Design and Simulation of 5-DOF Vision-Based Manipulator to Increase Radiation Safety for Industrial Cobalt-60 Irradiators

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1 Arab Journal of Nuclear Science and Applications, 94 ( ), (5-6) 6 Design and Simulation of 5-DOF Vision-Based Manipulator to Increase Radiation Safety for Industrial Cobalt-6 Irradiators A.E. Solyman, *, M.R. Roman, A.B. Keshk and K.A. Sharshar Radiation Engineering Department National Center for Radiation Research and Technology, Atomic Energy Authority, Egypt Mechanical Power Engineering Deprtment Faculty of Engineering, Helwan University Received: 5//6 Accepted: //6 ABSTRACT Robotics has proved its efficiency in nuclear and radiation fields. Computer vision is one of the advanced approaches used to enhance robotic efficiency. The current work investigates the possibility of using a vision-based controlled arm robot to collect the fallen hot Cobalt-6 capsules inside wet storage pool of industrial irradiator. A 5-DOF arm robot is designed and vision algorithms are established to pick the fallen capsules on the bottom surface of the storage pool, read the information printed on its edge (cap) and move it to a safe storage place. Two object detection approaches are studied; RGB-based filter and background subtraction technique. Vision algorithms and camera calibration are done using MATLAB/SIMULINK program. Robot arm forward and inverse kinematics are developed and programmed using an embedded microcontroller system. Experiments show the validity of the proposed system and prove its success. The collecting process will be done without interference of operators, hence radiation safety will be increased. Keywords: Robotic Arm/Computer vision/camera Calibration/ Industrial Irradiator/ Source Rack. INTRODUCTION Industrial Cobalt-6 irradiators () maintain many active results through successful irradiation processing. Fig. (a) shows a cross-sectional elevation of typical Cobalt-6 irradiator. As shown, the source rack, when not in use, is stored near the bottom of a 5.5 m deep storage water pool. It is raised from the pool to the irradiation position by a pneumatic hoist mounted on the roof of the facility above the radiation shield. A stainless steel hoist cable attached to the source rack passes through the shield and the roof to two sets of sheaves in the hoist cylinder that are moved by the pneumatic piston. The movement of the source rack is guided by two taut guide cables, one at each end of the rack (see Fig. (b)). On being raised, the piston guide of the hoist cylinder actuates one micro-switch mounted on the hoist cylinder to indicate that the source rack is no longer down; thus for any other position of the source rack than fully down, the micros-witch should indicate that the source rack is up. When air is exhausted from the source hoist, the source rack is returned under gravity to the safe storage position in the water pool. The weight of the source rack pulls the sheaves in the hoist cylinder back together, and the micro-switch is de-actuated to indicate that the source rack is down. Fig. (b) shows the source rack with six source modules, each containing up to 4 source pencils with two standard source elements in each pencil. The target capsules are Cobalt-6 or dummy which are arranged in their modules in the source rack. One pencil (capsule) is about 4gram weight; 45.cm length and.mm diameter. The Cobalt-6 capsules have intense blue light due to Cerenkov radiation that is visible in the storage pool when the source rack is submerged (). Correspondingauthor eng.ahmed88@yahoo.com 5

