Approach of Team SNU to the DARPA Robotics Challenge Finals

Sanghyun Kim1, Mingon Kim1, Jimin Lee1, Soonwook Hwang1, Joonbo Chae1, Beomyeong Park1,Hyunbum Cho1, Jaehoon Sim1, Jaesug Jung1, Hosang Lee1, Seho Shin1, Minsung Kim1, Nojun Kwak2,Yongjin Lee2, Sangkuk Lee2, Myunggi Lee2, Sangyup Yi3, Kyong-Sok K.C. Chang3, and Jaeheung Park1

Abstract— This paper presents the technical approaches in-cluding the system architecture and the controllers that havebeen used by Team SNU at the DARPA Robotics Challenge(DRC) Finals 2015. The platform THORMANG we used is amodular humanoid robot developed by ROBOTIS. On top ofthis platform, Team SNU developed the iris camera moduleand the end effector with passive palm in order to increasesuccess rate of the tasks at the DRC Finals. Also, we developedthe software architecture to operate the robot intuitively,in spite of degraded communication. The interface enablesoperator to select sensor data to be communicated during eachtask. These efforts on the hardware and the software reduceoperation time of the tasks, and increase reliability of the robot.Finally, the controllers for THORMANG were developed toconsider stability as the first priority, because the humanoidrobot for rescue should perform complex tasks in unexpectedenvironments. The proposed approaches were verified at theDRC Finals 2015, where Team SNU ranked 12th place out of23 teams.

I. INTRODUCTION

On June 5-6, 2015, the DARPA Robotics Challenge (DRC)Finals, the competition funded by the US Defense AdvancedResearch Projects Agency (DARPA), was held to developrobots which enable to provide assistance in future naturaland man-made disasters [1]. The DRC is motivated by theFukushima Daiichi nuclear disaster on March, 2012. Theplant workers at Fukushima were unable to stop leakage ofradioactive materials, because they could not perform tasksin the darkened reactor buildings. Thus, the necessity ofhumanoid robot substituting human activities in a disasterarea has attracted worldwide attention. Consequently, theDRC competition consists of various tasks, such as drivingand egressing a utility vehicle, opening a door, handling avalve, cutting a hole in a wall, walking over a pile of rubble,and walking up a short flight of stairs.

The main prerequisite skills for the DRC Finals to com-plete these tasks are as follows.• Perception & Decision-making : Ability to be operated

by autonomous algorithm or by humans who have little

1 are with the DYROS Lab, Graduate School of ConvergenceScience and Technology, Seoul National University, Seoul, Republicof Korea. Jaeheung Park is the corresponding author. {ggory15,mingonkim, jmpechem, jbs4104, lisser,on2lord, myugun, simjeh, jjs916, hslee1989,shinsh, minsungkim, park73}@snu.ac.kr

2 are with the MIPAL Lab, Graduate School of ConvergenceScience and Technology, Seoul National University, Seoul,Republic of Korea. {nojunk, esrobb3, lskpoet,myunggi89}@snu.ac.kr

3 are with the SimLab, Seoul, Republic of Korea.{seanyi,kcchang}@simlab.com

Fig. 1: THORMANG grabbing a tool for the wall task [2]

robotics training in the degraded communication.• Mounted Mobility : Mobility to maneuver a vehicle

safely.• Dexterity & Strength : Ability to manipulate a diverse

tools designed for humans.• Dismounted Mobility : Mobility to traverse the rough

terrain.We, Team SNU, is one of the 13 newly qualified teams to

join the DRC Finals 2015 with 11 teams that participatedin the December 2013 DRC Trials [3]-[10]. We workedfor several approaches on the hardware and the softwarearchitecture, in order to not only satisfy these skills fortasks, but also assure stability for the operation of the robot.Our hardware THORMANG (ROBOTIS, Co., Ltd, SouthKorea) is one of the lightest robots at the DRC Finals. Asshown in Fig. 1, THORMANG, upgraded version of THOR-OP, is assembled with module-type actuators to modify thekinematic structure easily [8]. To take advantage of modularcharacteristics, we developed the iris camera module tooperate well in direct sunlight, and the end effector withpassive palm to grasp objects firmly.

