A Survey on Design, Actuation, Modeling, and Control of Continuum Robot

Optimal control analysis for the Nipah infection with constant and time-varying vaccination and treatment under real data application

In the last two decades, Nipah virus (NiV) has emerged as a significant paramyxovirus transmitted by bats, causing severe respiratory illness and encephalitis in humans. NiV has been included in the World Health Organization's Blueprint list of priority pathogens due its potential for human-to-human transmission and zoonotic characteristics. In this paper, a mathematical model is formulated to analyze the dynamics and optimal control of NiV. In formulation of the model we consider two modes of transmission: human-to-human and food-borne. Further, the impact of contact with an infected corpse as a potential route for virus transmission is also consider in the model. The analysis identifies the model with constant controls has three equilibrium states: the NiV-free equilibrium, the infected flying foxes-free equilibrium, and the NiV-endemic equilibrium state. Furthermore, a theoretical analysis is conducted to presents the stability of the model equilibria. The model fitting to the reported cases in Bangladesh from 2001 to 2015, and the estimation of parameters are performed using the standard least squares technique. Sensitivity analysis of the model-embedded parameters is provided to set the optimal time-dependent controls for the disease eradication. The necessary optimality conditions are derived using Pontryagin's maximum principle. Finally, numerical simulation is performed to determine the most effective strategy for disease eradication and to confirm the theoretical results.

A Comparative Study on Imputation Techniques: Introducing a Transformer Model for Robust and Efficient Handling of Missing EEG Amplitude Data

In clinical datasets, missing data often occur due to various reasons including non-response, data corruption, and errors in data collection or processing. Such missing values can lead to biased statistical analyses, reduced statistical power, and potentially misleading findings, making effective imputation critical. Traditional imputation methods, such as Zero Imputation, Mean Imputation, and k-Nearest Neighbors (KNN) Imputation, attempt to address these gaps. However, these methods often fall short of accurately capturing the underlying data complexity, leading to oversimplified assumptions and errors in prediction. This study introduces a novel Imputation model employing transformer-based architectures to address these challenges. Notably, the model distinguishes between complete EEG signal amplitude data and incomplete data in two datasets: PhysioNet and CHB-MIT. By training exclusively on complete amplitude data, the TabTransformer accurately learns and predicts missing values, capturing intricate patterns and relationships inherent in EEG amplitude data. Evaluation using various error metrics and R2 score demonstrates significant enhancements over traditional methods such as Zero, Mean, and KNN imputation. The Proposed Model achieves impressive R2 scores of 0.993 for PhysioNet and 0.97 for CHB-MIT, highlighting its efficacy in handling complex clinical data patterns and improving dataset integrity. This underscores the transformative potential of transformer models in advancing the utility and reliability of clinical datasets.

An optimal method for diagnosing heart disease using combination of grasshopper evalutionary algorithm and support vector machines

Due to the importance of accurate diagnosis and prompt treatment of this condition, the medical world is searching for a solution for its early detection and efficient treatment. Heart disease is one of the leading causes of death in modern society. With the development of computer science today, this issue can be resolved using computers. Data mining is one of the solutions for diagnosing this illness. One of the cutting-edge disciplines, data mining, can aid in better decision-making in many areas of medicine, including disease diagnosis and treatment. In order to improve diagnosis accuracy, a combination method using the evolutionary algorithms locust and support vector machine has been tested in this study. Use should be made of heart disease. Because of the hybrid nature of this approach, normalization is actually carried out in three steps: first, by using pre-processing operations to remove unknown and outlier data from the data set; second, by using the locust evolutionary algorithm to choose the best features from the available features; and third, by classifying the data set using a support vector machine. The accuracy criterion for the proposed method compared to Niobizin methods, neural networks, and J48 trees improved by 18 %, 30 %, and 24 %, respectively, after implementing it on the data set and comparing it with other algorithms used in the field of heart disease diagnosis.

Hybrid-Driven Origami Gripper with Variable Stiffness and Finger Length

Soft grippers due to their highly compliant material and self-adaptive structures attract more attention to safe and versatile grasping tasks compared to traditional rigid grippers. However, those flexible characteristics limit the strength and the manipulation capacity of soft grippers. In this paper, we introduce a hybrid-driven gripper design utilizing origami finger structures, to offer adjustable finger stiffness and variable grasping range. This gripper is actuated via pneumatic and cables, which allows the origami structure to be controlled precisely for contraction and extension, thus achieving different finger lengths and stiffness by adjusting the cable lengths and the input pressure. A kinematic model of the origami finger is further developed, enabling precise control of its bending angle for effective grasping of diverse objects and facilitating in-hand manipulation. Our proposed design method enriches the field of soft grippers, offering a simple yet effective approach to achieve safe, powerful, and highly adaptive grasping and in-hand manipulation capabilities.

