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

The use of robots, as a social stimulus, provides several advantages over using another animal. In particular, for rat-robot studies, robots can produce social behaviour that is reproducible across trials. In the current work, we outline a framework for rat-robot interaction studies, that consists of a novel rat-sized robot (PiRat), models of robotic behavior, and a position tracking system for both robot and rat. We present the design of the framework, including constraints on autonomy, latency, and control. We pilot tested our framework by individually running the robot rat with eight different rats, first through a habituation stage, and then with PiRat performing two different types of behaviour - avoiding and frequently approaching. We evaluate the performance of the framework on latency and autonomy, and on the ability to influence the behaviour of individual rats. We find that the framework performs well on its constraints, engages some of the rats (according to the number of meetings), and features a control scheme that produces reproducible behaviour in rats. These features represent a first demonstration of a closed-loop rat-robot framework.

The use of magnetic resonance imaging (MRI) for guiding robotic surgical devices has shown great potential for performing precisely targeted and controlled interventions. To fully realize these benefits, devices must work safely within the tight confines of the MRI bore without negatively impacting image quality. Here we expand on previous work exploring MRI guided robots for neural interventions by presenting the mechanical design and assessment of a device for positioning, orienting, and inserting an interstitial ultrasound-based ablation probe. From our previous work we have added a 2 degree of freedom (DOF) needle driver for use with the aforementioned probe, revised the mechanical design to improve strength and function, and performed an evaluation of the mechanism's accuracy and effect on MR image quality. The result of this work is a 7-DOF MRI robot capable of positioning a needle tip and orienting it's axis with accuracy of 1.37 ± 0.06mm and 0.79° ± 0.41°, inserting it along it's axis with an accuracy of 0.06 ± 0.07mm, and rotating it about it's axis to an accuracy of 0.77° ± 1.31°. This was accomplished with no significant reduction in SNR caused by the robot's presence in the MRI bore, ≤ 10.3% reduction in SNR from running the robot's motors during a scan, and no visible paramagnetic artifacts.

This paper specifies a surgical robot performing semi-autonomous electrosurgery for tumor resection and evaluates its accuracy using a visual servoing paradigm. We describe the design and integration of a novel, multi-degree of freedom electrosurgical tool for the smart tissue autonomous robot (STAR). Standardized line tests are executed to determine ideal cut parameters in three different types of porcine tissue. STAR is then programmed with the ideal cut setting for porcine tissue and compared against expert surgeons using open and laparoscopic techniques in a line cutting task. We conclude with a proof of concept demonstration using STAR to semi-autonomously resect pseudo-tumors in porcine tissue using visual servoing. When tasked to excise tumors with a consistent 4mm margin, STAR can semi-autonomously dissect tissue with an average margin of 3.67 mm and a standard deviation of 0.89mm.

Diagnostic blood testing is the most prevalent medical procedure performed in the world and forms the cornerstone of modern health care delivery. Yet blood tests are still predominantly carried out in centralized labs using large-volume samples acquired by manual venipuncture, and no end-to-end solution from blood draw to sample analysis exists today. Our group is developing a platform device that merges robotic phlebotomy with automated diagnostics to rapidly deliver patient information at the site of the blood draw. The system couples an image-guided venipuncture robot, designed to address the challenges of routine venous access, with a centrifuge-based blood analyzer to obtain quantitative measurements of hematology. In this paper, we first present the system design and architecture of the integrated device. We then perform a series of in vitro experiments to evaluate the cannulation accuracy of the system on blood vessel phantoms. Next, we assess the effects of vessel diameter, needle gauge, flow rate, and viscosity on the rate of sample collection. Finally, we demonstrate proof-of-concept of a white cell assay on the blood analyzer using in vitro human samples spiked with fluorescently labeled microbeads.

Although existing mechanics-based models of concentric tube robots have been experimentally demonstrated to approximate the actual kinematics, determining accurate estimates of model parameters remains difficult due to the complex relationship between the parameters and available measurements. Further, because the mechanics-based models neglect some phenomena like friction, nonlinear elasticity, and cross section deformation, it is also not clear if model error is due to model simplification or to parameter estimation errors. The parameters of the superelastic materials used in these robots can be slowly time-varying, necessitating periodic re-estimation. This paper proposes a method for estimating the mechanics-based model parameters using an extended Kalman filter as a step toward on-line parameter estimation. Our methodology is validated through both simulation and experiments.

