Design and Control of a Highly Redundant Rigid-Flexible Coupling Robot to Assist the COVID-19 Oropharyngeal-Swab Sampling

• URL: http://arxiv.org/abs/2102.12726v1
• Date: Thu, 25 Feb 2021 08:08:48 GMT
• Title: Design and Control of a Highly Redundant Rigid-Flexible Coupling Robot to Assist the COVID-19 Oropharyngeal-Swab Sampling
• Abstract summary: The robot is composed of a visual system, UR5 robot arm, micro-pneumatic actuator and force-sensing system. The developed robot system is validated by OP-swab sampling on both oral cavity phantoms and volunteers.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The outbreak of novel coronavirus pneumonia (COVID-19) has caused mortality and morbidity worldwide. Oropharyngeal-swab (OP-swab) sampling is widely used for the diagnosis of COVID-19 in the world. To avoid the clinical staff from being affected by the virus, we developed a 9-degree-of-freedom (DOF) rigid-flexible coupling (RFC) robot to assist the COVID-19 OP-swab sampling. This robot is composed of a visual system, UR5 robot arm, micro-pneumatic actuator and force-sensing system. The robot is expected to reduce risk and free up the clinical staff from the long-term repetitive sampling work. Compared with a rigid sampling robot, the developed force-sensing RFC robot can facilitate OP-swab sampling procedures in a safer and softer way. In addition, a varying-parameter zeroing neural network-based optimization method is also proposed for motion planning of the 9-DOF redundant manipulator. The developed robot system is validated by OP-swab sampling on both oral cavity phantoms and volunteers.