Our software architecture is optimized to operate complex

Head

Front of Head Back of Head

IRISCamera

Torso - Computing System

Quad-core i5

HokuyoLIDAR

Power System (18.5V)

Left Arm(7 DOF)

+Left

Gripper(1 DOF)

Power System (22.2V)

Left Leg

(6 DOF)

FT sensor

pow

er pow

er

USB100Hz

RS485

Right Arm(7 DOF)

+Right

Gripper(1 DOF)

HokuyoLIDAR

WebCamera

WebCamera

WebCamera

GyroSensor

USB-2-RS485

(4 Ports)

Quad-core i5

RCU RPU

100HzRS485

Power System (22.2V)

RightLeg

(6 DOF)

FT sensor

pow

er

100Hz

RS485100HzRS485

Fig. 2: Electrical system diagram of THORMANG

tasks in degraded communication. To overcome limited band-width of communication line, the field computers performcomplex calculations by sharing the raw data of the robot’ssensors. We also designed that the operator was able to selectsensor data to be communicated depending on the tasks ofTHORMANG.

To ensure control stability of the robot, we concentratedon the development of the robust controller for various sit-uations, such as ground status, wind strength, reaction forceduring manipulation, and contact with uncertain wall. Thetrajectory of the robot’s motion is confirmed by simulatorin order to prevent self-collision and singularity. Addition-ally, the locomotion controller for THORMANG has twolocal feedback controllers to endure the disturbance: Theimpedance controller for the trajectory of landing foot andupper-body posture controller for pelvis orientation.

This paper is organized as follows. Section II introducesthe hardware and the software architecture of Team SNU inthe DRC Finals 2015. Section III and IV present the controlsystem of upper and lower-body controller, respectively.Section V discusses the results of the lab test and the DRCFinals 2015. Finally, the paper is concluded in Section VI.

II. HARDWARE & SOFTWARE ARCHITECTUREOn top of platform THORMANG we used, we con-

centrated on the modification of hardware to successfullycomplete the tasks at the DRC Finals and developmentof software architecture to operate the tasks intuitively.The subsections below describe the details of the proposedhardware and software system.

A. Hardware Architecture

The height, weight, and wingspan of THORMANG are1.47 m, 60 kg, and 1.95 m, respectively. THORMANG

(a) (b)

Fig. 3: The grippers for THORMANG; (a) the gripper forthe left arm, (b) the gripper for the right arm

consists of 32 DOF, 8 in each arm, 6 in both legs, 2 forthe torso, and 2 for the head.

Fig. 2 shows overall electrical diagram of THORMANG.THORMANG has two computers with Intel i5 2.7 GHz.First, the Robot Control Unit (RCU) is in charge of com-municating with actuators, FT sensors (ATI Mini 54), anda gyro sensor (Microstrain 3DM-GX4-45) by using RS-485 communications protocol. Second, the Robot PerceptionUnit (RPU) manages the perception system (described inSection II-B). To power the robot, there are two types ofLiPo battery in THORMANG; A 18.5V LiPo battery forthe robot computers and two 22.2V LiPo batteries for therobot actuation. These onboard batteries enabled us to runTHORMANG for two hours.

Meanwhile, we concentrated efforts to increase a successrate at each task. Especially, we developed two types ofgrippers, as shown in Fig. 3. The gripper on the left armconsists of two sticks to firmly grasp objects. Anothergripper on the right arm consists of two active fingers withpassive structure. By using the gripper with passive palm,THORMANG can turn on the drill and drive a vehicle bygrasping the steering wheel.

B. Perception System

The perception system of Team SNU consists of threetypes of sensors; three webcams (Logitech C905), two LI-DARs (Hukuyo UTM-30LX-EW LIDAR), and an iris camera(Imagingsource DFK 23G618.I).

Three webcams are attached on the head in series for awide Field-of-View (FOV). Thus, the operator can obtaina panorama view in front of THORMANG. The webcamsare also helpful to gather the sound around THORMANG,because of microphone in the webcam. The specification ofthe webcam is 480 x 640 resolution and 45 degrees FOV.