This paper presents the experimental validation of a novel control strategy that unifies the entire gait cycle of a powered knee-ankle prosthetic leg without the need to switch between controllers for different periods of gait. Current control methods divide the gait cycle into several sequential periods each with independent controllers, resulting in many patient-specific control parameters and switching rules that must be tuned for a specific walking speed. The single controller presented is speed-invariant with a minimal number of control parameters to be tuned. A single, periodic virtual constraint is derived that exactly characterizes the desired actuated joint motion as a function of a mechanical phase variable across walking cycles. A single sensor was used to compute a phase variable related to the residual thigh angle's phase plane, which was recently shown to robustly represent the phase of non-steady human gait. This phase variable allows the prosthesis to synchronize naturally with the human user for intuitive, biomimetic behavior. A custom powered knee-ankle prosthesis was designed and built to implement the control strategy and validate its performance. A human subject experiment was conducted across multiple walking speeds (1 to 3 miles/hour) in a continuous sequence with the single phase-based controller, demonstrating its adaptability to the user's intended speed.

Robotically Assisted Minimally Invasive Surgery (RAMIS) offers many advantages over manual surgical techniques. Most of the limitations of RAMIS stem from its non-intuitive user interface and costs. One way to mitigate some of the limitations is to automate surgical subtasks (e.g. suturing) such that they are performed faster while allowing the surgeon to plan the next step of the procedure. One component of successful suture automation is minimizing the internal tissue deformation forces generated by driving a needle through tissue. Minimizing the internal tissue forces requires segmenting the tissue deformation forces from other components of the needle tissue interaction (e.g. friction force). This paper proposes an Unscented Kalman Filter which can successfully model the force components, in particular the internal deformation force, generated by a needle as it is driven through a sample of tissue.

In this paper, a new task-oriented active-sensing method is presented. Most active sensing methods choose sensing actions that minimize the uncertainty of the state according to some information-theoretic measure. While this is reasonable for most applications, minimizing state uncertainty may not be most relevant when the state information is used to perform a task. This is because the uncertainty in some subspace of the state space could have more impact on the performance of the task than the others at a given time. The active-sensing method presented in this paper takes the task into account when selecting sensing actions by minimizing the uncertainty in future task action.

Concentric tube robots comprise telescopic precurved elastic tubes. The robot's tip and shape are controlled via relative tube motions, i.e. tube rotations and translations. Non-linear interactions between the tubes, e.g. friction and torsion, as well as uncertainty in the physical properties of the tubes themselves, e.g. the Young's modulus, curvature, or stiffness, hinder accurate kinematic modelling. In this paper, we present a machine-learning-based methodology for kinematic modelling of concentric tube robots and in situ model adaptation. Our approach is based on Locally Weighted Projection Regression (LWPR). The model comprises an ensemble of linear models, each of which locally approximates the original complex kinematic relation. LWPR can accommodate for model deviations by adjusting the respective local models at run-time, resulting in an adaptive kinematics framework. We evaluated our approach on data gathered from a three-tube robot, and report high accuracy across the robot's configuration space.

Surgeons and robots typically use different approaches for bone milling. Surgeons adjust their speed and tool incidence angle constantly, which enables them to efficiently mill porous bone. Surgeons also adjust milling parameters such as speed and depth of cut throughout the procedure based on proximity to sensitive structures like nerves and blood vessels. In this paper we use image-based bone density estimates and segmentations of vital anatomy to make a robot mill more like a surgeon and less like an industrial computer numeric controlled (CNC) milling machine. We produce patient-specific plans optimizing velocity and incidence angles for spherical cutting burrs. These plans are particularly useful in bones of variable density and porosity like the human temporal bone. They result in fast milling in non-critical areas, reducing overall procedure time, and lower forces near vital anatomy. We experimentally demonstrate the algorithm on temporal bone phantoms and show that it reduces mean forces near vital anatomy by 63% and peak forces by 50% in comparison to a CNC-type path, without adding time to the procedure.