NENA 1.0: Novel Extended Nuclear Application for the

NENA 1.0: Novel Extended Nuclear Application for theSafekeeping of Contamination-free Environments

MICHAEL MARSZALEK, MARTIN EDERANDREAS TROPSCHUG and ALOIS KNOLL

Technische Universitat MunchenDepartment of Informatics -

Robotics and Embedded SystemsBoltzmannstr. 3, Garching bei Munchen

GERMANY{marszale, martin.eder, tropschu, knoll}@in.tum.de

HAGEN HOFERHOFER & BECHTEL GmbHOstring 1, 63533 Mainhausen

[email protected]

Abstract: The accomplishment of inspection, safekeeping or cleaning tasks are essential daily routines in industrialfacilities. These tasks are associated with time, personal expenses and physically demanding occupations. Variousrobotic systems have been introduced for the automation of these routines. The inspection and cleaning of seversor an automation of a biotechnology laboratory are successful examples for robotic systems and automation [1].In this paper, we present a novel system for the autonomous 2D detection of radioactivity and contaminated areas.The importance of autonomous detection of radioactivity will increase in the upcoming years, due to stricter limitsfor contamination levels, when using radioactive materials in nuclear medicine, research or for nuclear powerplants. The use of radioactive substances will also increase for diagnosis and therapy in nuclear medicine. Anotherarea of application is the decommissioning of nuclear installations, concerning work time and personnel expensessavings. The mobile robot is able to scan the whole floor of a building, where radioactive contamination mayoccur, such as floors in nuclear power plants, hospitals with nuclear medicine departments or laboratories. It marksthe contaminated areas in a generated map of the building for further evaluation. The paper discusses the mainrequirements of a navigation system for 2D detection of radioactivity and is based on [2]. It presents the hardwareand software set-up and some real-world experiments with our mobile robot.

Key–Words: Industrial Application, Automation in Medicine, Automation, Robotics, SLAM, Path planning

1 IntroductionGenerally, the challenging field of robotics has anenormous potential to support the industry in differ-ent fields. A robot is able to perceive and manipulatethe physical world by means of different devices andcan act in various working environments, for exam-ple planetary explorations, in the car industry or as ademonstrator in a theatre.The market for service robots has an excellent chancefor the future. An example for successful commercialsystems are cleaning robots which have been studiedand developed some years ago. Meanwhile, the mar-ket offers some cleaning robots for private purpose.The common enabling technology for a cleaning robotand our application is autonomous navigation in ev-eryday indoor environments. In contrast, the main dif-ference is the efficiency of our navigation system. Anexploration task is an integral part of a robotic missionwhich provides a full coverage of the indoor environ-ment. While a hoover robot usually cleans the floorrandomly, our system aims to document all radioactiv-

ity measurements for every reachable position in theenvironment. The complete and efficient coverage ofterrain is one of the elementary problems in reconnais-sance [3][4], rescue [5], cleaning [6] and planetary ex-ploration [7][8]. The handling of radioactivity in a nu-

Figure 1: Predecessor of NENA

clear power plant or any other nuclear installation as

WSEAS TRANSACTIONS on SYSTEMS and CONTROLMichael Marszalek, Martin Eder, Andreas Tropschug, Alois Knoll, Hagen Hofer

ISSN: 1991-8763 123 Issue 2, Volume 5, February 2010

well as in nuclear medicine, is subject to many restric-tions and relevant legislation, concerning the compli-ance of safety measures. One important safety taskis the securing of a contamination-free working en-vironment, typically needed for the personnel, work-ing inside the controlled areas. Usually, the measure-ments are done by hand (see Figure 1), why a mem-ber of staff has to invest a lot of time for the safe-keeping of a contamination-free building. It is nec-essary to prepare a documentation of every checkedplane of the floor or walls. In the case of contamina-tion, the exact position of the determined area has tobe marked in a map of the building. The applicationof robots could be extremely timesaving, unforgeableand reduces physically demanding occupations. Ourapproach was driven by the idea for automation of thesafekeeping of a contamination-free environment.

