Soft Robotics:Morphology and Morphology-inspired Motion Strategy

Robotics has aroused huge attention since the 1950s.Irrespective of the uniqueness that industrial applications exhibit,conventional rigid robots have displayed noticeable limitations,particularly in safe cooperation as well as with environmental adaption.Accordingly,scientists have shifted their focus on soft robotics to apply this type of robots more effectively in unstructured environments.For decades,they have been committed to exploring sub-fields of soft robotics(e.g.,cutting-edge techniques in design and fabrication,accurate modeling,as well as advanced control algorithms).Although scientists have made many different efforts,they share the common goal of enhancing applicability.The presented paper aims to brief the progress of soft robotic research for readers interested in this field,and clarify how an appropriate control algorithm can be produced for soft robots with specific morphologies.This paper,instead of enumerating existing modeling or control methods of a certain soft robot prototype,interprets for the relationship between morphology and morphology-dependent motion strategy,attempts to delve into the common issues in a particular class of soft robots,and elucidates a generic solution to enhance their performance.

Dissolvable temporary barrier:a novel paradigm for flexible hydrogel patterning in organ-on-a-chip models

A combination of hydrogels and microfluidics allows the construction of biomimetic three-dimensional(3D)tissue models in vitro,which are also known as organ-on-a-chipmodels.The hydrogel patterningwith awell-controlled spatial distribution is typically achieved by embedding sophisticated microstructures to act as a boundary.However,these physical barriers inevitably expose cells/tissues to a less physiologically relevant microenvironment than in vivo conditions.Herein,we present a novel dissolvable temporary barrier(DTB)strategy that allows robust and flexible hydrogel patterning with great freedom of design and desirable flow stimuli for cellular hydrogels.The key aspect of this approach is the patterning of a water-soluble rigid barrier as a guiding path for the hydrogel using stencil printing technology,followed by a barrier-free medium perfusion after the dissolution of the DTB.Single and multiple tissue compartments with different geometries can be established using either straight or curved DTB structures.The effectiveness of this strategy is further validated by generating a 3D vascular network through vasculogenesis and angiogenesis using a vascularized microtumor model.As a new proof-of-concept in vasculature-on-a-chip,DTB enables seamless contact between the hydrogel and the culture medium in closed microdevices,which is an improved protocol for the fabrication ofmultiorgan chips.Therefore,we expect it to serve as a promising paradigm for organ-on-a-chip devices for the development of tumor vascularization and drug evaluation in the future preclinical studies.

A Novel Cable-Driven Soft Robot for Surgery

Robot-assisted laparoscopic radical prostatectomy(RARP)is widely used to treat prostate cancer.The rigid instruments primarily used in RARP cannot overcome the problem of blind areas in surgery and lead to more trauma such as more incision for the passage of the instrument and additional tissue damage caused by rigid instruments.Soft robots are relatively fexible and theoretically have infinite degrees of freedom which can overcome the problem of the rigid instrument.A soft robot system for single-port transvesical robot-assisted radical prostatectomy(STvRARP)is developed in this study.The soft manipulator with 10 mm in diameter and a maximum bending angle of 270°has good fexibility and dexterity.The design and mechanical structure of the soft robot are described.The kinematics of the soft manipulator is established and the inverse kinematics is compensated based on the characteristics of the designed soft manipulator.The master-slave control system of soft robot for surgery is built and the feasibility of the designed soft robot is verified.

Domain-Invariant Similarity Activation Map Contrastive Learning for Retrieval-Based Long-Term Visual Localization

