Chinese Journal of Aeronautics, (2016), 29(5): 1397–1404

Chinese Society of Aeronautics and Astronautics& Beihang University

Chinese Journal of Aeronautics

cja@buaa.edu.cnwww.sciencedirect.com

Multi-state autonomous drilling for lunar

exploration

* 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.

Production and hosting by Elsevier

http://dx.doi.org/10.1016/j.cja.2016.01.0041000-9361 � 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/).

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

Received 12 October 2015; revised 15 November 2015; accepted 5 December 2015Available online 14 January 2016

KEYWORDS

Finite state machine;

Recognition algorithm;

Simulants;

Support vector machine;

Wavelet transform

Abstract 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 algo-

rithm, lunar drilling process was categorized into three drilling states: the interface detection, initi-

ation of drilling parameters for recognition and drilling medium recognition. Support vector

machine (SVM) and continuous wavelet transform were employed for the online recognition of dril-

ling 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 detect-

ing 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.� 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/).

1. Introduction

The uncertainty of drilling media in lunar environment is achallenge for autonomous drilling devices because it is difficultto determine the geological structure of a landing site along

longitudinal direction in lunar subsurface drilling and coringmissions.1–3 According to lunar samples, images and dataobtained in the lunar exploration missions from the United

1398 C. Chen et al.

States and the former Soviet Union, two types of typicaldrilling media distributed in lunar environment are loose gran-ular lunar soil and massive lunar rocks. In the Chinese

unmanned drilling and coring mission of Chang’e-5, drillingdevice should be designed to be adaptable to the indeter-minable drilling environments. Unlike automatic ground dril-

ling, lunar subsurface drilling is restricted by rocketdelivering capacity and complex lunar environments. Thelunar drilling devices only rely on an intelligent drilling con-

troller equipped with limited sensor resources.4–7

The Soviet Union has achieved unmanned autonomousextraterrestrial subsurface drilling and sampling in last cen-tury.8 LUNA 16 and LUNA 20 controlled drilling processes

with fixed drilling parameters, which may have poor adapta-tion to the complex lunar environments. Once the monitoringsignal 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 lunarsoil samples to the earth, implemented by the former Soviet

Union. The drilling device was equipped with an adaptivemechanism to achieve autonomous drilling through a mechan-ical way. Due to limited intelligence, the sampler paused sev-

eral times during the drilling procedure for excessive drillingloads, failing to achieve expected drilling depth and samplingquantity.9

Currently, Honeybee Robotics focuses on the autonomous

extraterrestrial drilling research and has developed DAME,MARTE and CRUX intelligent ground drilling devices forfuture Mars drilling and sampling missions.10 The control

algorithms of the ground drilling devices were based on empir-ical models, fuzzy rules, and vibration modals. Experimentsindicated that the DAME can identify six types of drilling

faults and subsequently tune the drilling strategy accordingly.However, additional online monitoring sensors are required inthe drilling control.11,12 Despite the limitation of the DAME,

the Honeybee Robotics emphasized the importance of intelli-gent drilling control in space autonomous drilling and pro-vided a promising way.

The core of the intelligent drilling is to identify the types of

the current drilling media and then tune corresponding drillingparameters. Owing to the existence of drilling states for differ-ent drilling media and the interfaces, a recognition method and

a control algorithm should be developed for identification ofeach drilling media and transition among the drilling states,respectively. This paper presents a multi-state autonomous

drilling method based on online recognition. Support vectormachine (SVM) and continuous wavelet transform wereemployed for the online recognition of drilling media andinterface, respectively. Finite state machine was utilized to con-

trol the transition among different drilling states. This methodis capable of detecting drilling state variation and adjustingdrilling 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. Onlinerecognition based autonomous drilling is then introduced.The recognition of drilling media is presented subsequently.

The autonomous drilling based on the finite state machine isstated thereafter. Finally, validation experiments with multi-layered drilling media are conducted.