Robotino

Como170

Como170

Sick S300 laserscanner

IR sensorIR sensor

curcuit boardcurcuit board

display

laptop

Figure 2: Mobile Robot Exploded View

We equipped a mobile robot with various deviceswhich focuses on the detection of α, β/γ-radiationon planar surfaces. The complexity of our system ismanageable because we focused on the floor of the

areas. Thus, we did not need a robotic arm in or-der to reach walls and ceilings. The applied mobilerobot, called Robotino (see Figure 2), is equipped withan omnidirectional drive, three wheels and multiplesensors. It is able to move in all directions, as wellas to turn on the spot through its recent drive sys-tem. The control of the actuating elements and ac-quisition of sensor data results from the WLAN mod-ule. Alternatively, it is possible to use a networkcable for the communication with Robotino. FestoDidactic offers a software named ”Robotino View”,which is an interactive graphic programming tool forRobotino. Furthermore, an open-source framework,called OpenRobotino1, and the corresponding docu-mentation are available for the development of ap-plications. Robotino was additionally equipped witha laser rangefinder (SICK S300), two contaminationmonitors (S.E.A. CoMo 170, modified version) andan Intel Core Duo notebook for the developed nav-igation framework. The exploded view (see Figure2) visualises the hardware set-up with its extensions.The contamination monitor serves for the detectionof radioactive contaminated surfaces. The hardwarewas built into a new box, which is mounted in frontof Robotino. This box contains two detectors to at-tain a complete cover. An additional hardware featureare two infrared sensors, vertically mounted in frontof the box. Thus the detection of dangerous spotslike stairways or bigger holes can be avoided duringa mission in a nuclear power plant or other nuclear fa-cilities. The standard PC of Robotino is not capableof fulfilling the computation power requirement, whywe run the navigation framework on a mounted note-book. The power is supplied by two rechargeable 12Vbatteries with a rating of 4Ah. A full, in-depth pre-sentation of all features of our system is beyond thescope of this paper. Rather, we will focus on the corefunctions of the software respectively mobile robot.In section 2, we will briefly describe some recentlydeveloped robotic and navigation systems, followedby the overall architecture of the system in section3. To validate our approach we perform multiple ex-periments in section 4 to confirm the accuracy of oursystem. For this purpose, we received a permissionfor an experiment in a laboratory of the Max-Planckinstitute in Mainz. The Alpha Particle X-Ray Spec-trometer (APXS), which determines the consistencyof stones and dust on the mars surface, was originallydeveloped in this laboratory.

1http://www.openrobotino.org



2 State of the ArtIn the past, several efforts have been made, regard-ing the creation of autonomous mobile robot systems[9][10][11][12]. Since every creation had its specificrequirements and involves the combination of severalalgorithms, we will try to give a review for our workbelow. The problem that we are trying to solve falls inthe context of navigation systems. We therefore splitthe related work discussions into two distinct partsthat correspond to the respective subjects covered inthis paper, namely: SLAM and Exploration strate-gies for the full coverage of environments. A mobilerobot for the automation of the complete sample man-agement in a biotechnology laboratory is presented in[13]. The navigation of this mobile robot is based ona generated static map of the current working environ-ment. Another example, presented in [14], is the ser-vice robot LISA (Life Science Assistant) which sup-ports lab personnel in biological and pharmaceuticallaboratories. It makes automated experiment cyclesflexible and helps employees to prepare experiments,e.g. by collaboratively executing transportation tasksor filling microplates. A Hokuyo URG-04LX is usedfor the localization in an a-priori map and the collisiondetection. The map of the laboratory is handmade andis initially used for the localization. A generation ofa static map of the buildings is time-consuming andpresumes a new map for every domain. Our appli-cation needs a flexible adoption of the robot, whichrequires the ability to map and explore unknown en-vironments right from the start. The main technol-ogy for this purpose is called Simultaneous Localiza-tion and Mapping (SLAM) and has been in the fo-cus of many researchers for several years. SLAMdeals with the problem of constructing an accuratemap of an environment in real-time. Thereby, it hasto handle imperfect information about the robot’s tra-jectory through the environment. For example, a robotwhich was placed in an unknown environment, incre-mentally builds a consistent map of its surroundings,while simultaneously determining its location withinthis map. OpenSlam2 offers some open-source SLAMimplementations for 2D and 3D mapping. SLAM im-plementations are based on the type of input data.Some approaches are camera-based but we focused onthe algorithms which uses distance measurements of alaser rangefinder and odometry, allowing our systema deployment by night. A Rao-Blackwellized parti-cle filter based approach (GMapping) is presented in[15][16]. It is able to produce accurate maps of indoorand outdoor environments using occupancy grids. Theopen-source library requires some programming ef-