Visual localization is a crucial component in the application of mobile robot and autonomous driving.Image retrieval is an efficient and effective technique in image-based localization methods.Due to the drastic variability of environmental conditions,e.g.,illumination changes,retrievalbased visual localization is severely affected and becomes a challenging problem.In this work,a general architecture is first formulated probabilistically to extract domain-invariant features through multi-domain image translation.Then,a novel gradientweighted similarity activation mapping loss(Grad-SAM)is incorporated for finer localization with high accuracy.We also propose a new adaptive triplet loss to boost the contrastive learning of the embedding in a self-supervised manner.The final coarse-to-fine image retrieval pipeline is implemented as the sequential combination of models with and without Grad-SAM loss.Extensive experiments have been conducted to validate the effectiveness of the proposed approach on the CMU-Seasons dataset.The strong generalization ability of our approach is verified with the RobotCar dataset using models pre-trained on urban parts of the CMU-Seasons dataset.Our performance is on par with or even outperforms the state-of-the-art image-based localization baselines in medium or high precision,especially under challenging environments with illumination variance,vegetation,and night-time images.Moreover,real-site experiments have been conducted to validate the efficiency and effectiveness of the coarse-to-fine strategy for localization.

Development of Rehabilitation and Assistive Robots in China: Dilemmas and Solutions

China is rapidly becoming an aging society, leading to a significant demand for chronic disease management and personalized healthcare. The development of rehabilitation and assistive robotics in China has gatheredsignificant attention not only in research fields but also in industries. Such robots aim to either guide patientsin completing therapeutic training or assist people with impaired functions in performing their daily activities.In the past decades, we have witnessed the advancement in rehabilitation and assistive robotics, with diversemechanical designs, functionalities, and purposes. However, the construction of dedicated regulations and policiesis relatively lagged compared with the flourishing development in research fields. Moreover, these kinds of robotsare working or collaborating closely with human beings, bringing unprecedented considerations on ethical issues.This paper aims to provide an overview of major dilemmas in the development of rehabilitation and assistiverobotics in China and propose several potential solutions.

Hysteresis Modeling and Compensation for Distal Shaft Deflection of Flexible Ureteroscope

Flexible ureteroscopy(FURS)has been widely used in the diagnosis and treatment of upper urinarytract diseases.The key operation of FURS is that the surgeon manipulates the distal shaft of flexible ureteroscopeto a specific target for diagnosis and treatment.However,the hysteresis of flexible ureteroscope may be one ofthe most important factors that degrade the manipulation accuracy and the surgeon usually spends a long timenavigating the distal shaft during surgery.In this study,we obtained hysteresis curves of distal shaft deflectionfor the flexible ureteroscope through extensive repeated experiments.Then,two methods based on piecewiselinear approximation and long short-term memory neural network were employed to model the hysteresis curves.On this basis,we proposed two hysteresis compensation strategies for the distal shaft deflection.Finally,wecarried out hysteresis compensation experiments to verify the two proposed compensation strategies.Experimentalresults showed that the hysteresis compensation strategies can significantly improve position accuracy with meancompensation errors of no more than 5°.

Micro/nanosystems for controllable drug delivery to the brain

Drug delivery to the brain is crucial in the treatment for central nervous system disorders.While significant progress has been made in recent years,there are still major challenges in achieving controllable drug delivery to the brain.Unmet clinical needs arise from various factors,including controlled drug transport,handling large drug doses,methods for crossing biological barriers,the use of imaging guidance,and effective models for analyzing drug delivery.Recent advances in micro/nanosystems have shown promise in addressing some of these challenges.These include the utilization of microfluidic platforms to test and validate the drug delivery process in a controlled and biomimetic setting,the development of novel micro/nanocarriers for large drug loads across the blood-brain barrier,and the implementation of micro-intervention systems for delivering drugs through intraparenchymal or peripheral routes.In this article,we present a review of the latest developments in micro/nanosystems for controllable drug delivery to the brain.We also delve into the relevant diseases,biological barriers,and conventional methods.In addition,we discuss future prospects and the development of emerging robotic micro/nanosystems equipped with directed transportation,real-time image guidance,and closed-loop control.