2 Arab Journal of Nuclear Science and Applications, 94 ( ), (5-6) 6 Radiation processing is a regulated industry that has been operating safely for more than 4 years in usual commercial and business parks. The workers in these facilities wear normal everyday clothing and comply with standard health and safety regulations ().Yet it is recognized that large quantities of radioactive material located at one place for any purpose pose a potential hazard to people (workers as well as general public) and the environment, indicating the need to achieve a high degree of safety and reliability in the use of these sources. Host cable Source in safe position Guide cables Source rack holding six source modules Source module containing 4 source capsules Source capsule containing two source elements Fig. (a) A cross-sectional elevation of the irradiation facility and (b) Source rack () However, some major problems are recorded in some irradiators in different countries around the world (). One of these problems is source stuck between source rack and horizontal product conveyors and probability of falling Cobalt-6 capsules inside storage pool. The traditional methods used today for the solution of the above mentioned problem depend on the expert operators, who use special collection tools manually to collect the fallen capsules in the deep storage pool. This method consumes a lot of time and effort and increases the dose rate to operators as they work directly with the source. According to the principle of ALARA and radiation safety series (), the present study solves the mentioned problem without interference of operators for radiation safety. It presents the possibility to use robotics instead of humans to work in such hazardous places. From 99 to 996, the European commission ran a research program, TELEMAN, with the aim to develop robots for use in radiation environments, in particular with a view to the nuclear power industry (4).In, the American Nuclear Society (ANS) issued an important report, for engineers, containing some example applications of using robotics in radiation environments found in some past applications such as Three Mile Island (TMI) and Fukushima nuclear power plants (5). Szabo et al. () presented an application to sort colored objects with a robotic arm which picks different colored cubes and sorts them in different cups (6). Yan et al. (5) described industrial sorting system based on robot vision technology and introduced the main image processing methodology (7). This work investigates the possibility of using a vision controlled arm robot to collect the fallen Cobalt-6 capsules inside the wet storage pool instead of the traditional manual method. The robotic arm is designed, in a manner that makes its inverse kinematics is driven and integrated in vision algorithms specially designed to capture and detect fallen capsules. All the experiments were carried out in a dry non-radioactive environment to validate the designed system. 5

3 Arab Journal of Nuclear Science and Applications, 94 ( ), (5-6) 6. METHOD AND MATERIAL Figure describes the test rig used in the current research. The system consists of a 5-DOF arm robot in which the first joint is prismatic and the other four joints are revolute. The prismatic motion is achieved by a stepper motor (Motor) while the other four are achieved by servo motors (Motor to 5). A gripper having the capability to manipulate small-size objects is attached at the end of the arm so that the wrist of robotic arm offers motion in pitch and roll directions. The overall structure allows picking any capsules-like object at arbitrary orientation on a plane surface simulating the ground of the irradiator storage pool. A fixed camera is used to capture the location of the objects on the ground. The images are sent to a PC of moderate to high processing capability for image processing and running of vision algorithms. The position and rotation of the object (fallen capsules) is then sent to Microcontroller Arduino Mega (ATmega8) through Data Acquisition card (NI 68). The microcontroller uses the robot inverse kinematics equations and orders the robot motors to pick the objects and move them to a safe storage area. Motor Motor Camera Motor Object Motor4 Motor5 Microcontroller Data-Acquisition PC Fig. )(: The proposed test rig. ROBOT ARM KINEMATIC The study of robot kinematics problem can be carried out using the well-known method of Denavit-Hartenberg (DH) (8-9). This method is a systematic in nature and more suitable for modeling serial manipulators. DH method has been used in the current study to develop the kinematic because of its versatility and acceptability for modeling of serial robots with any number of joints and links ().In the next two sub-sections we discuss both forward and inverse kinematics of the proposed robot.. Robot Arm Forward Kinematics Given the link lengths and joint angles of a robotic arm, forward or direct kinematics computes the end-effector position and orientation. This usually leads to a series of equations to be solved together. Figure shows the arm robot layout and the different frames taken on it where, frame {S} denotes the station coordinates, {} denotes the base coordinate, {} to {5} denote the coordinates of different arm joints, {T} denotes the tool or end-effector coordinates and {G} denotes the goal coordinates. 5

4 Arab Journal of Nuclear Science and Applications, 94 ( ), (5-6) 6 Fig. )(: The designed manipulator coordinates of all joints After frame assignment, DH parameters defined as: αi-= the angle from Zi- to Zi measured about Xi- (link twist); ai-= the distance from Zi- to Zi measured along Xi- (link length); di = the distance from Xi- to Xi measured along Zi (link offset); θi = the angle from Xi- to Xi measured about Zi (joint angle). are determined. Table shows the values of the DH parameters for the proposed robotic arm. Table (): The D-H parameters for the designed robot i 4 5 αi- ai- -9 L L di d L Ɵi Ɵ Ɵ Ɵ 4 Ɵ 5 Based on these parameters, each joint frame (i) can be expressed relative to its preceding frame (i-) (7-8) using the general transformation matrix: cosi sin i ai i- sin i cos cos i cos sin sin d Ti= i i i i i )) sin i sin i cosi sin i cosi cosid i We can formulate the transformation matrix between each two successive frames as follow: T = Cos, T = Sin Sin Cos Cos, T = Sin Sin Cos L 5

