling,percussive drilling and rotarypercussive drilling.According to the former marble drilling experiments,the rotary torque and penetrating force in rotary-percussive drilling strategy are lower than those in rotary drilling strategy.18,19Appropriate drilling parameters for the marble are acquired as the following combination under experimental environments in this paper:rotary speed n=100 r/min,penetrating speed vp=10 mm/min and percussive frequency fp=5 Hz.13–15

3.Autonomous drilling based on online recognition

Online recognition based drilling strategy is an autonomous drilling control method with great adaptability.15The autonomous drilling control method includes recognizing current drilling media through sensor signals and adjusting drilling parameters(DP)according to the current drilling media in real time.As shown in Fig.1,the data acquisition subsystem of the drilling and coring test-bed obtains real-time sensor signals.The online recognition module determines current drilling media according to the analysis of the sensor signals.Accordingly drilling controller adjusts the real-time DP to achieve closed-loop autonomous drilling control.In this paper,the online recognition of the two typical drilling media was performed under the same DP which is named as drilling parameter for recognition(DPR).

Previous identification studies used the direct online recognition method.In the direct recognition method,when the control system detects the change of the drilling force conditions,the control system switches the current drilling parameters to the DPR to identify the drilling media.Recognition experiments demonstrated that a time delay existed at the moment that the drilling parameters switched to the rotary-percussive drilling module after the moment that the drill bit began to penetrate into therock(Fig.2).While this identification method has high recognition accuracy during the drilling process(higher than 80%,therecognition accuracy means ratios of correct identification number to total identification number),recognition response speed is required to be improved at the interface between the soil simulant and rock simulant.

The recognition model can identify the drilling media accurately under the DPR.Actually,in the direct recognition method,the recognition module was used to identify the drilling media according to online monitoring signal under the current drilling parameters instead of under the DPR.During the marble drilling,the percussive motion produces a large amount ofimpulse interference.This impulse interference may change the vibration frequencies of the rotary torque,which may produce wrong recognition results during the marble drilling as shown in Fig.2(the symbol 0 of the drilling media denotes soil simulant,1 denotes marble,and 2 denotes limestone).The main reason why the direct recognition method identified the drilling media according to online monitoring signal under the current drilling parameters instead of under the DPR was the improper arrangement of drilling states,which caused interference among drilling states.Therefore,arrangement of drilling states should be optimized.

Fig.1 Cyclic process of autonomous drilling.

Fig.2 Direct online recognition drilling.

4.Drilling signal characteristics

The aforementioned soil simulant and rock simulant are typical drilling media to test the performance of the drilling devices.Since the identification of the drilling media is based on the SVM in this paper,the drilling signal characteristics of the typical drilling media are required to be analyzed in order to build the recognition model.This section will present the drilling signal characteristics of lunar soil simulant,marble and interface between the soil simulant and the marble.

4.1.Soil simulant and rock simulant

Since the soil simulant is a type of a granular material,the shear rupture is major rupture method during the soil simulant drilling process.Unlike the cutting process of met al material,therupture of the soil simulant is discontinuous,which causes fluctuation of the drilling loads.Using the online monitoring signal of the rotary torque,we employed the time–frequency analysis method to analyze drilling signal characteristics of the soil simulant.After the time–frequency analysis,the vibration frequencies of the rotary signal were acquired,which represented the modes of the drilling media.The modes of the drilling media are related to the material properties,geometry and boundary conditions of the drilling media,and any changes in these parameters inevitably alter the dynamic characteristics.The vibration frequency functions which are acquired from the time–frequency analysis represent the inherent dynamic properties of drilling medium,so modal parameters are easily identified.Fig.3 shows the power spectral density(PSD)P and vibration frequencies(VF)f of the soil simulant.The observed PSD and VF in the drilling process are steady and can be used as a training sample to train the SVM.

