Dynamic Virtual Fixture Generation Based on Intra-Operative 3D Image Feedback in Robot-Assisted Minimally Invasive Thoracic Surgery

This paper proposes a method for generating dynamic virtual fixtures with real-time 3D image feedback to facilitate human-robot collaboration in medical robotics. Seamless shared control in a dynamic environment, like that of a surgical field, remains challenging despite extensive research on collaborative control and planning. To address this problem, our method dynamically creates virtual fixtures to guide the manipulation of a trocar-placing robot arm using the force field generated by point cloud data from an RGB-D camera. Additionally, the "view scope" concept selectively determines the region for computational points, thereby reducing computational load. In a phantom experiment for robot-assisted port incision in minimally invasive thoracic surgery, our method demonstrates substantially improved accuracy for port placement, reducing error and completion time by 50% (p=1.06×10-2) and 35% (p=3.23×10-2), respectively. These results suggest that our proposed approach is promising in improving surgical human-robot collaboration.

Gait Prediction and Variable Admittance Control for Lower Limb Exoskeleton With Measurement Delay and Extended-State-Observer

The measurement delay of the feedback control system is a universal problem in industrial engineering, which will degrade output performance, especially causing undesirable chatter responses. In this study, a deep-Gaussian-process (DGP)-based method for operator's gait prediction is proposed to estimate the real-time motion intention and to compensate for the measurement delay of the inertial measurement unit (IMU). On the basis of these gait prediction uncertainties quantified by the DGP method, a variable admittance controller is designed to reduce real-time human-exoskeleton interaction torque. The reference trajectory is generated by the admittance controller, which is smoothed by the two-order Bessel interpolation. Meanwhile, the admittance parameters are self-regulated based on the defined uncertainty index of gait prediction. The extend-state observer (ESO) with backstepping iteration is adopted to compensate unmeasured system state, model uncertainties, and unmodeled dynamics of lower limb exoskeleton. The effectiveness of the proposed gait prediction and control scheme is verified by both the comparative simulations and experimental results of the human-exoskeleton cooperative motion.

Portable Device to Assist With Force Control in Ultrasound Acquisition

This study presents a portable device that ensures precise contact force between a subject and a probe to improve the stability and reproducibility of ultrasound (US) acquisition. The mechanical portion of the device includes a servo motor, gears, and a ball screw linear actuator; two photoelectric switches are used to limit the stroke. A combined force and position control system is developed, and a pressure threshold is introduced to reduce the chattering of the system so that it can be applied to US examinations of tissues of different stiffness levels. Force control experiments were conducted on the device, and the results showed that the device can overcome the chattering of a physician's hand and movement caused by a subject's respiration. Additionally, the stability of the US acquisition was substantially improved. Based on clinical trials on humans, this device was observed to improve the consistency of ultrasonic results and the repeatability of images, and it assisted sonographers with maintaining suitable contact force and improving imaging quality. The device can either be handheld by a physician or easily integrated with a manipulator as an autonomous robotic US acquisition device, thereby validating its potential for US applications.

Variable Impedance Control Based on Target Position and Tracking Error for Rehabilitation Robots During a Reaching Task

To obtain an anthropomorphic performance in physical human-robot interaction during a reaching task, a variable impedance control (vIC) algorithm with human-like characteristics is proposed in this article. The damping value of the proposed method is varied with the target position as well as through the tracking error. The proposed control algorithm is compared with the impedance control algorithm with constant parameters (IC) and another vIC algorithm, which is only changed with the tracking error (vIC-e). The different control algorithms are validated through the simulation study, and are experimentally implemented on a cable-driven rehabilitation robot. The results show that the proposed vIC can improve the tracking accuracy and trajectory smoothness, and reduce the interaction force at the same time.

Adaptive Human Force Scaling via Admittance Control for Physical Human-Robot Interaction

The goal of this article is to design an admittance controller for a robot to adaptively change its contribution to a collaborative manipulation task executed with a human partner to improve the task performance. This has been achieved by adaptive scaling of human force based on her/his movement intention while paying attention to the requirements of different task phases. In our approach, movement intentions of human are estimated from measured human force and velocity of manipulated object, and converted to a quantitative value using a fuzzy logic scheme. This value is then utilized as a variable gain in an admittance controller to adaptively adjust the contribution of robot to the task without changing the admittance time constant. We demonstrate the benefits of the proposed approach by a pHRI experiment utilizing Fitts' reaching movement task. The results of the experiment show that there is a) an optimum admittance time constant maximizing the human force amplification and b) a desirable admittance gain profile which leads to a more effective co-manipulation in terms of overall task performance.

Improving Stability in Upper Limb Rehabilitation Using Variable Stiffness

In a robotic rehabilitation setup, patient's safety and interaction stability are critical throughout the therapy. This paper addresses the stability aspect by proposing a method to vary the endpoint stiffness using a variable impedance mechanism. The proposed device consists of permanent magnets in an antagonistic configuration that acts as springs and the variation in stiffness is achieved by modifying the separation between those magnets. This device is mounted on the end-effector of an admittance controlled robotic arm and tested with the help of healthy humans on a virtual maze traversal experiment consisting of both fine and gross motor regions. Moreover, the subjects are tested both in normal and simulated tremor conditions to verify the effectiveness of the device. The experimental results show that the VSM can not only suppress the high-frequency forces but can also reduce the interference of human endpoint stiffness in the stability of the robot.