Next, each LIDAR is located in the chest and the head ofTHORMANG. The chest LIDAR is attached on a panningservo motor which has a range of ±45 degrees. This sensoris used to calculate 3D reconstruction with point cloud data(PCD). By using this PCD and Support Vector Machine(SVM) with the Histogram of Oriented Gradients (HOG),we recognized the objects such as a valve, a door knob, anda drill automatically. On the other hand, the head LIDAR iscontrolled to gather only one layer of the raw data, which

Robot

RCU RPU

Field Server

FCU for Control FCU for Perception

Operator Control Station

OCU1 OCU2 OCU3

State Commander

Link 1(TCP, 300Mbit/sec)

Link 3(TCP, 9600bit/sec) Link 2(UDP, 300Mbit/sec)

Device Manager(Actuators, FT Sensors,

Gyro Sensor)

State ListenerState Listener

Robot Controller

Device Manager(LIDARs, WebCams,

Iris Camera)

Sound Streamer

State Commander

Multimedia Sender Object Recognition

Multimedia Converter

Robot Commander

Low Quality Image Viewer

2D Map Viewer

Sound Listener

Robot Viewer

High Quality Image Viewer

Sensor Commander

Iris Cam Viewer

Image Bird’s-eye Viewer

Device Manager(Racing Wheel)

Fig. 4: Diagram of Team SNU’s software architecture

is parallel to the horizontal surface of the ground, due tothe limited bandwidth. This method is helpful to draw anaccurate 2D map in degraded communication.

Finally, we attached an iris camera, which has adjustableaperture to control the amount of light, on the back of thehead to not only get rid of a blind spot for the driving task,but also operate well in direct sunlight. The resolution of aniris camera with 120 degrees FOV is 640 x 480.

C. Software Architecture

According to the rules of the DRC Finals, three typesof computers are recommended as shown in Fig. 4; robotcomputer unit including RCU and RPU, Field ComputerUnit (FCU), and Operator Computer Unit (OCU). To com-municate with each computer unit, DARPA provides threetypes of wireless line. First, the communication between therobot computers and the field computers is called Link1which is 300 Mbits/sec wireless LAN. Second, the fieldcomputers to communicate with the operator computers havetwo communications links; Link2 and Link3. Link2 is a UDPline and supports one second bursts of data at 300 Mbit/sec.However, this link has blackouts which can last from 1 to 30seconds, while the robot operates indoor tasks, such as thevalve task and the wall task. In contrast to Link2, Link3 is analways-on bidirectional TCP line, but it has small constantdata rate of 9600 bit/sec. Thus, our approach is focused on a

(a)

(b)

(c)

Fig. 5: The operation computer for THORMANG: (a) theinterface of OCU1, (b) the interface of OCU2, (c) theinterface of OCU3

development for the software structure to operate intuitivelyin degraded communication.

In the field server, there are two field computer units; onefor control and the other for perception. Our FCUs sharethe raw data of the robot’s sensors by Link1. Thus, theseFCUs handle complex calculation such as automatic objectrecognition, image converting, and sound streaming. Theadvantages of this system are to reduce power consumptionof THORMANG and amount of data from the robot to theoperator.

Based on prior researches for human-machine teamwork[11], [12], we used three computers for THORMANG. First,OCU1, main computer in operator control, is specializedto understand current status and control THORMANG in

degraded communication. OCU1 communicates with FCUsthrough Link3 in order to receive important data for operat-ing the robot. Thus, OCU1 treats necessary data, such as thecurrent status of the joint, values of F/T sensors and a gyrosensor, low quality images, refined sounds from webcams,and 2D map data from the head LIDAR. To represent thisinformation effectively, our interface in OCU1 consists ofvarious window modules, such as the robot commander, thelow quality image viewer, the 2d map image viewer, and thesound listener, as shown in Fig. 5a. Especially, the robotcommander including robot controllers and planners is amodule to control the movement of the robot. The robotcommander has two kinds of modes - auto mode and manualmode which are to complete the missions with predefinedtrajectories automatically and to control the robot manually,respectively. Meanwhile, the operator can choose what kindof data he will receive among the 2D map image viewer, thesound listener, and the low quality image viewer dependingon the situation. For instance, 2D map data was received foraccurate navigation in locomotion task, and sound data wasused to recognize whether the drill was turned on or not.

Second, OCU2 consists of the sensor commander, the highquality image viewer, and the robot viewer using Rviz. Toreceive the data from the FCU for perception, the commu-nication line in OCU2 is Link2. The sensor commanderis a module to operate the sensor for perception such asLIDARs and webcams. Next, the high quality image viewerrepresents original images from webcams when Link2 is notblackout. Finally, the robot viewer shows PCD from the chestLIDAR and current posture of the robot. Especially, it hasan interface to choose a target point the operator wants toselect on PCD using an interactive marker when automaticrecognition is failed on the FCU for perception.