2http://www.openslam.org

fort for the adoption and only runs under Linux sys-tems. Another framework, called CARMEN [17],provides basic navigation primitives like path plan-ning, mapping and localization. We used some gener-ated log files for the evaluation of the mapping mod-ule. It works for small environments correctly, but itwas not able to resolve the loop closing problem inlarge buildings. The commercial framework KARTO3

is a very efficient solution for navigation tasks. It pro-vides exploration strategies, mapping and trajectoryplanning. A free functional trial copy is available fordownload. The evaluation of the mapping, using ourCARMEN log files, has been very promising.[18][19] DP-SLAM needs odometry measurementsand an accurate laser rangefinder, e.g. SICK S300,for localization and building a map. It uses a particlefilter to maintain a joint probability distribution overmaps and robot positions, as well as some efficientdata structures, which allow an efficient mapping.DP-SLAM does not need predetermined landmarksand is accurate enough to close loops without any spe-cial off-line techniques. The data association problemwas also eliminated through the abandonment of land-marks. Moreover, it is not necessary to predeterminethe environment. With almost no exception, none ofthe open-source distributions can be used without ad-ditional programming effort. The KARTO frameworkis an ideal solution for mobile robots, but we prefer anopen-source software, due to the costs for the licence.GMapping is an efficient and accurate SLAM algo-rithm but it needs some additional adaptation for ourapplication. In contrast, DP-SLAM was implementedand successfully tested during a past project of ourfaculty. It runs under Windows and Linux and canbe easily configured for our mobile robot. Addition-ally, a successful strategy for the full coverage of ter-rain, usually aimed by cleaning applications or plan-etary explorations, needs to be discussed [20]. Thetrajectory needs to be planned in a way, which guar-antees a path through the whole environment. [21]proposed an approach for partitioning the environ-ment into polygons and co-operatively arrange mul-tiple cleaning robots. An interesting system for au-tonomous cleaning of supermarkets was presented in[22]. A similar navigation system, developed for poolcleaning, is presented in [6].The main difference to our system is an authentic andexact documentation of contaminated areas in a hu-man understandable format. Thus a technical em-ployee can verify the results, measured while drivingthrough the terrain. An exact documentation demandsexcellent navigation frameworks with exact pose de-terminations.

3http://www.kartorobotics.com



3 Software Architecture

Hardware

Math, Driver, OpenRobotino

SLAM Trajectory planning

Applications

Figure 3: Software Architecture

Our navigation framework (see Figure 3) consistsof a SLAM library, which interacts with a trajectoryplanning module. Thus, the robot is able to plan apath within known parts of the map. The radioac-tivity detectors log the results of the measurements,while driving through the building. The hardware isaccessed via an API for Robotino and some additionaldrivers for the laser rangefinder and the contamina-tion monitors. Based on the software architecture, itis possible to develop various applications for mobilerobots. The topic of the paper is focused on radioac-tivity detection, and therefore we will address its maincomponents (SLAM and Trajectory planing) below.A SLAM library for our application needs to meet thefollowing requirements:

• Accurate determination of the pose

• Correct map of the building

• Handling of dynamic objects

• Real-time ability

• Easy configuration for various scanners

• Usability for Windows and Linux

Our SLAM library is based on an algorithm, calledDP-SLAM [23][19] and fulfills all listed require-ments, except handling of dynamic objects. Gen-erally, this requirement deals with state changes ofobjects in short time intervals. These dynamic ob-jects addresses humans, chairs or doors for example.

