Mission
Robotic assistance in surgical procedures offers the potential to enhance precision, reduce surgeon fatigue, and improve patient outcomes. This paper presents the development and implementation of a robotic manipulation system for suturing using a UR5e robotic arm. The system is designed to autonomously perform key steps in the suturing process, including needle acquisition, insertion through simulated tissue, and retrieval from the opposite side. A significant challenge in this work lies in achieving the precise and complex motions required for accurately driving a needle through the skin. This includes ensuring proper orientation and force application during insertion and maintaining smooth trajectories to minimize tissue deformation. Leveraging advanced motion planning algorithms and precise control strategies, the system successfully navigates these challenges. Experimental results demonstrate the ability of the robotic arm to perform repeatable and accurate sutures in a controlled setting, showcasing its potential as a foundational technology for robotic-assisted surgical tasks. Future work aims to enhance system robustness and incorporate real-time vision-based feedback for adapting to dynamic surgical environments.
Project Scope
The primary goal of this project was to develop and implement a robotic suturing system using the UR5e robotic arm and Robotiq gripper, focusing on autonomous needle insertion through simulated tissue. To reduce environmental variability and create a repeatable experimental setup, a tissue phantom was utilized. The tissue phantom was sourced from the Surgical Education & Activities Laboratory (SEAL) and consists of multiple layers of silicone designed to mimic the biomechanical properties of human skin, with an additional mesh layer to prevent the suture from tearing through the material. This design allowed for a more accurate simulation of real-world suturing conditions while maintaining controlled, consistent results.
A 3.5-inch sewing needle was selected for the task, as its size facilitated a secure grip by the Robotiq gripper. To further reduce variability and ensure repeatability in the task, a custom-designed stand was created to hold the needle in a fixed position and orientation at the beginning of each trial. This stand allowed for precise placement of the needle, eliminating any potential sources of error from the starting configuration.
The suturing task itself involved inserting the needle into the side of the tissue phantom, pushing it through the top surface, and then retrieving it from the opposite side. This sequence mimics the key steps involved in a surgical stitch, providing an anatomically realistic test case for the robotic arm.
Physical Implementation
The physical implementation involved several key steps, beginning with the procurement of an off-the-shelf needle suitable for grasping by the Robotiq gripper. To further facilitate the suturing process, a 3D-printed suture holder was designed and mounted onto the work surface. Alignment features were added to the needle and needle holder to ensure that the needle was placed consistently for each trial, improving the repeatability of the experimental setup.
The tissue phantom was obtained from the SEAL Lab and mounted onto the robotic platform. Wooden planks and clamps were used to secure the phantom in place, and additional tension was provided by a roll of tape placed underneath the phantom to elevate it and improve needle insertion accessibility.
To translate the robotic suturing task from the simulated environment to the physical robot, a few modifications were made to the source code. The difference in grippers used in simulation versus the physical system meant that certain aspects of the code needed to be changed. The names of all the UR5e joints were different, as well as all aspects of the gripper interface. All old gripper functions had to be replaced with new ones that communicated with the gripper on the physical system. In addition, the joint angle goals were revised to improve the smoothness and accuracy of the needle insertion and retrieval process. These joint angles were determined by manually guiding the robot through the desired motion and recording the corresponding joint angles displayed by the UR tablet. These values were then used in the execution script to ensure accurate, consistent motion during the suturing task.
Before conducting each physical trial, the planned path was simulated in URSim Docker to verify the accuracy of the robot's movements before testing on the expensive machine. Once the path was validated visually, the code was executed remotely to control the physical UR5e arm. Iterative adjustments to the joint angles were made based on performance feedback from each trial, with the goal of achieving a smooth, repeatable suturing process.
Project Scope
The primary goal of this project was to develop and implement a robotic suturing system using the UR5e robotic arm and Robotiq gripper, focusing on autonomous needle insertion through simulated tissue. To reduce environmental variability and create a repeatable experimental setup, a tissue phantom was utilized. The tissue phantom was sourced from the Surgical Education & Activities Laboratory (SEAL) and consists of multiple layers of silicone designed to mimic the biomechanical properties of human skin, with an additional mesh layer to prevent the suture from tearing through the material. This design allowed for a more accurate simulation of real-world suturing conditions while maintaining controlled, consistent results.
A 3.5-inch sewing needle was selected for the task, as its size facilitated a secure grip by the Robotiq gripper. To further reduce variability and ensure repeatability in the task, a custom-designed stand was created to hold the needle in a fixed position and orientation at the beginning of each trial. This stand allowed for precise placement of the needle, eliminating any potential sources of error from the starting configuration.
The suturing task itself involved inserting the needle into the side of the tissue phantom, pushing it through the top surface, and then retrieving it from the opposite side. This sequence mimics the key steps involved in a surgical stitch, providing an anatomically realistic test case for the robotic arm.
Evaluation and Iteration
The system's performance was evaluated based on the ability to consistently perform accurate and repeatable sutures in the tissue phantom. Performance metrics were evaluated visibly during each trial and included avoidance of all obstacles, accurate and precise needle insertion, and minimal tissue deformation to reduce the risk of patient injury. Iterative testing allowed for fine-tuning of the joint angles and gripper commands, ultimately achieving satisfactory performance where the robotic arm could repeatedly complete the suturing task with minimal visual deviation.