Engineering vascularised organoid-on-a-chip:strategies,advances and future perspectives

In recent years,advances in microfabrication technology and tissue engineering have propelled the development of a novel drug screening and disease modelling platform known as organoid-on-a-chip.This platform integrates organoids and organ-on-a-chip technologies,emerging as a promising approach for in vitro modelling of human organ physiology.Organoid-on-a-chip devices leverage microfluidic systems to simulate the physiological microenvironment of specific organs,offering a more dynamic and flexible setting that can mimic a more comprehensive human biological context.However,the lack of functional vasculature has remained a significant challenge in this technology.Vascularisation is crucial for the long-term culture and in vitro modelling of organoids,holding important implications for drug development and personalised medical approaches.This review provides an overview of research progress in developing vascularised organoid-on-a-chip models,addressing methods for in vitro vascularisation and advancements in vascularised organoids.The aim is to serve as a reference for future endeavors in constructing fully functional vascularised organoid-on-a-chip platforms.

Application of artificial intelligence in surgery

Artificial intelligence(AI)is gradually changing the practice of surgery with technological advancements in imaging,navigation,and robotic intervention.In this article,we review the recent successful and influential applications of AI in surgery from preoperative planning and intraoperative guidance to its integration into surgical robots.We conclude this review by summarizing the current state,emerging trends,and major challenges in the future development of AI in surgery.

Design of Multi-Coil Wireless Power Transfer System for Gastrointestinal Capsule Robot

The existing wireless power transfer(WPT)systems for gastrointestinal capsule robot have the prob-lems of small coupling coefficient and low power transmission efficiency(PTE).The reasons are due to the long distance between the transmitting coil and the receiving coil and the large difference in size.A new type of WPT system is designed,which uses three sets of small coil pairs to form a power supply unit(PSU),and utilizes multiple PSUs to form a multi-coil WPT system.Compared with single-coil system,the multi-coil system can achieve higher power utilization by switching between PSUs,instead of opening all PSUs.ANSYS Maxwell is used to perform finite element modeling on the PSU,analyzing the characteristics of the transmitting magnetic field.The results of the experiment show that when the distance between the small coil pairs in the PSU is 180mm,the magnetic field has relatively good uniformity,and the magnetic strength change relative to the center point is less than 5%.The average received power of the system is greater than 800mW,and the PTE is up to 5.1%.

Finite Element Modeling of Human Thorax Based on MRI Images for EIT Image Reconstruction

Electrical impedance tomography(EIT)image reconstruction is a non-linear problem.In general,finite element model is the critical basis of EIT image reconstruction.A 3D human thorax modeling method for EIT image reconstruction is proposed herein to improve the accuracy and reduce the complexity of existing finite element modeling methods.The contours of human thorax and lungs are extracted from the layers of magnetic resonance imaging(MRI)images by an optimized Otsu’s method for the construction of the 3D human thorax model including the lung models.Furthermore,the GMSH tool is used for finite element subdivision to generate the 3D finite element model of human thorax.The proposed modeling method is fast and accurate,and it is universal for different types of MRI images.The effectiveness of the proposed method is validated by extensive numerical simulation in MATLAB.The results show that the individually oriented 3D finite element model can improve the reconstruction quality of the EIT images more effectively than the cylindrical model,the 2.5D model and other human chest models.

Real-Time Deformation Simulation of Kidney Surgery Based on Virtual Reality

Virtual reality-based surgery simulation is becoming popular with the development of minimally invasive abdominal surgery,where deformable soft tissue is modelled and simulated.The mass-spring model(MSM)and finite element method(FEM)are common methods used in the simulation of soft tissue deformation.However,MSM has an issue concerning accuracy,while FEM has a problem with efficiency.To achieve higher accuracy and efficiency at the same time,we applied a co-rotational FEM in the simulation of a kidney with a tumour inside,achieving a real-time and accurate deformation simulation.In addition,we set a multi-model representation for mechanical simulation and visual rendering.The implicit Euler method and conjugate gradient method were adopted for setting and solving the linear system.For a realistic simulation of surgery,constraints outside the kidney and between the kidney and tumour were set with two series of mechanical properties for the two models.Experiments were conducted to validate the accuracy and real-time performance.