NENA avoids collisions with them by using the inte-grated bumpers and laser rangefinder measurements.Our group has long experience working with thisSLAM library and the results were always promisingfor indoor buildings. The library works with variouslaser scanners and allows an easy configuration, forexample particle amount or scanner resolution. It pro-duces an occupancy grid map as output, which is thebasis for the trajectory planning. This is why we pre-ferred this algorithm instead of familiarise with a newframework. We briefly review the basics of the algo-rithm and refer the reader to the detailed description ofthe algorithm in [19]. This SLAM algorithm is basedon a particle filter and an ancestry tree. A particlefilter, also called Monte Carlo method (SMC), is onepossible approach for localization. Like in a HiddenMarkow Model, it is necessary to define a state tran-sition, a hidden state and the observation. In this case,the robot’s position is the hidden state that should betracked. The state transitions represent the movementsof the robot and the corresponding observations areextracted from the sensor readings. All sensor read-ings are noisy or ambiguous, because no scanner is ad-equate enough to resolve ambiguities. The state tran-sitions are represented by a motion model, which actsas the proposal distribution in the sampling process ofthe particle filter.The ancestry tree is the core data structure for anefficient maintenance of particles. It represents theelapsed time during the mapping process. The leavesrepresent the current particles and a corresponding oc-cupancy grid.Austin Eliazar and Ronald Parr proposed two differ-ent versions of DP-SLAM in the course of develop-ment. A hierarchical version of DP-SLAM, presentedin [19], is able to recognise, represent and recoverfrom drift which results from various small errors dur-ing the particle filtering process. Generally, these er-rors accumulate over time and result in the mentioneddrift. Even if the local maps seem to be accurate, therecould be a large total error at the end. These errorslead to a misaligned map after completing a loop inchallenging domains. The small errors can be causedby an insufficient particle coverage, coarse precisionor the resampling of the particles. On the one handa finite number of particles causes that drift, on theother hand a high amount of particles increases therunning time of the algorithm. Experiments with thehierarchical version have shown, that the running timehas been dramatically increased, due to the two levelsof DP-SLAM. The real-time ability is an importantrequirement for our application, why our library usesonly one level of the algorithm. Our library worksmore efficient with dense sensor data, concerning lo-calization accuracy and mapping results. A sufficient



amount of particles was experimentally determined.The next section describes our test scenarios and thegreat mapping performance. The Trajectory Plan-ning is based on occupancy grid data, provided by theSLAM library. We differentiate between disturbances,dynamic stationary obstacles, free space and static ob-stacles by using probability values of the occupancygrid. For example, a high probability in a cell standsfor a static obstacle. The trajectory planning is con-tinuously updated with the current pose of the robotand the occupancy grid, which represents the environ-ment. This information is used by the algorithm inorder to place waypoints in free space. Each waypointdescribes a possible position of the robot. The dis-tance between the waypoints and the distance to wallscan be set optional, but it has to be chosen carefully toavoid collisions with static objects. A bounding box(geometry of the robot) is placed on every possibleposition next to the robot and a validation outputs theusability of this position. If the box is located insidethe explored area and no obstacle overlaps the box,the algorithm accepts that position as a possible way-point. A road represents the way between two way-points, and it also enables a collisions-free driving.Road mapping is implemented by a graph data struc-ture. It reduces the problem of negotiating a paththrough a complex shaped 2D representation of theworld to finding a shortest path through a connectedgraph from node A to node B, for example.The trajectory planning computes a path through theknown part of the environment, starting from therobot’s current position. The robot drives to everygenerated waypoint, as long as a waypoint exists inthe map. The map of the environment is regularly up-dated with changes in the surrounding and registra-tions of unseen parts of the building. The trajectorymodule reacts accordingly to these changes and ex-tends the path by new waypoints in these uncoveredareas. Thus, we abandon a foregoing Explorationphase which would prolong the whole radioactivitysearch process. The only one constraint for this ap-proach is the securing of a closed area. This is nec-essary to stop the trajectory planning from exploringnew parts of the building.There have been many research activities, which dealwith the problem of an optimal autonomous explo-ration in unknown environments. A mobile robot mustbe able to control its movement and its perception sys-tem so that it collects the highest amount of informa-tion possible, allowing it to localise itself in its en-vironment and to create a representation of this envi-ronment at the same time. Usually, the explorationstrategy tries to cover unknown parts of the environ-ment as fast as possible without re-localising itself inknown areas. This naive approach is sub-optimal be-