Kinematics, dynamics and control of stiffness-tunable soft robots

Modeling and control methods for stiffness-tunable soft robots (STSRs) have received less attention compared to standard soft robots. A major challenge in controlling STSRs is their infinite degrees of freedom, similar to standard soft robots. In this paper, demonstrate a novel STSR by combing a soft-rigid hybrid spine-mimicking actuator with a stiffness-tunable module. Additionally, we introduce a new kinematic and dynamic modeling methodology for the proposed STSR. Based on the STSR characteristics, we model it as a series of PRP segments, each composed of two prismatic joints(P) and one revolute joint(R). This method is simpler, more generalizable, and more computationally efficient than existing approaches. We also design a multi-input multi-output (MIMO) controller that directly adjusts the pressure of the STSR's three pneumatic chambers to precisely control its posture. Both the novel modeling methodology and MIMO control system are implemented and validated on the proposed STSR prototype.

Performance evaluation of PSO-PID and PSO-FLC for continuum robot's developed modeling and control

Continuum robots are complex structures that require sophisticated modeling and control methods to achieve accurate position and motion tracking along desired trajectories. They are highly coupled, nonlinear systems with multiple degrees of freedom that pose a significant challenge for conventional approaches. In this paper, we propose a system dynamic model based on the Euler-Lagrange formulation with the assumption of piecewise constant curvature (PCC), where we accounts for the elasticity and gravity effects of the continuum robot. We also develop and apply a particle swarm optimization (PSO) algorithm to optimize the parameters of our developed controllers: an inverse dynamic proportional integral derivative (PID) controller and an inverse dynamic fuzzy logic controller (FLC), where we use the integral time of absolute error (ITAE) as the objective function for the PSO algorithm. We validate our proposed model and optimized controllers through different designed trajectories, simulated using our developed unique animated MATLAB simulation. The results show that the PSO-PID controller improves the rise time, overshoot percentage, and settling time by 16.3%, 31.1%, and 64.9%, respectively, compared to the PID controller without PSO. The PSO-FLC controller shows the best performance among all controllers, with a settling time of 0.7 s and a rise time of 0.4 s, leading to the highest level of precision in trajectory tracking. The ITAE error for the PSO-FLC controller is 11.4% and 29.9% lower than that of the PSO-PID and FLC controllers, respectively.

A Pre-Grasping Motion Planning Method Based on Improved Artificial Potential Field for Continuum Robots

Secure and reliable active debris removal methods are crucial for maintaining the stability of the space environment. Continuum robots, with their hyper-redundant degrees of freedom, offer the ability to capture targets of varying sizes and shapes through whole-arm grasping, making them well-suited for active debris removal missions. This paper proposes a pre-grasping motion planning method for continuum robots based on an improved artificial potential field to restrict the movement area of the grasping target and prevent its escape during the pre-grasping phase. The analysis of the grasping workspace ensures that the target is within the workspace when starting the pre-grasping motion planning by dividing the continuum robot into delivery and grasping segments. An improved artificial potential field is proposed to guide the continuum robot in surrounding the target and creating a grasping area. Specifically, the improved artificial potential field consists of a spatial rotating potential field, an attractive potential field incorporating position and posture potential fields, and a repulsive potential field. The simulation results demonstrate the effectiveness of the proposed method. A comparison of motion planning results between methods that disregard and consider the posture potential field shows that the inclusion of the posture potential field improves the performance of pre-grasping motion planning for spatial targets, achieving a success rate of up to 97.8%.

Neurosurgical Education Using Cadaver-Free Brain Models and Augmented Reality: First Experiences from a Hands-On Simulation Course for Medical Students

Background and Objectives: Neurosurgery has been underrepresented in the medical school curriculum. Advances in augmented reality and 3D printing have opened the way for early practical training through simulations. We assessed the usability of the UpSurgeOn simulation-based training model and report first experiences from a hands-on neurosurgery course for medical students. Materials and Methods: We organized a two-day microneurosurgery simulation course tailored to medical students. On day one, three neurosurgeons demonstrated anatomical explorations with the help of life-like physical simulators (BrainBox, UpSurgeOn). The surgical field was projected onto large high-definition screens by a robotic-assisted exoscope (RoboticScope, BHS Technologies). On day two, the students were equipped with microsurgical instruments to explore the surgical anatomy of the pterional, temporal and endoscopic retrosigmoid approaches. With the help of the RoboticScope, they simulated five clipping procedures using the Aneurysm BrainBox. All medical students filled out a digital Likert-scale-based questionnaire to evaluate their experiences. Results: Sixteen medical students participated in the course. No medical students had previous experience with UpSurgeOn. All participants agreed that the app helped develop anatomical orientation. They unanimously agreed that this model should be part of residency training. Fourteen out of sixteen students felt that the course solidified their decision to pursue neurosurgery. The same fourteen students rated their learning experience as totally positive, and the remaining two rated it as rather positive. Conclusions: The UpSurgeOn educational app and cadaver-free models were perceived as usable and effective tools for the hands-on neuroanatomy and neurosurgery teaching of medical students. Comparative studies may help measure the long-term benefits of UpSurgeOn-assisted teaching over conventional resources.

