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Objective: We present a general framework of simultaneous needle shape reconstruction and control input generation for robot-assisted spinal injection procedures, without continuous imaging feedback. Methods: System input-output mapping is generated with a real-time needle-tissue interaction simulation, and single-core FBG sensor readings are used as local needle shape feedback within the same simulation framework. FBG wavelength shifts due to temperature variation is removed by exploiting redundancy in fiber arrangement. Results: Targeting experiments performed on both plastisol lumbar phantoms as well as an ex vivo porcine lumbar section achieved in-plane tip errors of 0.6 ± 0.3 mm and 1.6 ± 0.9 mm , and total tip errors of 0.9 ± 0.7 mm and 2.1 ± 0.8 mm for the two testing environments. Significance: Our clinically inspired control strategy and workflow is self-contained and not dependent on the modality of imaging guidance. The generalizability of the proposed approach can be applied to other needle-based interventions where medical imaging cannot be reliably utilized as part of a closed-loop control system for needle guidance.

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This paper considers the design, manufacture, and testing of a prototype "soft-stiff" worm-inspired robot referred to herein, as the PneumaticallyActuated PeristaLtic Advancing Modular (PALAM) robot. The robot has a modular structure, mimicking the segmented nature of earthworms, and each segment is individually actuated by a set of three pneumatic artificial muscles (PAMs). The PAMs contract when inflated by pressurised air, generating a pulling force and fulfilling the role of biological muscles in the robot. The PAMs are made from the elastomer silicone rubber, which affords the robot flexibility and enables a wide range of real-life applications. A control-system is designed which can inflate any PAM on demand, and hence replicate the peristaltic motion of earthworms in the PALAM robot. Finally, this paper discusses a successful, low-cost, and widely accessible approach for the manufacture of the PAMs utilised herein. The PAMs can be scaled dimensionally and made from different materials with varying mechanical properties and behaviours, meaning that they are suitable for use in a wide range of robotics applications.

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Micro/nanomotors represent a promising class of drug delivery carriers capable of converting surrounding chemical or external energy into mechanical power, enabling autonomous movement. Their distinct autonomous propulsive force distinguishes them from other carriers, offering significant potential for enhancing drug penetration across cellular and tissue barriers. A comprehensive understanding of micro/nanomotor dynamics with various power sources is crucial to facilitate their transition from proof-of-concept to clinical application. In this review, micro/nanomotors are categorized into three classes based on their energy sources: endogenously stimulated, exogenously stimulated, and live cell-driven. The review summarizes the mechanisms governing micro/nanomotor movements under these energy sources and explores factors influencing autonomous motion. Furthermore, it discusses methods for controlling micro/nanomotor movement, encompassing aspects related to their structure, composition, and environmental factors. The remarkable propulsive force exhibited by micro/nanomotors makes them valuable for significant biomedical applications, including tumor therapy, bio-detection, bacterial infection therapy, inflammation therapy, gastrointestinal disease therapy, and environmental remediation. Finally, the review addresses the challenges and prospects for the application of micro/nanomotors. Overall, this review emphasizes the transformative potential of micro/nanomotors in overcoming biological barriers and enhancing therapeutic efficacy, highlighting their promising clinical applications across various biomedical fields.

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We propose a data-driven, model-free adaptive sliding mode control (MFASMC) approach to address the Haidou-1 ARV under-actuated motion control problem with uncertainties, including external disturbances and parameter perturbations. Firstly, we analyzed the two main difficulties in the motion control of Haidou-1 ARV. Secondly, in order to address these problems, a MFASMC control method was introduced. It is combined by a model-free adaptive control (MFAC) method and a sliding mode control (SMC) method. The main advantage of the MFAC method is that it relies only on the real-time measurement data of an ARV instead of any mathematical modeling information, and the SMC method guarantees the MFAC method's fast convergence and low overshooting. The proposed MFASMC control method can maneuver Haidou-1 ARV cruising at the desired forward speed, heading, and depth, even when the dynamic parameters of the ARV vary widely and external disturbances exist. It also addresses the problem of under-actuated motion control for the Haidou-1 ARV. Finally, the simulation results, including comparisons with a PID method and the MFAC method, demonstrate the effectiveness of our proposed method.