cause a good pose estimate is necessary to enable agood data association, i.e. to determine if the cur-rent measurements fit into the map built so far. Therevisiting of seen places in necessary for an accurateposition estimation in the map [27]. Our applicationareas are bounded in the size and we use a S300 laserrangefinder which provides up to 540 measurements.These dense data improve the localization and map-ping. The position estimation is always very accuratebecause the robot revisits known parts of the environ-ment. This is an advantage of the full coverage strat-egy. The Documentation of the radioactivity mea-surements is done by logging all measurements forevery reachable position in the map. For this purpose,the geometry of the box with its detectors, as well asthe deviation from the centre of the robot has to be de-fined. Additionally, all results are marked in the corre-sponding map. For example, the detection of radioac-tivity is marked as a radioactivity sign and clean areasare repainted in the map in green colour. The positionof detected radiation has a deviation of +/- 3 cm, dueto uncertainties in the localization process. The repre-sentation of the distance measurements, in form of acorresponding map of the terrain, allows the technicalemployee to verify the documentation results.

4 ResultsBefore this assignment, the sensitivity of the radiationdetectors was tested, using an old gas mantle from agas-powered camping light, which contains natural ra-dioactive Thorium (So called NORM = Naturally Oc-curing Radioactive Matter). The contamination mon-itors need to drive slowly in order to get a statisticalassured measurement result and to achieve a detectionlimit, which is considerably below the regular limits.The detection limit can be calculated according to theappropriate nuclear regulations. Since the detectionlimit is depending on the radionuclide mixture or nu-clide vector, it is necessary to adapt the speed of therobot to the specific measurement task.

To validate the SLAM library, we performedsome experiments in the computer science departmentin Munich. The evaluations were done online and off-line, in order to experiment with different parameters.The offline results are more accurate, due to the higheramount of particles, but also need a lot of computationtime. The online tests were done with optimized pa-rameters in order to enable a real-time mapping whileexploring a building. The first experiment deals withthe closing loop problem and is one of the difficul-ties in the mapping process (see Figure 4a). The maphas to be accurate after completing a loop. Concern-ing this goal, the robot travelled approximately 130 m



a) Third floor of the Computer Science Department

b) Robotics and Embedded Systems

Doors

Figure 4: Mapping Results

with a speed of 10 m per minute at the third floor ofthe computer science department. The size of the en-vironment is roughly 39 m x 16 m. It is a challengingdomain because of its asymmetric characteristics. Theexperiment illustrates, that is was possible to completethe loop, applying 100 particles for the localizationand a grid resolution of 35 grids per meter. This fig-ure was generated in an online modus while drivingthrough the corridor. So far, it was not possible tomap a bigger area. It leads to bad performance andthe probability of losing its own position dramaticallyarises.Another experiment (see Figure 4b) generated an ac-curate map of the faculty for Robotics and Embed-ded Systems, using the same SLAM configuration.Robotino travelled through the faculty with a speedof 10m per minute. The collected measurements weresaved in a log file, which could be used for off-lineexperiments. The railing with its balusters was ac-curately mapped, while moving along the hallway.Slight changes in the robot orientation affects, whichbalusters are hit by the laser, and which are missed.Robotino also passes an office at the left side. The

glass wall in front of the office produced an inaccuratemapping of the office because glass is a semi-opaquesurface to the laser. Glas stops the laser beams occa-sionally, due to dirt and angle of incidence. The resultwould get more accurate, if the robot revisits this partof the hallway a second time. The entrance door ofthe faculty is also shown, as well as the passage withsome doors. The door was opened while the robotmoved forward. Thus, it is possible to see the doorin a closed and an opened state in the figure. This isillustrated as a slightly grey colored line. The SLAMlibrary is even accurate enough to map the columnsand closed office doors which were passed. The sev-eral small grey points in the rooms are the table-legsand chairs. This exploration was stopped at the end ofthe passage and all saved measurements were used togenerate the illustrated map of the faculty.