5 Arab Journal of Nuclear Science and Applications, 94 ( ), (5-6) 6 Cos T 4 = Sin 4 4 Sin Cos 4 4 L Cos 5, 4 T 5 = Sin 5 Sin Cos 5 5 )). Robot Arm Inverse Kinematics In the inverse kinematic problem the target is to find the manipulator parameters d, θ, θ, θ 4 and θ 5 to bring the tool frame {T} coincide with the goal frame {G} (9) : T 5 = T. T. T. T 4. 4 T 5 = T S. S T G. G=T T 5 Given the goal position relative to the station frame ( S T G ) we can solve for the manipulator parameters through a series of algebraic equations. The closed form solution of these equations is found to be: Cos Cos { S x [( L { Ɵ 4 = (Ɵ +Ɵ ) G +(L L )L L L L ) L + L (L Sin ] + [(L + L + L Ɵ 5 = β (Roll angle=rotation angle of {G}) d = S z G L + L ) } Cos Cos where L, L, L, L are links lengths, X G, Z G and β are the goal (capsule) position and orientation relative to frame {S}. 4. COMPUTER VISION The camera captures the image and sends it to a PC where the vision algorithms are set. The PC processes the image and sends the position and orientation data of the objects (capsules) to the microcontroller through the data acquisition system, see Fig.. The microcontroller solves the robot inverse kinematics and sends the motion signals to the robot motors to move it to grasp the objects from the floor and place them in a safe place. User just has to input target storage place. The robot uses this information together with the developed robot model and image processing algorithm to move the picked capsules to the required place thus accomplishing its task, see Fig. 4. ) S x G ] } () (4) Object Position Camera Picture Image Processing Object Position is determined in Pixels Camera Model Object position relative to {S} in (mm) End-effector Position Robot Robot Variables d, θ, θ, θ4, θ5 Robot Inverse Kinematics Fig. (4): Block diagram of the vision controlled robot arm 5

6 Arab Journal of Nuclear Science and Applications, 94 ( ), (5-6) 6 4. Camera Calibration In the vision system, camera calibration is the key step of deciding precision in the whole sorting system (). The purpose of camera calibration is to establish the relevant coordinate system and get relationship model between image coordinate and space coordinate system (). According to the corresponding relationship between characteristic points, calibration process system calculates the camera intrinsic parameters and extrinsic parameters (). MATLAB camera calibrator toolbox is used to form the (pixelmm) governing equations: S X G =.6 x image 8.5 S Y G =.6 y image (5) where (x image, y image ) represents the capsule position in pixel and ( S X G, S Y G ) are in mm. 4. Computer Vision Algorithms The proposed algorithms should guide the gripper to grasp the objects at its centroid while maintaining the same orientation. The blob analysis in computer vision toolbox of MATLAB is used to measure a set of properties such as area, centroid, bounding box and orientation for objects in an image file. In the current research we investigate two vision algorithms to detect objects:(a) RGB based filter and (b) background subtraction. The details of the proposed SIMULINK models are explained in the next sections. 4.. RGB-Based Filter Algorithm As the Co-6 capsules have intense blue light due to Cerenkov radiation that is visible in the storage pool when the source rack is submerged in the water pool. This color can be used to identify the fallen cobalt capsules (blue). So, an algorithm for object detection by color can be designed. The Simulink model for this algorithm mainly consists of two parts; first identifying the RGB of objects and then processing the blob analysis, see Fig. 5. For the RGB identifying (4), color analyzer program is used to identify the RGB values of objects. Fig. (5): RGB-Based Simulink block diagram The RGB filter subtracts the required RGB from the RGB of each pixel in the image. The values of the RGB subtraction are then compared with a threshold number to convert the image to a binary image (B/W) illustrating the identified objects only. This image will be passed to the blob analysis block in order to obtain the boundary box, centroid and the corresponding area, (Fig. 6). An important condition is set to the area that is, only objects with area between to 5 are identified this is done to avoid shadows and other light reflections to be identified. 55

