Online Trajectory Planning in Dynamic Environments for Surgical Task Automation

Automation of robotic surgery has the potential to improve the performance of surgeons and the quality of the life of patients. However, the automation of surgical tasks has challenging problems that must be resolved. One such problem is adaptive online trajectory planning based on the state of the surrounding dynamic environment. This study presents a framework for online trajectory planning in a dynamic environment for automatic assistance in robotic surgery. In the proposed system, a demonstration under various states of the environment is used for learning. The distribution of the demonstrated trajectory over the environmental conditions is modeled using a statistical model. The trajectory, under given environmental conditions, is computed as a conditional expectation using the learned model. Because of its low computational cost, the proposed scheme is able to generalize and plan a trajectory online in a dynamic environment. To design the motion of the system to track the planned trajectory in a stable and smooth manner, the concept of a sliding mode control was employed; its stability was proved theoretically. The proposed scheme was implemented on a robotic surgical system and the performance was verified through experiments and simulations. These experiments and simulations verified that the developed system successfully planned and updated the trajectories of the learned tasks in response to the changes in the dynamic environment.