Integrated Design Fabrication and Control of a Bioinspired Multimaterial Soft Robotic Hand

Machines that mimic humans have inspired scientists for centuries. Bioinspired soft robotic hands are a good example of such an endeavor, featuring intrinsic material compliance and continuous motion to deal with uncertainty and adapt to unstructured environments. Recent research led to impactful achievements in functional designs, modeling, fabrication, and control of soft robots. Nevertheless, the full realization of life-like movements is still challenging to achieve, often based on trial-and-error considerations from design to fabrication, consuming time and resources. In this study, a soft robotic hand is proposed, composed of soft actuator cores and an exoskeleton, featuring a multimaterial design aided by finite element analysis (FEA) to define the hand geometry and promote finger's bendability. The actuators are fabricated using molding, and the exoskeleton is 3D-printed in a single step. An ON-OFF controller keeps the set fingers' inner pressures related to specific bending angles, even in the presence of leaks. The FEA numerical results were validated by experimental tests, as well as the ability of the hand to grasp objects with different shapes, weights, and sizes. This integrated solution will make soft robotic hands more available to people, at a reduced cost, avoiding the time-consuming design-fabrication trial-and-error processes.

A Fast Soft Continuum Catheter Robot Manufacturing Strategy Based on Heterogeneous Modular Magnetic Units

Developing small-scale continuum catheter robots with inherent soft bodies and high adaptability to different environments holds great promise for biomedical engineering applications. However, current reports indicate that these robots meet challenges when it comes to quick and flexible fabrication with simpler processing components. Herein, we report a millimeter-scale magnetic-polymer-based modular continuum catheter robot (MMCCR) that is capable of performing multifarious bending through a fast and general modular fabrication strategy. By preprogramming the magnetization directions of two types of simple magnetic units, the assembled MMCCR with three discrete magnetic sections could be transformed from a single curvature pose with a large tender angle to a multicurvature S shape in the applied magnetic field. Through static and dynamic deformation analyses for MMCCRs, high adaptability to varied confined spaces can be predicted. By employing a bronchial tree phantom, the proposed MMCCRs demonstrated their capability to adaptively access different channels, even those with challenging geometries that require large bending angles and unique S-shaped contours. The proposed MMCCRs and the fabrication strategy shine new light on the design and development of magnetic continuum robots with versatile deformation styles, which would further enrich broad potential applications in biomedical engineering.

A Novel Robotic Bronchoscope System for Navigation and Biopsy of Pulmonary Lesions

Transbronchial biopsy sampling, as a minimally invasive method with relatively low risk, has been proved to be a promising treatment in the field of respiratory surgery. Although several robotic bronchoscopes have been developed, it remains a great challenge to balance size and flexibility, while integrating multisensors to realize navigation during complex airway networks. This paper proposes a novel robotic bronchoscope system composed by end effector with relatively small size, relevant actuation unit, and navigation system with path planning and surgical guidance capability. The main part of the end effector is machined by bidirectional groove on a nickel-titanium tube, which can realize bending, rotation, and translation 3 degrees of freedom. A prototype of the proposed robotic bronchoscope system is designed and fabricated, and its performance is tested through several experiments to verify the stiffness, flexibility, and navigation performance. The results show that the proposed system is with good environment adaptiveness, and it can become a promising biopsy method through natural cavity of the human body.

A Versatile Continuum Gripping Robot with a Concealable Gripper

Continuum robots with their inherent compliance provide the potential for crossing narrow unstructured workspace and safely grasping various objects. However, the display gripper increases the size of the robots, and therefore, it tends to get stuck in constrained environments. This paper proposes a versatile continuum grasping robot (CGR) with a concealable gripper. The CGR can capture large objects with respect to the robot's scale using the continuum manipulator and can grasp various objects using the end concealable gripper especially in narrow and unstructured workspaces. To perform the cooperative operation of the concealable gripper and the continuum manipulator, a global kinematic model based on screw theory and a motion planning approach referred to as "multi-node synergy method" for the CGR are presented. The simulation and experimental results show that objects of different shapes and sizes can be captured by the same CGR even in complex and narrow environments. Finally, in the future, the CGR is expected to serve for satellite capture in harsh space environments such as high vacuum, strong radiation, and extreme temperatures.