Finally, OCU3 has modules which are used to control therobot for the driving task. In the driving task, we used asteering wheel and a pedal designed for racing games tocontrol the robot intuitively. Thus, OCU3 consists of onedevice manager for a racing joystick and two viewers thatshow image data from an iris camera. The device manager inOCU3 supervises this input device and sends joint commandsto OCU1 for THORMANG to drive a utility vehicle. Twoviewers show front circumstances to the operator. As shownin Fig. 5c, the left viewer in interface of OCU3 is a rawimage viewer from an iris camera and the other viewer isa bird’s-eye view of an iris camera. Especially, a bird’s-eyeview is an elevated view of the street from above, whichhelps the operator to recognize the distance.

D. Simulation System

To validate our motion planners and controllers, we usedtwo simulators, V-Rep for control algorithms and Robotic-sLab for motion planning. V-Rep is based on Vortex physicsengine producing high fidelity physics simulations. V-Repoffers real-world parameters such as recursive force andnon-linear friction model making the simulator realistic andprecise [13]. On the other hand, RoboticsLab has highcalculation speed in virtual world [14]. Thus, we could save

development time to validate our motion trajectories. Finally,the same code located on OCU1 could be operated to controlthe robot in virtual environment, because both V-Rep andRoboticsLab support remote API to customize the simulator.

III. UPPER-BODY CONTROL

In this section, we introduce upper-body controllers ofTeam SNU. The upper-body motion is generated by the taskcontroller which is based on Constrained Closed Loop In-verse Kinematics (Constrained CLIK) [15],[16], as follows:

q = J∗(xd +K(xd−x)), (1)

where q ∈ R, xd ∈ R, x ∈ R, and K ∈ R× are the jointvalue, the desired pose, the current pose of the arm, andsquare matrix for CLIK gain, respectively. The weightedpseudo-inverse of the Jacobian, J∗ ∈ R×, is calculated by,

J∗ = W−1JT (JW−1JT +λ2I)−1, (2)

where W ∈ R× is the weighted matrix for avoidance ofthe joint limit and the torque limit, λ is a scalar componentfor singularity avoidance, and I∈R× is an identity matrix.W is the diagonal matrix and calculated by considering themission state. For example, manipulation with a drill oftencauses a torque limit of the elbow joint. For this reason,weighting factor of the elbow joint is set a lower value thanother ones in wall task.

Meanwhile, λ is calculated by

λ =

{0 if w > θ

θ otherwise , (3)

where w is the manipulability of the arm, and θ is thethreshold. Additionally, the manipulability, w, is calculatedby,

w =√

det(JJT ), (4)

where det means a determinant with the square matrix.

IV. LOWER-BODY CONTROL

In this section, we discuss our locomotion controllers. Asshown in Fig. 6, the proposed locomotion control schemeconsists of three parts: the high level planning with footstepplanning, the low level planning including preview controlfor Center of Mass (COM) trajectory generation, and therobot control with the impedance control and the upper-bodyposture control. The subsections below describe the detailsof our approaches for the locomotion control.

A. High Level Planning

The robot needs desired footstep in order to move fromits current position to a goal position. Thus, the high levelplanning consists of the path planner and the footstep plan-ner. The path planner automatically generates a trajectorywhich passes exactly through the via points by using Hermitespline. The operator picks several points through which theCOM of the robot should pass on the 2D Map Viewer, andselects yaw angle of its pelvis at the final position. Next,the footstep planner calculates positions of the robot feet

High Level Planning

Map Generation

Path Planning

Footstep Planning

Low Level Planning

Ref ZMP Trajectory Generation

Swing Foot Trajectory Generation

Footstep

Pelvis Trajectory Generation

Preview control(COM Generation)

Estimation of Pelvis Trajectory

Upper-body Posture Control

(Pelvis Orientation Modification)

Robot Control with Local Feedback Controller

Gyro Sensor

Impedance control(Foot Trajectory Modification)

Force Sensor

InveraseKinematics

Pelvis & Foot Trajectory

Fig. 6: Overview of the locomotion control system

along with the spline curve within the constraints of thereference step length and width. The orientation of each footis determined by the tangential gradient of the nearest path.The reference step length and width were determined by trialand error through experiments.