2. Lunar drilling media and drilling parameters

Since China has not acquired lunar regolith samples, researchfor drilling parameters is based on lunar regolith simulant

which is produced according to images and data collected bytelemetry, in situ and laboratory tests of lunar soil samplesand activities of landers, rovers and astronauts on the lunar

surface. As lunar regolith has a considerable amount ofmechanical properties, it is rather difficult to identify all theparameters individually online. To be convenient to conductthe drilling parameter research of the lunar regolith, the lunar

regolith was divided into different drillability grades based onplanetary drillability in the authors’ previous research.13 Dif-ferent types of drilling media should match the corresponding

drilling parameters.13–15 Since this paper focuses on the multi-state drilling control algorithm, in the recognition of drillingmedia we chose two typical drilling media: lunar soil simulant

and lunar rock simulant (marble), which represent two extremedrilling conditions during the lunar drilling process: lunar soiland lunar rock.16

2.1. Lunar soil

Lunar soil which is distributed widely on lunar surface is a typeof loose granular material. As lunar soil has fine flow charac-teristics, appropriate drilling parameters should be selected to

keep the coring rates under limited drilling power. Since themechanical properties of the basaltic simulant bracket thatof the actual lunar regolith, the basaltic simulant was used to

mimic the lunar soil in this paper. The basaltic simulant wascreated 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 land-ing site.17 The simulant particle size range is 0.1–1 mm; theminimum density is 1.63 g/cm3; the maximum density is2.15 g/cm3; the internal friction angle is 30.53� (relative den-

sity = 75%); the cohesion is 0.33 kPa. According to theauthors’ previous research, suitable drilling parameters forthe basaltic simulant are: rotary speed n= 100 r/min, and pen-

etrating speed vp = 100 mm/min.13–15

2.2. Lunar rock

Since the lunar rocks are widely distributed in the lunar rego-lith, lunar rock drilling is an inevitable drilling condition in

lunar drilling process. According to the rock drillability ofgeology, the drillability grade of the marble is similar to thecomplex polymict breccia which is distributed widely in the

lunar regolith. In terrestrial rock drilling, main drilling meth-ods are rotary drilling, percussive drilling and rotary-percussive drilling. According to the former marble drilling

experiments, the rotary torque and penetrating force inrotary-percussive drilling strategy are lower than those inrotary drilling strategy.18,19 Appropriate drilling parameters

for the marble are acquired as the following combinationunder experimental environments in this paper: rotary speedn= 100 r/min, penetrating speed vp = 10 mm/min and per-cussive frequency fp = 5 Hz.13–15

Fig. 2 Direct online recognition drilling.

Multi-state autonomous drilling for lunar exploration 1399

3. Autonomous drilling based on online recognition

Online recognition based drilling strategy is an autonomousdrilling control method with great adaptability.15 The autono-

mous drilling control method includes recognizing current dril-ling media through sensor signals and adjusting drillingparameters (DP) according to the current drilling media in real

time. As shown in Fig. 1, the data acquisition subsystem of thedrilling and coring test-bed obtains real-time sensor signals.The online recognition module determines current drillingmedia according to the analysis of the sensor signals. Accord-

ingly drilling controller adjusts the real-time DP to achieveclosed-loop autonomous drilling control. In this paper, theonline recognition of the two typical drilling media was per-

formed under the same DP which is named as drilling param-eter for recognition (DPR).

Previous identification studies used the direct online recog-

nition method. In the direct recognition method, when the con-trol 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 experi-ments demonstrated that a time delay existed at the momentthat the drilling parameters switched to the rotary-percussivedrilling module after the moment that the drill bit began to

penetrate into the rock (Fig. 2). While this identificationmethod has high recognition accuracy during the drilling pro-cess (higher than 80%, the recognition accuracy means ratios

of correct identification number to total identification num-ber), recognition response speed is required to be improvedat the interface between the soil simulant and rock simulant.

The recognition model can identify the drilling media accu-rately under the DPR. Actually, in the direct recognitionmethod, the recognition module was used to identify the dril-

ling media according to online monitoring signal under thecurrent drilling parameters instead of under the DPR. Duringthe marble drilling, the percussive motion produces a largeamount of impulse interference. This impulse interference

may change the vibration frequencies of the rotary torque,which may produce wrong recognition results during the mar-ble drilling as shown in Fig. 2 (the symbol 0 of the drilling

Fig. 1 Cyclic process of autonomous drilling.

media denotes soil simulant, 1 denotes marble, and 2 denotes

limestone). The main reason why the direct recognitionmethod identified the drilling media according to online mon-itoring signal under the current drilling parameters instead of

under the DPR was the improper arrangement of drillingstates, which caused interference among drilling states. There-fore, arrangement of drilling states should be optimized.