Our approach is designed for laboratories, whichusually have a smaller geometry. Thus, we focusedon the overall performance of our system in the nextexperiment. The third scenario (see Figure 5 and 6)illustrates the operation in a laboratory of the formerMax-Planck Institute in Mainz, while interpreting the



Start

Start

LaboratoryHallway

t

Path

Figure 5: Search for Radioactivity

measurements of the radioactivity detectors. An ar-chitectural drawing of the controlled area of the for-mer Max-Planck Institute was additionally added forthe allocation of the corresponding room in the com-plex of buildings. The exploration progress is visu-alised by three points in time on the right side in fig-ure 5. The robot is randomly placed and starts its ex-ploration. Immediately after the first mapping of thesubarea, the trajectory planning starts setting the way-points in the map. We used the same configuration forthe SLAM algorithm as in the last described exper-iment. The maximal accepted distance of the S300is 8 m with 0.5◦ angle resolution over a 270◦ area.The waypoints keep a distance of 0.2 m to the walland to each other. We decided for a small distance

between the waypoints because it enables an approx-imated continuous path around obstacles. We aim afloor coverage, providing radioactivity measurementsfor every location. After reaching a waypoint, us-ing its omnidirectional drive, it marks this position ascompleted and takes the next waypoint as target posi-tion. Finally, it traverses the seen section of the envi-ronment by itself until every possible location in themap was reached. The mapping process of the com-plete room was achieved after some minutes, due tothe small size of the test terrain. We closed the door inorder to avoid a further exploration down the hallway.The architectural drawing, combined with the result-ing mapping results, demonstrates the accuracy of themapping process. The map of the hallway was created



in an additional run after finishing the experiment inthe laboratory. A critical situation (see Figure 6) is

Table- leg Path of Robot

Figure 6: Allocation: Map Position - Real World

the exploration under tables. The trajectory planningmodule checks the distances between the table-legs,concerning its own geometry, in order to verify thepotentiality of a collision-free path. Generally, the ex-ploration of these regions is only possible, if the robothas enough space to move. Some areas in the map arenot covered because of too narrow passages. The redarrows show the direction of the robot’s movement.It starts to drive with a speed of 40mm per secondalong the wall and continues its search in the middleof the room until every reachable position was com-pleted. The uncovered areas next to the walls resultedfrom a safe distance to the walls. Figure 5 also showsthat the robot did not cover some areas under the ta-bles because of the inapplicable geometry of the robot.The search for radioactivity was accomplished with-out any collisions. The green area stands for the cleanfloor. In the case of detected radiation a radioactiv-ity sign would mark the corresponding position in themap. The robot drove with a speed of 40 mm/s andneeded around 1 hour for the laboratory. Approxi-mately 70% of the laboratory can be covered at themoment. We work on some extensions, which shouldsolve that problem and guarantee a 100% coverage ofthe floor.

5 ConclusionIn this paper, we have presented a completely newapplication for securing of a contamination-free en-vironment. So far, a comparable approach has neverbeen deployed for this purpose. We extended a SLAMapproach with the ability to plan a path and driveautonomously through an indoor environment. In-spired by the Open Source community, we releasedthe SLAM library of our mobile robot on the Inter-net 3. By providing this service to the internationalrobotic community, we hope to attract other research

3http://www.openrobotino.org

groups to do the same and help to enlarge the ex-isting knowledge base in this field of research. TheDP-SLAM re-implementation with its extensions en-ables an adoption for various robots or laser rangefind-ers. There are some disadvantages, which have to besolved in future works. The SLAM algorithm is notable to map outdoor environments in real-time. Wesuggest to implement a Rao-Blackwellized particlefilter which is able to compute an accurate proposaldistribution taking into account not only the move-ment of the robot but also the most recent observation[15]. This approach reduces the uncertainty about therobot’s position and allows to track its own position inlarge-scale and outdoor domains. Another improve-ment is the implementation of an efficient occupancygrid through a Quadtree, for example. Both propos-als could drastically speed up the performance of theSLAM algorithm.The strategy for an optimal coverage of the floor needsto be adapted in order to guarantee a 100% coveragefor all possible environments. Another important fac-tor for a successful mission is the power supply inorder to guarantee preferable long operation times.We will resolve this claim by adding additional andmore powerful batteries. The current prototype usestwo 12V lead-gel rechargeable non-spillable electricstorage batteries, permitting a limited running timeof maximal 2 hours. We want to extend NENA byAutomotive Class Lithium Ion Cells in order to triplethe current running time. The experiments within thisframework have been promising. That is why we wantto extend the progress in near future. We aim for usingour approach for a real industrial application.

Acknowledgements: The authors would like to ac-knowledge the support of the Hofer & Bechtel GmbHand Robotic Equipment Corporation GmbH for thisresearch. We also thank Gerd Bauer for many helpfulsuggestions and comments.

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