Parallel robot trajectory smoothing improves pancreatic surgery control
Towards AI-enhanced control: a numerical technique for trajectory smoothing of a parallel robot for pancreatic surgery
Robotics
Summary
Smooth robot movements help reduce harm during delicate pancreatic surgeries. The authors developed a method that makes these robot arm movements smoother by controlling how fast and how quickly they change speed. This system uses a 3D space mouse to control the robot in real time, ensuring gentle and steady motions. Their tests showed this approach avoids sudden jerks, making the robot safer and more precise in surgeries.
What this means in practice
- •For robotic surgery teams: Enable smoother control of surgical robot arms to reduce tissue damage in minimally invasive pancreas operations.
- •For robotic training developers: Incorporate trajectory smoothing techniques into AI-enabled training tools for improving surgical robot handling skills.
Authors
Iosif Birlescu, Alexandru Pusca, Bogdan Gherman, Calin Vaida, Ionut Zima, Damien Chablat, Doina Pisla
Abstract
The paper presents a numerical approach for the end-effector trajectory smoothing of a parallel robot designed for minimally invasive pancreatic surgery. The approach is tailored for real-time master-slave control architecture and uses a 3D space mouse for command input for velocity control. The trajectory smoothing is achieved by generating S-curves in the end-effector velocity fields, thus controlling the accelerations, which in turn reduces tissue trauma in the minimally invasive procedures. Real-time control is enabled by segmenting the S-curves based on the command inputs from the 3D space mouse. A special case is considered where the acceleration time is constant for all command inputs. Numeric results demonstrate stable transitions (without abrupt changes) in both the end-effector parameter space and in the active joints parameters, thereby validating the proposed approach. Further work aims to test the approach on an experimental model and integrate it into AI-based training modules.