4. Drilling signal characteristics

The aforementioned soil simulant and rock simulant are typi-

cal drilling media to test the performance of the drillingdevices. Since the identification of the drilling media is basedon the SVM in this paper, the drilling signal characteristics

of the typical drilling media are required to be analyzed inorder to build the recognition model. This section will presentthe drilling signal characteristics of lunar soil simulant, marbleand 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 simulantdrilling process. Unlike the cutting process of metal material,the rupture of the soil simulant is discontinuous, which causes

fluctuation of the drilling loads. Using the online monitoringsignal of the rotary torque, we employed the time–frequencyanalysis method to analyze drilling signal characteristics of

the soil simulant. After the time–frequency analysis, the vibra-tion frequencies of the rotary signal were acquired, which rep-resented the modes of the drilling media. The modes of thedrilling media are related to the material properties, geometry

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

1400 C. Chen et al.

and boundary conditions of the drilling media, and anychanges in these parameters inevitably alter the dynamic char-acteristics. The vibration frequency functions which are

acquired from the time–frequency analysis represent the inher-ent dynamic properties of drilling medium, so modal parame-ters are easily identified. Fig. 3 shows the power spectral

density (PSD) P and vibration frequencies (VF) f of the soilsimulant. The observed PSD and VF in the drilling processare 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 ishardly performed under limited drilling power. Brittle fracture

is the major method to penetrate into marble. When blades onthe bit cut the fragile surface of the marble, the production ofrock fragments is not continuous, which causes obvious vibra-

tion during the drilling process. Fig. 3 presents the PSD andVF of the rotary torque signal when the bit penetrates intothe marble. Despite the fluctuation of the rotary torque during

drilling process, the PSD of the marble is steady and can beused 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 drillingmedium.15–17 To ensure the drilling device safe at the interface,the online recognition of the two typical drilling media shouldbe performed under the same DP which is safe for both types

of drilling media in terms of drilling loads. In this paper, thespecial DP is DPR. The training samples were obtained fromdrilling experiments with typical drilling media under the

DPR. In actual drilling process, the controller collects drillingsignal characteristics under the DPR as input of a well-trainedrecognition network, and then identifies the type of the current

drilling medium.

4.2. At the interface

It is appropriate to start online recognition when drill bit startspenetrating the interface between lunar soil and lunar rock. Toensure that the online recognition performs properly, interfacedetection module should be developed to detect the interface

between different drilling media during the drilling process.Experiments of marble drilling under the DPR were conductedto investigate the drilling characteristics at the interface. Fig. 4

shows obvious steps in penetrating speed vp, derivatives ofrotary torque and derivatives of penetrating force (dT/dt and

Fig. 3 PSD samples of lunar soil si

dFp/dt) while the bit penetrated into the marble. This step sig-nal is the drilling signal characteristics at the interface andreflects the physical process that the drill bit penetrates into

the marble.To detect the step signal, a method based on continuous

wavelet transform was proposed.20–22 The function of the sig-

nal x(t), the wavelet function w and its primitive function h are

w ¼ �2te�t2ffiffiffiffiffiffiffiffi2=p4

ph ¼ e�t2

ffiffiffiffiffiffiffiffi2=p4

p(

ð1Þ

The restrictions for the scale sa of wavelet transform are: (1)the maximum of the wavelet transform is no less than 50%maximum of primitive function h; (2) the support width of

wavelet function d is no more than half of the detection periodTm. Besides, the estimated step amplitude is no less than half ofset value vpset. Thus, the threshold of the wavelet transform for

detecting interface ½jWsavpj� can be expressed as Eq. (2).

Fig. 5 shows an example of interface detection with the

appropriate sa and ½jWsavpj�, in which the source signal is the

same as Fig. 4. The test result demonstrates that the continu-ous wavelet transform method can capture the three step

mulant and lunar rock simulant.

Fig. 5 Wavelet transform of vp.Fig. 6 State diagram for autonomous drilling.

Table 1 State transition table for autonomous drilling.