B. Low Level Planning

The purpose of the low level planning is to generate a de-sired COM and swing foot trajectory for inverse kinematics.To calculate the desired COM trajectory, we used a previewcontrol which is the online pattern generation method forgenerating the COM trajectory with Linear Inverted Pendu-lum Model (LIPM), a reference Zero Moment Point (ZMP)plan, and the current status of the robot [17]. A basic previewcontrol algorithm with a ZMP based LIPM, however, has alimitation when a disturbance occurs or a reference ZMPtrajectory suddenly changes. To solve this problem, we usea modified preview controller [18]. The proposed algorithmcan prevent generation of discrete desired COM, becausethe reference ZMP trajectory is modified by considering thecurrent COM status and the current foot landing position.

The swing foot trajectory is generated in the Cartesiancoordinate based on the pelvis. In order to satisfy constraintson acceleration and velocity, we designed the foot trajectoryby fifth and sixth-order polynomial curves.

C. Robot Control with Local Feedback Controllers

The robot controller for locomotion is based on typicalinverse kinematics. In addition, we applied local feedbackcontrollers with FT sensors and a gyro sensor in order toincrease stability on the unexpected ground.

The impedance controller is used to modify the verticalmotion, roll, and pitch components of the swing foot tra-jectory, because the contact between the foot of the robotand ground is modeled by mass-spring-damper system [19],[20]. In order to estimate contact force on the ground, the

mass-spring-damper model between the foot of the robot andground is designed, as follows:

Fe(s) =des+ ke

mes2 +des+ keFm(s), (5)

where Fe(s), Fm(s), me, de, and ke are the estimated contactforce on the ground, the measured force of a FT sensor, theequivalent mass of the foot, the damping, and the stiffness,respectively. By using the estimated contact force, Fe(s), theimpedance controller for the modification of the swing foottrajectory is represented by

X(s) =Fe(s)

mrs2 +drs+ kr, (6)

where X(s), mr, dr, and kr are the displacement between de-sired and actual trajectory, the equivalent mass, the damping,and the stiffness between the foot and the pelvis of the robot,respectively. The coefficients of our impedance controller areobtained by experiments.

Next, the upper-body posture controller with a gyro sen-sor can modify the position and orientation of the pelvisby calculating geometrically. This controller calculates themodified trajectories of the feet in order to walk upright onthe inclined ground. However, the estimation of the slope isa difficult problem because the value of the gyro sensor is af-fected by many factors such as not only global inclination ofthe ground but also low accuracy of the actuators. Thus, wedecided that the slope of the ground was only updated duringDouble Support Phase (DSP). Consequently, the upper-bodyposture controller assures the stability of bipedal walking oninclined terrain.

D. DSP Duration Adjustment

This subsection introduces a strategical approach regardingthe stability of the robot. Dynamic compensation by usingthe local feedback controllers does not always guarantee itsstability. Thus, we established a strategy that DSP durationwas adjustable in balancing state. For example, if values ofFT sensors and a gyro sensor are bigger than thresholds,the robot could retrieve stability by increasing DSP durationuntil these values were found under thresholds.

V. RESULTS

This section contains the strategy of each task and theresults of our team’s performances in the DRC Finals 2015.Although we successfully had tested all tasks in our lab,Team SNU performed only four tasks in the DRC Finalsbecause of the bad ground condition and unexpected errorin RCU.

A. Experimental Tests

Although the proposed architectures and controllers arehelpful to operate complex tasks well, it is difficult to per-form some tasks because of the limitations of the mechanicaldesign such as short legs and low power of the actuators. Thechallenges Team SNU faced are summarized as follows.• Driving Task: How to hold the robot so that the wheel

and pedal can be controlled.

Fig. 7: Snapshots of the experiment for the egress task.

Fig. 8: Snapshots of the experiment for the stairs task.

Wall

Drive

Egress

Door

Valve

Time (min)

Fig. 9: The time distribution of each task at the DRC finals.

