Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems最新文献

Concentric tube robots have the potential to enable new minimally invasive surgical procedures by curving around anatomical obstacles to reach difficult-to-reach sites in body cavities. Planning motions for these devices is challenging in part due to their complex kinematics; concentric tube robots are composed of thin, pre-curved, telescoping tubes that can achieve a variety of shapes via extension and rotation of each of their constituent tubes. We introduce a new motion planner to maneuver these devices to clinical targets while minimizing the probability of colliding with anatomical obstacles. Unlike prior planners for these devices, we more accurately model device shape using mechanics-based models that consider torsional interaction between the tubes. We also account for the effects of uncertainty in actuation and predicted device shape. We integrate these models with a sampling-based approach based on the Rapidly-Exploring Roadmap to guarantee finding optimal plans as computation time is allowed to increase. We demonstrate our motion planner in simulation using a variety of evaluation scenarios including an anatomy-based neurosurgery case that requires maneuvering to a difficult-to-reach brain tumor at the skull base.

Concentric tube robots are a subset of continuum robots constructed by combining pre-curved elastic tubes. As the tubes are rotated and translated with respect to each other, their curvatures interact elastically, enabling control of the robot's tip configuration as well as the curvature along its length. This technology is projected to be useful in many types of minimally invasive medical procedures. Because these robots are flexible by design, they deflect considerably when applying forces to the external environment. Thus, in contrast to rigid-link robots, their kinematic and static force models are coupled. This paper derives a multi-tube quasistatic model that relates tube rotations and translations together with externally applied loads to robot shape and tip configuration. The model can be applied in robot design, procedure planning as well as control. For validation, the multi-tube model is compared experimentally to a computationally-efficient single-tube approximate model.

Stiffness control of a continuum robot can prevent excessive contact forces during robot navigation inside delicate, uncertain and confined environments. Furthermore, it enables the selection of tip stiffnesses that match varying task requirements. This paper introduces a computationally-efficient approach to continuum-robot stiffness control that is based on writing the forward kinematic model as the product of two transformations. The first transformation calculates the non-contact kinematics of the robot and can be formulated based on the specific type of continuum robot under consideration. The second transformation calculates the tip deflection due to applied forces and is efficiently computed using the special Cosserat rod model. To implement a desired tip stiffness, the two transformations are used to solve for the actuator positions that deform the manipulator so as to generate the required tip force at the measured tip position. The efficacy of the proposed controller is demonstrated experimentally on a concentric-tube continuum robot.

This paper presents a human-robot interface with perceptual docking to allow for the control of multiple microbots. The aim is to demonstrate that real-time eye tracking can be used for empowering robots with human vision by using knowledge acquired in situ. Several micro-robots can be directly controlled through a combination of manual and eye control. The novel control environment is demonstrated on a virtual biopsy of gastric lesion through an endoluminal approach. Twenty-one subjects were recruited to test the control environment. Statistical analysis was conducted on the completion time of the task using the keyboard control and the proposed eye tracking framework. System integration with the concept of perceptual docking framework demonstrated statistically significant improvement of task execution.

Robotic surgical assistants (RSAs) have the potential to facilitate surgeries and reduce human fatigue. In this paper we focus on surgical retraction, the common surgical primitive of grasping and lifting a thin layer of tissue to expose an underlying area. Given a 2D cross-sectional model of heterogeneous tissue with embedded structures (such as veins) and a desired underlying exposure region, we present an algorithm that computes a set of stable and secure grasp-and-retract trajectories, and runs a 3D finite element (FEM) simulation to certify the quality of each trajectory. To choose secure candidate grasp locations, we introduce the continuous spring method and combine it with the Deformation Space (D-Space) approach to grasping deformable objects with a linearized potential energy model based on the locations of embedded bodies. Experiments show that this method produces many of the same grasps as an exhaustive computation with an FEM mesh, but is orders of magnitude cheaper: our method runs in O(υ log υ) time, when υ is the number of veins, while the FEM computation takes O(pn ³) time, where n is the number of nodes in the FEM mesh and p is the number of nodes on its perimeter. Furthermore, we present a constant tissue curvature (CTC) retraction trajectory that distributes strain uniformly around the medial axis of the tissue, by moving the gripper such that the tissue follows a constant-curvature, constant-length arc. 3D FEM simulations show that the CTC achieves retractions with lower tissue strain than circular and linear trajectories. Overall, our algorithm computes and certifies a high-quality retraction in about one minute on a PC.

Accurate insertion of needles to targets in 3D anatomy is required for numerous medical procedures. To reduce patient trauma, a "fireworks" needle insertion approach can be used in which multiple needles are inserted from a single small region on the patient's skin to multiple targets in the tissue. In this paper, we explore motion planning for "fireworks" needle insertion in 3D environments by developing an algorithm based on Rapidly-exploring Random Trees (RRTs). Given a set of targets, we propose an algorithm to quickly explore the configuration space by building a forest of RRTs and to find feasible plans for multiple steerable needles from a single entry region. We present two path selection algorithms with different optimality considerations to optimize the final plan among all feasible outputs. Finally, we demonstrate the performance of the proposed algorithm with a simulation based on a prostate cancer treatment environment.

Lie group symmetry in a mechanical system can lead to a dimensional reduction in its dynamical equations. Typically, the symmetries that one exploits are intrinsic to the mechanical system at hand, e.g. invariance of the system's Lagrangian to some group of motions. In the present work we consider symmetries that arise from an extrinsic control task, rather than the intrinsic structure of configuration space, constraints, or system dynamics. We illustrate this technique with several examples. In the examples, the reduction enables us to design essentially global feedback controllers on the reduced systems.We apply task-induced symmetry and reduction to a recently developed 6 DOF kinematic model of steerable bevel-tip needles. The resulting controllers cause the needle tip to track a subspace of its configuration space. We envision that the methodology presented in this paper will form the basis for a new planning and control framework for needle steering.