The cohesion of marble is much higher than soil simulant,which means that in marble drilling large scale deformation is hardly performed under limited drilling power.Brittle fracture is the major method to penetrate into marble.When blades on the bit cut the fragile surface of the marble,the production of rock fragments is not continuous,which causes obvious vibration during the drilling process.Fig.3 presents the PSD and VF of the rotary torque signal when the bit penetrates into the marble.Despite the fluctuation of the rotary torque during drilling process,the PSD of the marble is steady and can be used as a SVM training sample.

It should be emphasized that the DP which is safe for one specified drilling medium may not be safe for another drilling medium.15–17To ensure the drilling device safe at the interface,the online recognition of the two typical drilling media should be performed under the same DP which is safe for both types of drilling media in terms of drilling loads.In this paper,the special DP is DPR.The training samples were obtained from drilling experiments with typical drilling media under the DPR.In actual drilling process,the controller collects drilling signal characteristics under the DPR as input of a well-trained recognition network,and the n identifies the type of the current drilling medium.

4.2.At the interface

It is appropriate to start online recognition when drill bit starts penetrating the interface between lunar soil and lunar rock.To ensure that the online recognition performs properly,interface detection module should be developed to detect the interface between different drilling media during the drilling process.Experiments of marble drilling under the DPR were conducted to investigate the drilling characteristics at the interface.Fig.4 shows obvious steps in penetrating speed vp,derivatives of rotary torque and derivatives of penetrating force(dT/dt and dFp/dt)while the bit penetrated into the marble.This step signal is the drilling signal characteristics at the interface and reflects the physical process that the drill bit penetrates into the marble.

Fig.4 Derivatives of signal monitoring in rock simulant drilling.

To detect the step signal,a method based on continuous wavelet transform was proposed.20–22The function of the signal x(t),the wavelet function ψ and its primitive function θ are

The restrictions for the scale saof wavelet transform are:(1)the maximum of the wavelet transform is no less than 50%maximum of primitive function θ;(2)the support width of wavelet function δ is no more than half of the detection period Tm.Besides,the estimated step amplitude is no less than half of set value vpset.Thus,the threshold of the wavelet transform for detecting interface[|Wsavp|]can be expressed as Eq.(2).

Fig.3 PSD samples of lunar soil simulant and lunar rock simulant.

Fig.5 Wavelet transform of vp.

Fig.5 shows an example of interface detection with the appropriate saand[|Wsavp|],in which the source signal is the same as Fig.4.The test result demonstrates that the continuous wavelet transform method can capture the three step signals of the interface drilling characteristics.The drilling signal characteristics at t2when the drill bit began to invade the marble are used to judge whether the drill bit is at the interface(t1represents the moment that the bit began to contact the interface,and t3represents the moment that the bit penetrated into the marble).

where A is the estimated step amplitude;cotα the gradient of the estimated step;|Wsavp|the amplitude of the wavelet transform.

5.Finite state machine based drilling process control

Finite state machine(FSM)represents a system as means of states and transitions,which deals with system inputs and procedure outputs.Since the drilling process has multiple states,using FSM can simplify the multi-state drilling control.This section builds a FSM based control system to control the drilling process.23,24

5.1.Multi-state drilling control principle

The drilling states can be divided into three conditions:the interface detection,initiation of DPR and drilling medium recognition.The prerequisite for converting the drilling states is the signal of interface detection and the output of produce.It is appropriate to develop a control system based on the FSM to control the drilling process(Fig.6).According to the structure of the FSM,the state transition table is organized(Table 1).The control system detects the interface during the drilling process.The DPR initiates after the interface was detected.Subsequently,the recognition algorithm initiates to identify drilling media.The drilling parameters are the n tuned to appropriate combination according to the types of the drilling media.The drilling parameters remain unchanged until the interface has been detected.

5.2.Implementation of multi-state drilling

Fig.6 State diagram for autonomous drilling.

Table 1 State transition table for autonomous drilling.

