Dynamic Motion Planning for Autonomous Assistive Surgical Robots

Abstract

The paper addresses the problem of the generation of collision-free trajectories for a robotic manipulator, operating in a scenario in which obstacles may be moving at non-negligible velocities. In particular, the paper aims to present a trajectory generation solution that is fully executable in real-time and that can reactively adapt to both dynamic changes of the environment and fast reconfiguration of the robotic task. The proposed motion planner extends the method based on a dynamical system to cope with the peculiar kinematics of surgical robots for laparoscopic operations, the mechanical constraint being enforced by the fixed point of insertion into the abdomen of the patient the most challenging aspect. The paper includes a validation of the trajectory generator in both simulated and experimental scenarios.Electronics 2019, 8, 957 2 of 24 requires addressing both technical and ethical/legal issues (see [6,7] for updated reviews). In particular, from the technological point of view, soft-tissue surgery in non-rigid anatomical environments forces taking into account hardly predictable scene changes, complicating the tasks of collision-free motion planning and physical environment interaction (i.e., contact with objects with unknown and even variable viscoelastic properties).The growth of investments in research and development projects for autonomous surgical robotics demonstrates the confidence and the expectations of the medical community regarding the benefits of such technologies. For example, the European Union has recently funded several projects related to the automation of surgical tasks, like I-SUR (Intelligent Surgical Robotics, FP7 Grant No. 270396, the automation of needle insertion and suturing tasks [8] by means of a dual-arm robot with hybrid parallel/serial kinematics. The cognitive control architecture proposed by I-SUR [9] was able to operate in either teleoperated [10] or autonomous mode [11], guaranteeing a stable switch between the two and an adaptive interaction with the environment in both modes [12]. The inherent relationship between surgical and industrial collaborative robotics is demonstrated by the fact that the same control methods (i.e., admittance control with variable dynamics) have also been applied by the same authors to enforce stability in pHRI [13,14]. Turning back to the specific case of the suturing task, the work presented in [11] proposed a motion planning solution based on a combination of previously-specified motion primitives for the dual-arm system, designed to mimic the bimanual gestures of a human surgeon, and collision-free paths generated with a plan-and-move strategy. Similar approaches to surgical robotic suturing were described in [15,16], investigating advanced learning techniques, or [17,18], addressing the task using more classical robot motion planning techniques and analytic geometry. Even though surgical suturing tasks have also been automated by designing specific devices, not mimicking at all human gestures [19], the solutions based on general-purpo...

A First Evaluation of a Multi-Modal Learning System to Control Surgical Assistant Robots via Action Segmentation

“…However, this does not guarantee that the resulting motion is feasible for all the goal positions with the presence of obstacles along the path and the remote center-of-motion constraint. Therefore, a geometrical solution has been developed to provide the MPC with a real-time sequence of waypoints towards the goal [26]. This solution exploits the fact that a laparoscopy tool always presents an obstacle-free motion along the instrument axis towards the insertion trocar.…”

Global/local motion planning based on Dynamic Trajectory Reconfiguration and Dynamical Systems for Autonomous Surgical Robots

“…In the proposed approach at each control step the trajectory is locally modified by a reactive local planner based on the algorithm outlined in [15]. This algorithm modulates the desired velocity of the robot to obtain a new velocity which drives the robot on a collision free trajectory.…”

“…Since it is possible to model the laparoscopic tools as capsules, i.e. cylinders with hemispheric terminations (as described in [15]), the point on the axis of the tool closest to the closest obstacle can be computed using the parameterization of the segments introduced in [16]. In case of moving obstacles we also take into account their motion considering the velocity of the robot relative to the obstacles ṗrel = ṗc − ṗobs (7) where ṗobs is computed as follows…”

Optimal Motion Planning in GPS-Denied Environments Using Nonlinear Model Predictive Horizon

“…Path planning is a computational problem to generate and follow a collision-free trajectory from one point to another [ 1 ]. It has many applications, such as robotic surgery [ 2 ], driverless cars [ 3 ], automation [ 4 ], and mining [ 5 ]. An extensive amount of research has been conducted in the field of path planning for autonomous vehicles [ 3 , 6 ].…”