Current

state

Input Next state Output

Interface

detection

Non-step vp Interface

detection

Step vp Initiate DPR DPR

Initiate

DPR

DPR initiating Initiate DPR DPR

DPR initiated Drilling medium

recognition

Drilling

medium

recognition

Drilling signal of

lunar soil simulant;

DPR initiating

Drilling medium

recognition

DP for

lunar soil

simulant

Drilling signal of

lunar soil simulant;

DPR initiated

Interface

detection

Drilling signal of

lunar rock

simulant;

DPR initiating

Drilling medium

recognition

DP for

lunar

rock

simulant

Drilling signal of

lunar rock

simulant;

DPR initiated

Interface

detection

Multi-state autonomous drilling for lunar exploration 1401

signals of the interface drilling characteristics. The drilling sig-nal characteristics at t2 when the drill bit began to invade the

marble are used to judge whether the drill bit is at the interface(t1 represents the moment that the bit began to contact theinterface, and t3 represents the moment that the bit penetrated

into the marble).

A cot a

2ffiffiffiffiffiffiffiffiffiffiffiffi� lnð0:5Þ

p 6 sa 6 Tm

2d

jWsavpj P jWsavpj� � ¼ vpset

ffiffiffiffisa

phmax

4

8><>: ð2Þ

where A is the estimated step amplitude; cot a the gradient ofthe estimated step; jWsavpj the amplitude of the wavelet

transform.

5. Finite state machine based drilling process control

Finite state machine (FSM) represents a system as means ofstates and transitions, which deals with system inputs and pro-

cedure outputs. Since the drilling process has multiple states,using FSM can simplify the multi-state drilling control. Thissection builds a FSM based control system to control the dril-

ling process.23,24

5.1. Multi-state drilling control principle

The drilling states can be divided into three conditions: theinterface detection, initiation of DPR and drilling mediumrecognition. The prerequisite for converting the drilling statesis the signal of interface detection and the output of produce.

It is appropriate to develop a control system based on the FSMto control the drilling process (Fig. 6). According to the struc-ture of the FSM, the state transition table is organized

(Table 1). The control system detects the interface during thedrilling process. The DPR initiates after the interface wasdetected. Subsequently, the recognition algorithm initiates to

identify drilling media. The drilling parameters are then tunedto appropriate combination according to the types of the dril-ling media. The drilling parameters remain unchanged until

the interface has been detected.

5.2. Implementation of multi-state drilling

The multi-state drilling control system mainly consists of

multi-state recognition module, motion control module anddata storage module (Fig. 7). The data acquisition (DAQ)

module is used to collect and store the data of the online mon-itoring signals in real time. The core of the control system is

the multi-state recognition module which is employed to con-trol the drilling states. In the multi-state recognition module,the recognition of the drilling media is achieved by the inter-

face detection algorithm and SVM based recognition algo-rithm. The motion control module is used to regulate drillingparameters 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 con-trol system judges the type of the drilling medium according to

the feedback signals. If the interface is detected, the FSM willconvert 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 dril-ling medium recognition, and the control system identifies thedrilling media under the DPR. The control system then regu-

lates the drilling parameters according to the type of the

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

1402 C. Chen et al.

drilling medium. The monitoring data of the drilling deviceand the recognition results are stored in the data acquisitionmodule for post-treatment.

6. Multi-layered drilling experiments

6.1. Apparatus

In order to verify the effectiveness of the multi-state drillingcontrol method, experiments were carried out using a drilling

and coring test-bed equipped with a base, a rotary unit, a pen-etrating unit, a percussive unit and a lunar regolith simulantbin (Fig. 8). The rotation of the drill is achieved by an AC ser-

ver motor via a pair of gears with reduction ratio of 9:1. Thepercussive motion is achieved by the impact of the percussivemass driven by a ram. The ram is driven by an AC motor with

reduction ratio of 1:1, and then drives the percussive mass tocompress the spring. As the cam bowl detaches from thecam, the percussive mass is pushed by the spring to collide withthe drill tool. The percussive unit and rotary unit can be verti-

cally actuated along two sliding guides at a desired speed by

Fig. 8 Drilling and coring test-bed.

using a penetrating motor via chains. The control system ofthe test-bed collects the real-time data and controls drillingparameters during the drilling process.25 The sensors in the

test-bed are a torque sensor for rotary torque, load cells forpenetrating force, travel switches and a magnetic scale for pen-etrating depth. The lunar regolith simulant bin is used to hold

the lunar soil simulant and lunar rock simulant. The multi-layered drilling media in the experiments were constructedwith basaltic simulant and lunar rock simulant as shown in