• Egress Task: How to get out of the car safely.• Stairs Task: How to overcome stair’s height.To solve these problems, we developed the strategies based

on our control algorithms. First, we established a strategythat the robot held the vehicle frame by the left hand andthe driving wheel by the right hand. Also, in order to pressthe pedal, we attached a pedal-assistant tool to the utilityvehicle because the robot could not reach to the pedal dueto its short legs in the driving mission. Second, in order toescape the vehicle, THORMANG used two arms to push the

body forward against vehicle and jumped out of the vehicle,as shown in Fig. 7. Finally, due to the limit of kinematicstructure of the robot, THORMANG was unable to ascendthe stairs by dynamic walking. Thus, our strategy for thestairs task was to walk statically by holding the left rail, asshown in Fig. 8. This strategy was helpful to overcome notonly stair’s height but also low accuracy of the actuators.

B. DRC Finals 2015 Results

Due to the uneven ground surface in stadium, most teamswhich participated with bipedal robot fell down on theground. Team SNU, however, successfully finished the com-petition without falling by using the proposed locomotioncontrol scheme. Fig. 9 and 10 show the analysis about timedistribution of each task and the snapshot of Team SNU atthe DRC Finals, respectively. Our performance at the DRCFinals is summarized as follows.• In the driving task, our operator used racing joystick

to drive the vehicle intuitively, as mentioned in Sec. II.Team SNU was the third fastest among the teams thatsucceeded the drive mission.

• We could not try the egress task because our strategyfor egress was not guaranteed that THORMANG suc-cessfully performed the egress mission on the inclinedground. The ground at the DRC finals had 3∼4 degreesof inclination.

(a) (b) (c)

(d) (e)

Fig. 10: Team SNU at the DRC Finals 2015: (a) THORMANG driving in utility vehicle, (b) THORMANG opening thedoor, (c) THORMANG turning the valve, (d) THORMANG cutting a hole in wall, (e) The operation room at the DRCFinals 2015

• When THORMANG opened the door widely, An un-known error occurred in RCU. So, we stopped thecompetition to check the robot system and got 10-minute penalty. After intervention, THORMANG couldgo through the door frame, as shown in Fig. 9.

• In the valve mission, the main focus was to recognizethe location of valve in degraded communication. Wesuccessfully approached to the valve by using our per-ception system and our robot turned the valve perfectlywithout any penalty.

• The wall task is one of the most difficult tasks in theDRC Finals, because it is hard to turn on the switch ofthe drill. Actually, only seven out of twenty three teamscompleted the wall task. However, with our gripper withpassive palm, THORMANG grabbed and turned on thedrill at the same time. Another problem of our team inthe wall mission is to avoid torque limit of arm joints,during cutting a hole. Due to this problem, only shoulderjoint was used to draw a circle, as shown in Fig. 10d.The detailed strategy for wall mission and its analysiswere described in [21].

As a result, we obtained 4 points for 58 minutes andour team ranked 12th place out of 23 teams. Althoughour conservative locomotion controllers guaranteed stabilityin uneven and inclined ground, Team SNU spent most ofperformance time with walking safely, as shown in Fig. 9.Thus, we did not perform all tasks in the DRC Finals due toa time-consuming walking. For details of our performance,please see the full video [22].

VI. CONCLUSIONS

This paper presents a technical and strategical approachof Team SNU to the DARPA Robotics Challenge Finals2015. With THORMANG by ROBOTIS, we developed thehardware & software architectures and the controllers forthe tasks. Especially, the end effector with passive palm wasoptimized for turning on the drill and the iris camera modulewith adjustable aperture is used to provide the operatorwith a bird’s-eye view for the driving mission. In softwarearchitecture, the operating interface was developed, whichcan receive the selected data by the operator during degradedcommunication. Finally, we designed control algorithms,robust to external disturbances. Although the terrain of thestadium was uneven and inclined, our robot did not fall

down during two runs in the finals. Consequently, with theproposed approaches, Team SNU ranked top twelve in theDRC Finals 2015.

However, there are some remaining issues that should bedealt with. Most of all, the reason that THORMANG couldnot complete all the mission lies in the fact that its walkingspeed was not fast enough for the sake of stability. Therefore,our future work will involve developing new hardware andcontrol algorithms that can perform robust and efficientwalking on uncertain environments better.

ACKNOWLEDGMENT

This research was supported by the MOTIE under theRobot industry core technology development project (No.10050036) supervised by the KEIT. We would like to thankROBOTIS for providing THORMANG and technical sup-port.

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