Related papers

• RC-NF: Robot-Conditioned Normalizing Flow for Real-Time Anomaly Detection in Robotic Manipulation [68.7948300643741]
  We propose Robot-Conditioned Normalizing Flow (RC-NF), a real-time monitoring model for robotic anomaly detection and intervention.<n>RC-NF decouples the processing of task-aware robot and object states within the normalizing flow.<n>It achieves state-of-the-art performance across all anomaly types compared to previous methods in monitoring robotic tasks.
  arXiv  Detail & Related papers  (2026-03-11T10:14:37Z)
• Give me scissors: Collision-Free Dual-Arm Surgical Assistive Robot for Instrument Delivery [4.187693745535412]
  During surgery, scrub nurses are required to frequently deliver surgical instruments to surgeons.<n>Existing research on robotic scrub nurses relies on predefined paths for instrument delivery.<n>We present a collision-free dual-arm surgical assistive robot capable of performing instrument delivery.
  arXiv  Detail & Related papers  (2026-03-03T03:14:59Z)
• Fluoroscopy-Constrained Magnetic Robot Control via Zernike-Based Field Modeling and Nonlinear MPC [4.093982213892736]
  Magnetic actuation enables surgical robots to navigate complex anatomical pathways while reducing tissue trauma and improving surgical precision.<n>This paper presents a control framework that remains accurate and stable under such conditions by combining a nonlinear model predictive control framework.<n> Experimental validation is conducted with two magnetic robots in a 3D-printed fluid workspace and a spine phantom replicating drug delivery in the epidural space.
  arXiv  Detail & Related papers  (2026-02-17T04:50:18Z)
• Autonomous Multi-Robot Infrastructure for AI-Enabled Healthcare Delivery and Diagnostics [0.0]
  This research presents a multi-robot system for inpatient care incorporating wearable health sensors, RF-based communication, and AI-driven decision support.<n>Within a simulated hospital environment, the system adopts a leader-follower swarm configuration to perform patient monitoring, medicine delivery, and emergency assistance.
  arXiv  Detail & Related papers  (2025-09-30T11:27:33Z)
• Organ-Agents: Virtual Human Physiology Simulator via LLMs [66.40796430669158]
  Organ-Agents is a multi-agent framework that simulates human physiology via LLM-driven agents.<n>We curated data from 7,134 sepsis patients and 7,895 controls, generating high-resolution trajectories across 9 systems and 125 variables.<n>Organ-Agents achieved high simulation accuracy on 4,509 held-out patients, with per-system MSEs 0.16 and robustness across SOFA-based severity strata.
  arXiv  Detail & Related papers  (2025-08-20T01:58:45Z)
• BRAVE: Brain-Controlled Prosthetic Arm with Voice Integration and Embodied Learning for Enhanced Mobility [5.528262076322921]
  BRAVE is a hybrid EEG and voice-controlled prosthetic system.<n>It aims to interpret EEG-driven motor intent, enabling movement control without reliance on residual muscle activity.<n>The system operates in real time, with a response latency of 150 ms.
  arXiv  Detail & Related papers  (2025-05-23T11:44:33Z)
• Hysteresis-Aware Neural Network Modeling and Whole-Body Reinforcement Learning Control of Soft Robots [14.02771001060961]
  We present a soft robotic system designed for surgical applications. We propose a whole-body neural network model that accurately captures and predicts the soft robot's whole-body motion. The proposed method showed strong performance in phantom-based surgical experiments.
  arXiv  Detail & Related papers  (2025-04-18T09:34:56Z)
• Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing [88.99939660183881]
  We propose a framework for autonomous robotic navigation for subretinal injection. Our method consists of an instrument pose estimation method, an online registration between the robotic and the i OCT system, and trajectory planning tailored for navigation to an injection target. Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method.
  arXiv  Detail & Related papers  (2023-01-17T21:41:21Z)
• Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems [79.07468367923619]
  We propose a backpropagation-free approach to robotic control through the neuro-cognitive computational framework of neural generative coding (NGC) We design an agent built completely from powerful predictive coding/processing circuits that facilitate dynamic, online learning from sparse rewards. We show that our proposed ActPC agent performs well in the face of sparse (extrinsic) reward signals and is competitive with or outperforms several powerful backprop-based RL approaches.
  arXiv  Detail & Related papers  (2022-09-19T16:49:32Z)
• A Novel Sample-efficient Deep Reinforcement Learning with Episodic Policy Transfer for PID-Based Control in Cardiac Catheterization Robots [2.3939470784308914]
  The model was validated for axial motion control of a robotic system designed for intravascular catheterization. Performance comparison with conventional methods in average of 10 trials shows the agent tunes the gain better with error of 0.003 mm.
  arXiv  Detail & Related papers  (2021-10-28T08:18:01Z)
• Tele-Operated Oropharyngeal Swab (TOOS) RobotEnabled by TSS Soft Hand for Safe and EffectiveCOVID-19 OP Sampling [39.56608582360945]
  To diagnose COVID-19, oropharyngeal swab (OP SWAB) sampling is generally applied for viral nucleic acid (VNA) specimen collection. Traditional robot suffers from safety, cost, and control complexity issues for wide-scale deployment. We present soft robotic technology is promising to achieve robotic OP swab sampling with excellent swab manipulability in a confined oral space.
  arXiv  Detail & Related papers  (2021-09-20T10:02:58Z)
• Online Body Schema Adaptation through Cost-Sensitive Active Learning [63.84207660737483]
  The work was implemented in a simulation environment, using the 7DoF arm of the iCub robot simulator. A cost-sensitive active learning approach is used to select optimal joint configurations. The results show cost-sensitive active learning has similar accuracy to the standard active learning approach, while reducing in about half the executed movement.
  arXiv  Detail & Related papers  (2021-01-26T16:01:02Z)
• Detecting COVID-19 from Breathing and Coughing Sounds using Deep Neural Networks [68.8204255655161]
  We adapt an ensemble of Convolutional Neural Networks to classify if a speaker is infected with COVID-19 or not. Ultimately, it achieves an Unweighted Average Recall (UAR) of 74.9%, or an Area Under ROC Curve (AUC) of 80.7% by ensembling neural networks.
  arXiv  Detail & Related papers  (2020-12-29T01:14:17Z)
• Semi-supervised Neural Networks solve an inverse problem for modeling Covid-19 spread [61.9008166652035]
  We study the spread of COVID-19 using a semi-supervised neural network. We assume a passive part of the population remains isolated from the virus dynamics.
  arXiv  Detail & Related papers  (2020-10-10T19:33:53Z)
• Using Conditional Generative Adversarial Networks to Reduce the Effects of Latency in Robotic Telesurgery [0.0]
  In surgery, any micro-delay can injure a patient severely and in some cases, result in fatality. Current surgical robots use calibrated sensors to measure the position of the arms and tools. In this work we present a purely optical approach that provides a measurement of the tool position in relation to the patient's tissues.
  arXiv  Detail & Related papers  (2020-10-07T13:40:44Z)
• A Real-time Robot-based Auxiliary System for Risk Evaluation of COVID-19 Infection [31.76845310194196]
  We propose a real-time robot-based auxiliary system for risk evaluation of COVID-19 infection. It combines real-time speech recognition, temperature measurement, keyword detection, cough detection and other functions.
  arXiv  Detail & Related papers  (2020-08-18T01:58:52Z)
• Populations of Spiking Neurons for Reservoir Computing: Closed Loop Control of a Compliant Quadruped [64.64924554743982]
  We present a framework for implementing central pattern generators with spiking neural networks to obtain closed loop robot control. We demonstrate the learning of predefined gait patterns, speed control and gait transition on a simulated model of a compliant quadrupedal robot.
  arXiv  Detail & Related papers  (2020-04-09T14:32:49Z)
• Efficiently Calibrating Cable-Driven Surgical Robots with RGBD Fiducial Sensing and Recurrent Neural Networks [26.250886014613762]
  We propose a novel approach to efficiently calibrate such robots by placing a 3D printed fiducial coordinate frames on the arm and end-effector that is tracked using RGBD sensing. With the proposed method, data collection of 1800 samples takes 31 minutes and model training takes under 1 minute. Results on a test set of reference trajectories suggest that the trained model can reduce the mean tracking error of the physical robot from 2.96 mm to 0.65 mm.
  arXiv  Detail & Related papers  (2020-03-19T00:24:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.