Automatic Segmentation Method for Cone-Beam Computed Tomography Image of the Bone Graft Region within Maxillary Sinus Based on the Atrous Spatial Pyramid Convolution Network

Sinus floor elevation with a lateral window approach requires bone graft(BG)to ensure sufficient bone mass,and it is necessary to measure and analyse the BG region for follow-up of postoperative patients.However,the BG region from cone-beam computed tomography(CBCT)images is connected to the margin of the maxillary sinus,and its boundary is blurred.Common segmentation methods are usually performed manually by experienced doctors,and are complicated by challenges such as low efficiency and low precision.In this study,an auto-segmentation approach was applied to the BG region within the maxillary sinus based on an atrous spatial pyramid convolution(ASPC)network.The ASPC module was adopted using residual connections to compose multiple atrous convolutions,which could extract more features on multiple scales.Subsequently,a segmentation network of the BG region with multiple ASPC modules was established,which effectively improved the segmentation performance.Although the training data were insufficient,our networks still achieved good auto-segmentation results,with a dice coefficient(Dice)of 87.13%,an Intersection over Union(Iou)of 78.01%,and a sensitivity of 95.02%.Compared with other methods,our method achieved a better segmentation effect,and effectively reduced the misjudgement of segmentation.Our method can thus be used to implement automatic segmentation of the BG region and improve doctors’work efficiency,which is of great importance for developing preliminary studies on the measurement of postoperative BG within the maxillary sinus.

Biomechanical Analysis of a Radial Expansion Mechanism of Intestinal Robot Coupling with Hyperelastic Intestinal Wall

This paper proposes a new type of radial expansion mechanism by adopting the scissor type telescopic design for intestinal robot to meet the requirements of the intestinal robot’s movement and residence in the intestinal tract.The robot’s maximum expansion radius is up to 25mm,which can well adapt to the intestinal tract with different diameters.At first,the mathematical model of the scissors-type telescopic mechanism(STM)is established to further study its dynamics characteristics by theoretical analysis and simulation.Then,in order to study the coupling effect between the STM and intestinal wall,the strain-energy function of Fung-type is used to establish the constitutive model of intestinal wall.Moreover,aimed at solving the non-convergence problem caused by the selection of material parameters in general Fung-type model,the restrictions for selecting material parameters were given by using positive definite matrix theory.Furthermore,the motion coupling characteristics between the mechanism and intestinal wall were analyzed by using the finite element method.The result shows that if the expansion radius of the STM exceeds a certain value,the intestinal wall may reach its deformation limit,which means that the maximum rotating angle of the three-claw butterfly disc of STM can be decided based on the maximum deformation stress of the intestinal wall.Therefore,it provides a design basis for formulating a reasonable expansion radius in mechanism control to avoid damage to the intestinal wall.

Development of a Robotic Cochlear Implantation System

Traditional cochlear implantation surgery has problems such as high surgical accuracy requirement and large trauma,which cause the difficulty of the operation and the high requirements for doctors,so that only a few doctors can complete the operation independently.However,there is no research on robotic cochlear implantation in China.In response to this problem,a robotic cochlear implantation system is proposed.The robot is controlled by robot operating system(ROS).A simulation environment for the overall surgery is established on the ROS based on the real surgery environment.Through the analysis of the kinematics and the motion planning algorithm of the manipulator,an appropriate motion mode is designed to control the motion of the manipulator,and perform the surgery under the simulation environment.A simple and feasible method of navigation is proposed,and through the model experiment,the feasibility of robotic cochlear implantation surgery is verified.

Survey of EIT Image Reconstruction Algorithms

With the recent promotion of clinical applications of electrical impedance tomography(EIT)technology,more scholars have begun studying EIT technology.Although the principle of EIT technology seems simple,EIT image reconstruction is a non-linear and ill-posed problem that is quite difficult to solve because of its soft field characteristics and the inhomogeneous distribution of its sensitive field.What’s more,the EIT reconstruction algorithm requires further improvements in robustness,clarity,etc.The image-reconstruction algorithm and image quality are among the key challenges in the application of EIT technology;thus,more research is urgently needed to improve the performance of EIT technology and use it to solve a larger variety of clinical problems.In this paper,we pay special attention to the latest advances in the study of EIT image-reconstruction algorithms to provide a convenient reference for EIT beginners and researchers who are newly involved in research on EIT image reconstruction.