7 Arab Journal of Nuclear Science and Applications, 94 ( ), (5-6) Background Subtraction Algorithm Fig. (6): Subsystem that determines the blob analysis Background subtraction (5), also known as Foreground Detection, is a technique in which image s foreground is extracted for further processing. The basic idea of background subtraction method is first to initialize a background, and then subtracting the current frame in which an object present from the background frame to detect the object. This method is simple and easy to realize, but it is sensitive to the change of external environment, so it is applicable in the condition that the background is known and fixed. Figure 7 describes the SIMULINK implementation of the algorithm. The device source block represents the source of the input data (image). After converting the image to an intensity image it is passed to a main subsystem block. A median filter is used over time and then it is subtracted from the original image. After that we can use the blob analysis as in Fig. 6. Fig. (7): Background estimation Simulink block diagram 5. EXPERIMENTAL WORK Experimental work has been conducted to validate the research idea. The two previously studied algorithms (RGB-based filter and background subtraction) are implemented to control the 5-DOF robotic arm using an independent joint control mechanism. As shown, Fig. 8 illustrates the test rig that has been used to test the performance of the proposed algorithms as implemented to the designed robotic arm. In order to move the robot arm to any position, the values of joints angles should be available, therefore the inverse kinematics is solved to convert the Cartesian space into joint space and specify each joint angle. The angle value is digitized by the microcontroller to generate discrete motional steps. The data of object position (x G, y G ) should be in (mm) so, camera calibration should be done as illustrated before to compute (pixel mm) relation. The home position of the robot arm is at x G = and joint angels equal to9. 5

8 Arab Journal of Nuclear Science and Applications, 94 ( ), (5-6) 6 Fig. (8): Exprimental test rig with robot arm in home position 5. Object Detection The proposed image processing algorithms start with a given picture and ends up with some information about a certain object (position, orientation and object number). The studied objects (capsules) position and orientation are considered the required data to be determined by image processing algorithms. The first step of the experiment work is to capture image of the work space and send it to the computer, then the required data can be computed using the researched algorithms. Fig. 9 represents the captured image of the objects inside the workspace before using the algorithms. Fig. (9): Captured image of work space using the test rig camera After capturing the image as shown in Fig. 9 (pre-image processing), the next step is to filter out the target objects using the studied algorithms of RGB filter and background subtraction. These algorithms convert the captured image into black and white image. Then by using the blob analysis, the object required information is determined. Fig. shows the filtered (black and white) image using the proposed algorithms. As can be noticed, the image that is filtered using background subtraction algorithm, Fig. (b) is more clear than the image which is filtered using RGB filter, Fig. (a). So, the object position and orientation data, if calculated using subtraction algorithm, will be more accurate than RGB algorithm. (a) Fig. (): The filtered image: (a) using RGB algorithm and (b) using background subtraction algorithm (b) 5

9 Arab Journal of Nuclear Science and Applications, 94 ( ), (5-6) 6 Objects detected through the image subtraction algorithm, see Fig., where black box is drawn around the detected objects, are then processed through blob analysis to compute the required data, see Table. (a) (b) (c) Table (): The objects position / orientation and inverse kinematics solutions From image To robot image x (pixel) y (pixel) β ( º ) Ɵ ( º ) Ɵ ( º ) Ɵ 4 ( º ) Ɵ 5 ( º ) d (mm) a º 69.85º.8º 7.º 8.46º.5 b º 57.66º.5º 7.6º 9.7º 6. c º 77.5º 45.º º 4.84º 44 d º 6.6º 7.º 4º 84.4º 99.5 e º 77.9º 45.5º.95º 5.8º Grasping Object (d) (e) Fig. (): Objects detection using the background subtraction technique The steps of picking and placing object, shown in Fig. (c), are illustrated in Fig.. As can be seen in Fig. (-) the robotic arm moves in x-direction a prismatic distance (Z G ). In Fig. (4-6) the arm moves to swoop the object, and in Fig. (7-4), the object is elevated by the arm to capture the data printed on its edge and then is moved to the storage place. Finally the arm returns back to home position. In Fig. (5) the arm is directed to a new object and so on.. () () () 5

