Microsoft Word 85 87 docx J J Kang et al (Eds ) IICCC 2019, SCCTL 05, 2019 © The International Promotion Agency of Culture Technology 2019 A novel approach for collision avoidance in robot assisted su[.]
A novel approach for collision avoidance in robot-assisted surgery Quoc Cuong Nguyen1,2†, Tuan Hai Nguyen2 and Hyukdong Kwon3 Center for Application of Scientific and Technological Prorgress, Dong Nai Department of Science and Technology, Dong Nai, Vietnam 2Faculty of Technology, Dong Nai Technology University, Dong Nai, Vietnam 3Graduate School of NID Fusion Technology, Seoul National University of Science and Technology, Seoul, Korea Email:†nguyenquoccuong@dntu.edu.vn Abstract The limited workspace and potential for collision between the robot arm and surrounding environment are challenges in robot- assisted surgery In robot-assisted surgical procedures, the surgical robot’s end-effector must reach the patient’s anatomical targets without collision with the patient or surrounding instrument This paper proposes a novel approach for collision detection and avoidance method to create a collision-free path to enhance patient safety Experimental results showed that the proposed method successfully solves the collision avoidance problem in robot-guided surgery Keywords: Path planning, Medical robot, Collision detection, Collision avoidance, Anterior cruciate ligament Introduction Nowadays, computer-assisted surgical (CAS) systems are widely used in the surgical field In anterior cruciate ligament (ACL) reconstruction surgery, for example, a CAS system (or computer navigation system) can be used to assist the surgeon in accurately locating the ideal graft placement site For graft placement, the surgeon makes one or two tunnel(s) on both the tibia and femur [1] The placements of these tunnels for ACL reconstruction are determined using preoperative planning software with patient CT data [2] Then, the grafts are inserted into the tunnels to connect the tibia and femur In an operating room, many medical instruments, including the robot, are installed around the patient During surgery, the path of the surgical robot’s endeffector should be optimally planned to avoid collision with the patient and other instruments Although previous studies have proposed methods to solve the problem of path planning for mobile and industrial robots in two-dimensional (2-D) and three-dimensional (3-D) spaces [3-6], only a few approaches have been addressed for surgical robots [7-9] In this paper, we propose a novel approach to detect and avoid collisions between the robot end-effector and patient (or tracking markers attached to the patient) Phantom experiments were performed to verify the simulation results Methods To enhance patient safety, it is necessary to detect and avoid any collision between the robot end-effector and the surrounding objects during the operation The robot’s end-effector may collide with the patient or the optical tracking markers attached to the patient, as illustrated in Fig 2(a) The geometry of the tracking markers, patient, and robot end-effector was simplified for fast collision detection using the oriented bounding boxes method [10] J.J Kang et al (Eds.): IICCC 2019, SCCTL 05, 2019 © The International Promotion Agency of Culture Technology 2019 250 Fig Modification of path for collision avoidance First, the position of an obstacle (e.g., optical tracking marker) is determined with respect to the reference coordinate system of the optical tracking camera Next, a modified path for collision avoidance is calculated based on the original planned path and position of the obstacle The original path is discretely separated using distinct points, as shown in Fig Here, ⃗ is the normal vector of the cutting plane in the path planning step, and ⃗ is the direction vector from Pi to Pi+1 on the planned path ⃗ is the correction vector from Pi (on the r r r c = n ´ d original path) to Pi' (on the modified path) obtained by the cross product , where ⃗ = (c1, c2, c3) The parametric equations of a line in a 3-D space with parameter ti at Pi are defined as follows [11]: = + , = + , = + (1) where xi, yi, zi, and xi', yi’, zi’ are the coordinates of Pi and Pi' on the planned path and modified path, respectively From (1), ti is calculated: ( ) ( ) ( ) = (2) In (2), the numerator is the distance Li between Pi and Pi’, which is continuously changing from zero at the start of path, to the maximum value at the obstacle position, and then back to zero at the end of the path, as shown in Fig If Li is known, parameter ti can be calculated from (2), and xi’, yi’, zi’ are determined from (1) Finally, the modified path is the curve that passes through the distinct calculated points Validation test We validated the proposed methods of path planning using a 3-D virtual simulation In addition, to verify the virtual simulation results, we performed experiments using a robotic system (VS068, Denso, Japan)[12], an optical tracking system (NDI, Canada)[13], and a phantom lower limb (Kyoto Kagaku, Japan)[14], as illustrated in Fig 2(b) 251 Fig 2(a) 3-D virtual simulation layout with robot, patient, and tracking markers, and (b) experimental layout of operating room to validate proposed method Results and Discussion The collisions between the robot end-effector and markers attached to