The multi-state drilling control system mainly consists of multi-state recognition module,motion control module and data storage module(Fig.7).The data acquisition (DAQ) module is used to collect and store the data of the online monitoring signals in real time.The core of the control system is the multi-state recognition module which is employed to control the drilling states.In the multi-state recognition module,the recognition of the drilling media is achieved by the interface detection algorithm and SVM based recognition algorithm.The motion control module is used to regulate drilling parameters of the drilling device according to the recognition results.

During the drilling process,the online DAQ transmits the monitoring signals to the control system in real time.The control system judges the type of the drilling medium according to the feedback signals.If the interface is detected,the FSM will convert the state of interface detection to the DPR initiation.The control system tunes the current drilling parameters to the DPR.Subsequently,the FSM initiates the state of the drilling medium recognition,and the control system identifies the drilling media under the DPR.The control system the n regulates the drilling parameters according to the type of the drilling medium.The monitoring data of the drilling device and the recognition results are stored in the data acquisition module for post-treatment.

Fig.7 Control algorithm for multi-state autonomous drilling.

6.Multi-layered drilling experiments

6.1.Apparatus

Fig.8 Drilling and coring test-bed.

In order to verify the effectiveness of the multi-state drilling control method,experiments were carried out using a drilling and coring test-bed equipped with a base,a rotary unit,a penetrating unit,a percussive unit and a lunar regolith simulant bin(Fig.8).The rotation of the drill is achieved by an AC server motor via a pair of gears with reduction ratio of 9:1.The percussive motion is achieved by the impact of the percussive mass driven by a ram.The ram is driven by an AC motor with reduction ratio of 1:1,and the n drives the percussive mass to compress the spring.As the cam bowl detaches from the cam,the percussive mass is pushed by the spring to collide with the drill tool.The percussive unit and rotary unit can be vertically actuated along two sliding guides at a desired speed by using a penetrating motor via chains.The control system of the test-bed collects the real-time data and controls drilling parameters during the drilling process.25The sensors in the test-bed are a torque sensor for rotary torque,load cells for penetrating force,travel switches and a magnetic scale for penetrating depth.The lunar regolith simulant bin is used to hold the lunar soil simulant and lunar rock simulant.The multilayered drilling media in the experiments were constructed with basaltic simulant and lunar rock simulant as shown in Fig.9(h1and h2are the depth of the marbles).A hollow drill tool was designed to fulfill the sampling experiments.As shown in Fig.9,the drill tool consists of a drill bit and an auger.The drill bit includes four kentanium blades which are welded to the drill bit.

Before the experiments,the SVM was trained by the samples obtained from drilling experiments with typical drilling media under the DPR.To achieve recognition of drilling media during the drilling process,the control system used the online filter sensor signals as input of the well-trained SVM under the DPR,and the n obtained the type of the current drilling medium.The controller continuously detected the interface during the drilling process.Once the controller detected the interface,the controller switched the current drilling parameters to the DPR and identified the drilling media in real time.

6.2.Results

The multi-layered drilling experiments demonstrated that the step signal of the penetrating speed was observed when the drill began to contact the marble surface with drilling parameters of n=100 r/min and vp=100 mm/min(Fig.10).The control system also detected the interface between the marble and soil simulant when the drill cut through the first marble and the n contacted the second marble.After detecting the interface according to the feedback signals,the controller tuned the drilling parameters to the DPR.Subsequently,the controller identified the types of drilling media and the n switched the DPR to the corresponding drilling parameters of the current drilling medium.During the non-interface drilling process,no miscarriage of justice happened.At the interface,the DPR only started once,and the switch of drilling parameters had a 4.0 s delay during drilling the first marble and the second marble.In the course of drilling the soil simulant between the two marbles,the switch of drilling parameters had a 3.5 s delay.

Fig.9 Multi-layered drilling media and drill tool.

Fig.10 Experimental results of multi-layered drilling.

Table 2 Comparison of different recognition methods.