Fig. 9 (h1 and h2 are the depth of the marbles). A hollow drilltool was designed to fulfill the sampling experiments. Asshown in Fig. 9, the drill tool consists of a drill bit and anauger. The drill bit includes four kentanium blades which are

welded to the drill bit.Before the experiments, the SVM was trained by the sam-

ples obtained from drilling experiments with typical drilling

media under the DPR. To achieve recognition of drillingmedia during the drilling process, the control system usedthe online filter sensor signals as input of the well-trained

SVM under the DPR, and then obtained the type of the cur-rent drilling medium. The controller continuously detectedthe interface during the drilling process. Once the controller

detected the interface, the controller switched the current dril-ling parameters to the DPR and identified the drilling media inreal time.

6.2. Results

The multi-layered drilling experiments demonstrated that thestep signal of the penetrating speed was observed when the

drill began to contact the marble surface with drilling param-eters of n= 100 r/min and vp = 100 mm/min (Fig. 10). Thecontrol system also detected the interface between the marble

and soil simulant when the drill cut through the first marbleand then contacted the second marble. After detecting theinterface according to the feedback signals, the controller

tuned the drilling parameters to the DPR. Subsequently, thecontroller identified the types of drilling media and thenswitched the DPR to the corresponding drilling parametersof the current drilling medium. During the non-interface dril-

ling process, no miscarriage of justice happened. At the inter-face, the DPR only started once, and the switch of drillingparameters had a 4.0 s delay during drilling the first marble

and the second marble. In the course of drilling the soil simu-lant between the two marbles, the switch of drilling parametershad a 3.5 s delay.

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

Table 2 Comparison of different recognition methods.

Comparison items Multi-state drilling

control

Direct

recognition

Accuracy of simulant

recognition (%)

100 99.1

Accuracy of marble

recognition (%)

100 82.7

Delay (s) 4 5.8

Fig. 10 Experimental results of multi-layered drilling.

Multi-state autonomous drilling for lunar exploration 1403

As shown in Table 2, comparison of the direct recognition

method with the multi-state drilling control method indicatedthat the multi-state drilling control method performed wellat the identification accuracy and the delay time of the drilling

parameter switch. The difference between the direct recogni-tion method and the proposed method is the organization ofthe drilling states and the drilling parameter switching algo-rithm which is used to switch the current drilling parameters

to the DPR. On one hand, the multi-state control methoddivides the drilling states into three conditions and arrangesthe drilling states based on FSM. Comparing the drilling

results in Fig. 2 and those in Fig. 10, we can see that the wellarrangement of the drill states improves the identificationaccuracy during drilling one drilling medium. On the other

hand, the direct recognition and the proposed method switchthe current drilling parameters to the DPR according to the

change of the drilling force conditions and the detection ofthe interface, respectively. Compared with the direct recogni-

tion method, the response speed of drilling medium recogni-tion improves at the interface between the soil simulant androck simulant (Table 2) by the interface detection method pre-

sented in this paper. To reduce the delay time of the recogni-tion, the scale sa of wavelet transform and the detectionperiod Tm should be decreased. However, the reduction ofthe two parameters may reduce the accuracy of the

recognition.

7. Conclusions

This paper presents a multi-state autonomous drilling methodbased on online recognition. SVM and continuous wavelettransform method were employed for the online recognition

of drilling media and interface, respectively. Finite statemachine was utilized to control the transition among differentdrilling states. This method is capable of detecting drilling state

variation and adjusting drilling parameters timely at differentdrilling loads and under vibration interferences. This may pro-vide a feasible reference for the control of extraterrestrial

autonomous drilling.

Acknowledgments

The project is financially supported by fundamental researchfunds for National Natural Science Foundation of China

1404 C. Chen et al.

(No. 61403106), the Fundamental Research Funds for the

Central Universities (No. HIT.NSRIF.2014051), the Programof Introducing Talents of Discipline to Universities (No.B07018), Heilongjiang Postdoctoral Grant (No. LBH-

Z11168), and China Postdoctoral Science Foundation (No.2012M520722).

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Chen Chongbin is 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 Qiquan is 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 Technol-

ogy 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 orbit &

ground test for space mechanism.

Shi Xiaomeng received 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 Zongquan is 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 Dewei is a professor and Ph.D. supervisor at School of Mecha-

tronics 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 Shengyuan is 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.