Online Trajectory Planning in Dynamic Environments for Surgical Task Automation

Abstract

Abstract-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.

A learning-based shared control architecture for interactive task execution

“…The framework called ProMP models the distribution of the trajectories in the parameter space [10] while the method in [11] modeled the distribution of the demonstrated trajectories at each time step using Gaussian Processes. These studies showed that the demonstrated behaviors can be generalized to new situation by modeling the distribution of the demonstrated trajectories.…”

“…We assume that the demonstrations are available under various contexts s i . In this case, we can model the conditional distribution of the demonstrated trajectories given the context in order to generalize the demonstrated trajectories to new situations [11], [24], [25]. Here, we use Locally Weighted Regression (LWR) to model this distribution [26], [27].…”

“…In LfD methods, the distribution of the demonstrated trajectories is often modeled, and the learned distribution is leveraged for generalizing the demonstrated behaviors [10], [11]. Recent work on LfD showed that the variance of the demonstrated trajectories can be used to adaptively control the robot behaviors [12].…”

Learning by observation for surgical subtasks: Multilateral cutting of 3D viscoelastic and 2D Orthotropic Tissue Phantoms

“…Van den Berg et al [34] proposed an iterative technique to learn a reference trajectory and execute it at higher than demonstration speeds for suture knot tying. This work was recently extended by Osa et al [25] to deal with dynamic changes in the environment, but with an industrial manipulator. Mayer et al [20] use principles of fluid dynamics and Schulman et al [31] use non-rigid registration techniques to generalize human demonstrations to similar, yet previously unseen, initial conditions.…”

A learning based training and skill assessment platform with haptic guidance for endovascular catheterization

“…These studies include complete automation of time-consuming and repetitive tasks [5], as well as collaborative surgery in which the control is shared back and forth between the operator and the robot [6]. Recent studies have looked into generalizing learned demonstrations to previously unseen initial conditions [7], as well as an adaptive trajectory planning to deal with dynamic changes in the environment [8]. In the field of endovascular intervention these learning-based techniques have been used for automation of motion trajectories from expert demonstrations for the same catheterization task, by a robotic driver, demonstrating potential improvements over manual catheterization [9].…”