10 Arab Journal of Nuclear Science and Applications, 94 ( ), (5-6) 6 (4) (5) (6) (7) (8) (9) () () () () (4) (5) Fig. (): Robot arm detecting and grasping objects 6. CONCLUSION AND RECOMMENDATION The desired research objective was accomplished in three stages: the first stage was to provide forward and inverse kinematics solutions for the designed robot manipulator using DH method. In the second stage, the electro-mechanical system of the robot arm was established. In the third stage, the camera calibration techniques and computer vision algorithms used for object detection where discussed. All simulations were presented using MATLAB and SIMULINK. The path of the robot starts as objects enter the camera scope and ends by placing them in a storage place. Camera calibration was done using MATLAB camera calibrator toolbox to extract the intrinsic and extrinsic parameters. Two vision algorithms were used; the RGB filter and the background subtraction algorithm. The subtraction algorithm was more accurate than RGB filter. The results show that the robot arm has the ability to grasp the target filtered objects and follow a path planned to capture an image for the grasped object and place it in a storage area. 5

11 Arab Journal of Nuclear Science and Applications, 94 ( ), (5-6) 6 The research results have proved their success. However applying the results on the real environment of the cobalt_6 irradiators necessitates considering other factors. This includes working in a radioactive environment and inside a water pool. Hence, it's advisable to choose radiation and rust resistant materials (Stainless steel for example), wire insulators, well insulated motors, etc., when implementing the research in a real environment. As a future work plan, the following advancements are suggested: To enhance the vision system visibility eye-in-hand or eye-to-hand can be used. Two cameras above the robot work space may also be used. To reduce the grasping error a feedback system can be used, see Fig.. For this action, the gripper position will be required. This algorithm depends on (video processing). Object Position Camera Picture Image Processing Object Position is determined in Pixels Feedback Algorithm End-effector Position Camera Video Robot Video Processing Robot Variables d, θ, θ, θ4, θ5 End-effector Position is determined in Pixels Robot Inverse Kinematics Correcting Signal Fig. (): The suggested closed-loop control block diagram REFERENCES () INTERNATIONAL ATOMIC ENERGY AGENCY, Gamma Irradiators For Radiation Processing, IAEA DGPF/CD, Vienna (4). () Barabanova, A., J. R. Croft, and D. M. Delves. "The radiological accident in Soreq." Vienna: IAEA (99): -7. () Meissner, J., Kishor Mehta, and A. G. Chmielewski. Radiation safety at irradiation facilities. na, 8. (4) Lauridsen, Kurt, et al. Reliability and radiation tolerance of robots for nuclear applications. Risoe National Lab., Roskilde (Denmark). Systems Analysis, 996. (5) Reid L. Kress. Robotics, Remote Systems, and Radiation. American Nuclear Society. July, (6) Szabo, Roland, and Ioan Lie. "Automated colored object sorting application for robotic arms." Electronics and Telecommunications(ISETC), th International Symposium on. IEEE,. (7) Yan, Juan, and Huibin Yang. "Research on Workpiece Sorting System Based on Machine Vision Mechanism." Intelligent Control and Automation 6. (5):. (8) J. Denavit and R. S. Hartenberg. A kinematic notation for lower-pair mechanisms based on matrices. Journal of Applied Mechanics, pages 5, June 955. (9) Craig, John J. Introduction to robotics: mechanics and control. Vol.. Upper Saddle River: Pearson Prentice Hall, 5.

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