the patient were checked as the robot moved along the planned path, as shown in Fig (marked in red) Fig (a) Collision detection of robot end-effector with (a) femoral marker and (b) tibial marker In Fig 3(a), when the robot end-effector detects a collision with the femur marker, the text on the screen changes from green to red, whereas when no collision occurs between the patient’s leg and tibia marker the text is displayed in green Similarly, in Fig 3(b), the robot end-effector collides with the tibia marker, and the text is displayed in red After detecting a collision, the proposed collision avoidance method is applied The positions of the tracking markers may vary with changes in their visibility by the optical camera, as shown in Fig 4(a) 252 Fig (a) Modified trajectories from original trajectory according to marker position changes, and (b) corresponding phantom legs An experiment was also conducted on the robot end-effector movement to verify the path planning modification (Fig 5) The results showed that there was no collision between the robot end-effector and femoral marker, as illustrated in Fig 5(a) (displayed in green) To verify the simulation results, we conducted an experiment with the same conditions using an actual robot and a phantom leg The experimental results showed that the proposed method successfully solved the collision avoidance problem with various tracking marker positions (Fig.5(b)) Fig (a) Simulation with marker position to verify collision avoidance method and (b) experimental setup with actual robot and phantom leg 253 Conclusion We presented the collision avoidance methods for robot-assisted surgery based on the patient geometry and surgical targets For example, we demonstrated the use of the proposed method for robotic and optically guided ACL reconstruction surgery Simulation and experimental results verified that the proposed method successfully generated the robot end-effector path to reach the surgical targets while avoiding collisions with obstacles considering patient movement In this study, path planning and collision avoidance for robotic surgery were attempted for the first time on a phantom, and we plan to apply the proposed method to clinical use in future experiments References [1] Yamamoto, Y., Ishibashi, Y., Tsuda, E., Tsukada, H., and Toh, S., “Single- Versus Double-Bundle Anterior Cruciate Ligament Reconstruction Results Using Navigation: The Japanese Experience,” Operative Techniques in Orthopaedics, Vol 18, No 3, pp 173-180, 2008 [2] Choi, J., Lim, S., Kim, Y., Lee, D., Park, S., “3D Preoperative Surgical Planning Software for Anterior Cruciate Ligament Reconstruction,” 13th Int’l Conference on Control, Automation and Systems, Gwangju, Korea, pp 344346, 2013 [3] Belkhouche, T., “Reactive Path Planning in a Dynamic Environment,” IEEE Transactions on Robotic, Vol 25, No 4, pp 902-911, 2009 [4] Berg, J V., Abbeel, P., and Goldberg, K., “LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information,” The International Journal of Robotics Research, Vol 30, No 7, pp 895-913, 2011.Brand, M., Wehne, N., and Yu, X H., “Ant Colony Optimization for Robot Path Planning,” Proceedings of the IEEE Int’l Conference on Computer Design and Applications, pp 436-410, 2010 [5] Yu, J., Zhang, Q., Kroumov, V., Cheng S, and Zhang, Z., “Path Planning Algorithm for Robot in 3D Environment Based on Neural Network,” ICIRA 2008, Part I, pp 1081–1088, 2008 [6] Ye, R., and Chen, Y., “Path Planning for Robot Assisted Femur Shaft Fracture Reduction: A Preliminary Investigation,” Int’l Conference on Virtual Environments, Human-Computer Interfaces and Measurements System, Hong Kong, China, pp 113-117, 2009 [7] Ye, R., and Chen, Y., “Path Planning for Robot Assisted Femur Shaft Fracture Reduction: A Preliminary Investigation,” Int’l Conference on Virtual Environments, Human-Computer Interfaces and Measurements System, Hong Kong, China, pp 113-117, 2009 [8] Zhang, J., Roland, J., Manolidis, S., and Simaan, N., “Optimal Path Planning for Robotic Insertion of Steerable Electrode Arrays in Cochlear Implant Surgery,” Journal of Medical Devices, Vol 3, pp 1-10, 2008 [9] Jackson, R.C., and Cavusoglu, M C., “Needle Path Planning for Autonomous Robotic Surgical Suturing,” Proceedings of the IEEE Int’l Conference on Robotics and Automation, pp 1669-1675, 2013 [10] Gottschalk, S., Lin, M., and Manocha, D., “OBBTree: A Hierachical Structure for Rapid Interference,” Proceedings of ACM Siggraph, pp 171-180, 1996 [11] Stewart, J.S: Calculus: Early Transcendentals, Thomsom Publishing Company, CA 94002, USA, 2008 [12] Denso Wave Inc, http://www.denso-wave.com/ [13] Northern Digital Inc, http://www.ndigital.com/ [14] Kyoto Kagaku Co., Ltd, http://www.kyotokagaku.com/ ... Human-Computer Interfaces and Measurements System, Hong Kong, China, pp 113-117, 2009 [8] Zhang, J., Roland, J., Manolidis, S., and Simaan, N., “Optimal Path Planning for Robotic Insertion of. .. (NDI, Canada)[13], and a phantom lower limb (Kyoto Kagaku, Japan)[14], as illustrated in Fig 2(b) 251 Fig 2(a) 3-D virtual simulation layout with robot, patient, and tracking markers, and (b)... Modified trajectories from original trajectory according to marker position changes, and (b) corresponding phantom legs An experiment was also conducted on the robot end-effector movement to verify