As shown in Table 2,comparison of the direct recognition method with the multi-state drilling control method indicated that the multi-state drilling control method performed well at the identification accuracy and the delay time of the drilling parameter switch.The difference between the direct recognition method and the proposed method is the organization of the drilling states and the drilling parameter switching algorithm which is used to switch the current drilling parameters to the DPR.On one hand,the multi-state control method divides the drilling states into three conditions and arranges the drilling states based on FSM.Comparing the drilling results in Fig.2 and those in Fig.10,we can see that the well arrangement of the drill states improves the identification accuracy during drilling one drilling medium.On the other hand,the direct recognition and the proposed method switch the current drilling parameters to the DPR according to the change of the drilling force conditions and the detection of the interface,respectively.Compared with the direct recognition method,the response speed of drilling medium recognition improves at the interface between the soil simulant and rock simulant(Table 2)by the interface detection method presented in this paper.To reduce the delay time of the recognition,the scale saof wavelet transform and the detection period Tmshould be decreased.However,the reduction of the two parameters may reduce the accuracy of the recognition.

7.Conclusions

This paper presents a multi-state autonomous drilling method based on online recognition.SVM and continuous wavelet transform method were employed for the online recognition of drilling media and interface,respectively.Finite state machine was utilized to control the transition among different drilling states.This method is capable of detecting drilling state variation and adjusting drilling parameters timely at different drilling loads and under vibration interferences.This may provide a feasible reference for the control of extraterrestrial autonomous drilling.

Acknowledgments

The project is financially supported by fundamental research funds for National Natural Science Foundation of China(No.61403106),the Fundamental Research Funds for the Central Universities(No.HIT.NSRIF.2014051),the Program of Introducing Talents of Discipline to Universities(No.B07018),Heilongjiang Postdoctoral Grant(No.LBHZ11168),and China Postdoctoral Science Foundation(No.2012M520722).

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Chen Chongbinis a Ph.D.candidate at School of Mechatronics Engineering,Harbin Institute of Technology,Harbin,China.He received his M.S.degree from Nanjing University of Science and Technology in 2012.His area of research is automated planetary sampling.

Quan Qiquanis an assistant professor at School of Mechatronics Engineering,Harbin Institute of Technology,Harbin,China.He received his B.S.and M.S.degrees from Harbin Institute of Technology in 2005 and 2007,respectively,and received the Ph.D.degree from Ritsumeikan University in 2010.His main research interests include automated planetary sampling,ultrasonic levitation,and on orbitamp;ground test for space mechanism.

Shi Xiaomengreceived his B.S.and M.S.degrees from Harbin Institute of Technology in 2008 and 2010,respectively,and received the Ph.D.degree from Harbin Institute of Technology in 2015.His area of research is automated planetary sampling.

Deng Zongquanis a professor and Ph.D.supervisor at School of Mechatronics Engineering,Harbin Institute of Technology,Harbin,China.His current research interests include automated planetary sampling.

Tang Deweiis a professor and Ph.D.supervisor at School of Mechatronics Engineering,Harbin Institute of Technology,Harbin,China.He received his Ph.D.degree from the same university in 2000.His current research interests include automated planetary sampling.

Jiang Shengyuanis a professor and Ph.D.supervisor at School of Mechatronics Engineering,Harbin Institute of Technology,Harbin,China.He received his Ph.D.degree from the same university in 2001.His current research interests include automated planetary sampling.

12 October 2015;revised 15 November 2015;accepted 5 December 2015

Available online 14 January 2016

Finite state machine;

Recognition algorithm;

Simulants;

Support vector machine;

Wavelet transform

©2016 The Authors.Production and hosting by Elsevier Ltd.on behalf of Chinese Society of Aeronautics and Astronautics.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

*Corresponding author.Tel.:+86 451 86413857.

E-mail addresses:chongbin831@hotmail.com(C.Chen),quanqiquan@hit.edu.cn(Q.Quan),vsimon@sohu.com(X.Shi).

Peer review under responsibility of Editorial Committee of CJA.