On Flexible Trajectory Description for Effective Rigid Body Motion Reproduction and Recognition

Recognizing and reproducing spatiotemporal motions are necessary when analyzing behaviors andmovements during human-robot interaction. Rigid body motion trajectories are proven as compact and informativeclues in characterizing motions. A flexible dual square-root function (DSRF) descriptor for representing rigid bodymotion trajectories, which can offer robustness in the description over raw data, was proposed in our previousstudy. However, this study focuses on exploring the application of the DSRF descriptor for effective backwardmotion reproduction and motion recognition. Specifically, two DSRF-based reproduction methods are initiallyproposed, including the recursive reconstruction and online optimization. New trajectories with novel situationsand contextual information can be reproduced from a single demonstration while preserving the similarities withthe original demonstration. Furthermore, motion recognition based on DSRF descriptor can be achieved byemploying a template matching method. Finally, the experimental results demonstrate the effectiveness of theproposed method for rigid body motion reproduction and recognition.

Progress in Force-Sensing Techniques for Surgical Robots

Force sensing is vital for situational awareness and safe interaction during minimally invasive surgery.Consequently,surgical robots with integrated force-sensing techniques ensure precise and safe operations.Over the past few decades,there has been considerable progress in force-sensing techniques for surgical robots.This review summarizes the existing electrically-and optically-based force sensors for surgical robots,including piezoresistive,piezoelectric,capacitive,intensity/phase-modulated,andfiber Bragg gratings.Their principles,applications,advantages,and limitations are also discussed.Finally,we summarize our conclusions regarding state-of-the-art force-sensing technologies for surgical robotics.

Lidar-Visual-Inertial Odometry with Online Extrinsic Calibration

To achieve precise localization,autonomous vehicles usually rely on a multi-sensor perception system surrounding the mobile platform.Calibration is a time-consuming process,and mechanical distortion will cause extrinsic calibration errors.Therefore,we propose a lidar-visual-inertial odometry,which is combined with an adapted sliding window mechanism and allows for online nonlinear optimization and extrinsic calibration.In the adapted sliding window mechanism,spatial-temporal alignment is performed to manage measurements arriving at different frequencies.In nonlinear optimization with online calibration,visual features,cloud features,and inertial measurement unit(IMU)measurements are used to estimate the ego-motion and perform extrinsic calibration.Extensive experiments were carried out on both public datasets and real-world scenarios.Results indicate that the proposed system outperforms state-of-the-art open-source methods when facing challenging sensor-degenerating conditions.

Improving Colonoscopy Polyp Detection Rate Using Semi-Supervised Learning

Colorectal cancer is one of the biggest health threats to humans and takes thousands of lives every year.Colonoscopy is the gold standard in clinical practice to inspect the intestinal wall,detect polyps and remove polypsin early stages,preventing polyps from becoming malignant and forming colorectal cancer instances.In recentyears,computer-aided polyp detection systems have been widely used in colonoscopies to improve the qualityof colonoscopy examination and increase the polyp detection rate.Currently,the most efficient computer-aidedsystems are built with machine learning methods.However,developing such a computer-aided detection systemrequires experienced doctors to label a large number of image data from colonoscopy videos,which is extremelytime-consuming,laborious and expensive.One possible solution is to adopt a semi-supervised learning,which canbuild a detection system on a dataset where part of its data is not necessary to be labeled.In this paper,on thebasis of state-of-the-art object detection method and semi-supervised learning technique,we design and implementa semi-supervised colonoscopy polyp detection system containing four main steps:running standard supervisedtraining with all labeled data;running inference on unlabeled data to obtain pseudo labels;applying a set ofstrong augmentation to both unlabeled data and pseudo label;combining labeled data,and unlabeled data withits pseudo labels to retrain the detector.The semi-supervised learning system is evaluated both on public datasetand our original private dataset and proves its effectiveness.Also,the inference speed of the semi-supervisedlearning system can meet the requirement of real-time operation.