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Various industrial processes require motion profiles that determine machine movement over time for workpiece transfer, with the goal of minimizing transfer time to enhance productivity. However, pursuing the maximum speed can trigger excessive motion-induced vibration, which can reduce the manufacturing efficiency, accuracy and operational lifespan of the system. This study proposes a generalized motion profile optimization method that considers the strategic placement of inherent zeros within the motion profile: each inherent zero contributes to the reduction of residual vibration or optimization of arrival time. By offering freedom to the placement of zeros, four optimization options for polynomial-based motion profiles of each order are proposed, expanding on existing work that offered a single option. All proposed optimization options have closed-form solutions, making them easily applicable to industrial applications. The practical applicability of the suggested methodology is demonstrated through case studies and experiments.

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Although there are many studies on repetitive motion control of robots, few schemes and algorithms involve posture collaboration motion control of constrained dual-arm robots in three-dimensional scenes, which can meet more complex work requirements. Therefore, this study establishes the minimum displacement repetitive motion control scheme for the left and right robotic arms separately. On the basis of this, the design mentality of the proposed dual-arm posture collaboration motion control (DAPCMC) scheme, which is combined with a new joint-limit conversion strategy, is described, and the scheme is transformed into a time-variant equation system (TVES) problem form subsequently. To address the TVES problem, a novel adaptive Taylor-type discretized recurrent neural network (ATT-DRNN) algorithm is devised, which fundamentally solves the problem of calculation accuracy which cannot be balanced well with the fast convergence speed. Then, stringent theoretical analysis confirms the dependability of the ATT-DRNN algorithm in terms of calculation precision and convergence rate. Finally, the effectiveness of the DAPCMC scheme and the excellent convergence competence of the ATT-DRNN algorithm is verified by a numerical simulation analysis and two control cases of dual-arm robots.

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The field of large numerical aperture microscopy has witnessed significant advancements in spatial and temporal resolution, as well as improvements in optical microscope imaging quality. However, these advancements have concurrently raised the demand for enhanced precision, extended range, and increased load-bearing capacity in objective motion carrier (OMC). To address this challenge, this study introduces an innovative OMC that employs a ball screw mechanism as its primary driving component. Furthermore, a robust nonlinear motion control strategy has been developed, which integrates fast nonsingular terminal sliding mode, experimental estimation techniques, and adaptive radial basis neural network, to mitigate the impact of nonlinear friction within the ball screw mechanism on motion precision. The stability of the closed-loop control system has been rigorously demonstrated through Lyapunov theory. Compared with other enhanced sliding mode control strategies, the maximum error and root mean square error of this controller are improved by 33% and 34% respectively. The implementation of the novel OMC has enabled the establishment of a high-resolution bio-optical microscope, which has proven its effectiveness in the microscopic imaging of retinal organoids.

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A robust model-free adaptive iterative learning control (R-MFAILC) algorithm is proposed in this work to address the issue of laterally controlling an autonomous bus. First, according to the periodic repetitive working characteristics of autonomous buses, a novel dynamic linearized method used in the iterative domain is utilized, and a time-varying data model with a pseudo gradient (PG) is given. Then, the R-MFAILC controller is designed with a proposed adaptive attenuation factor. The proposed algorithm's advantage lies in the R-MFAILC controller, which solely utilizes the input and output data of the regulated entity. Moreover, the R-MFAILC controller has strong robustness and can handle the nonlinear measurement disturbances of the system. In simulations based on the Truck-Sim simulation platform, the effectiveness of the proposed algorithm is verified. A rigorous mathematical analysis is employed to demonstrate the stability and convergence of the proposed algorithm.

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In this work, we focus on overcoming the challenge of a snake robot climbing on the outside of a bifurcated pipe. Inspired by the climbing postures of biological snakes, we propose an S-shaped rolling gait designed using curve transformations. For this gait, the snake robot's body presenting an S-shaped curve is wrapped mainly around one side of the pipe, which leaves space for the fork of the pipe. To overcome the difficulty in constructing and clarifying the S-shaped curve, we present a method for establishing the transformation between a plane curve and a 3D curve on a cylindrical surface. Therefore, we can intuitively design the curve in 3D space, while analytically calculating the geometric properties of the curve in simple planar coordinate systems. The effectiveness of the proposed gait is verified by actual experiments. In successful configuration scenarios, the snake robot could stably climb on the pipe and efficiently cross or climb to the bifurcation while maintaining its target shape.

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Introduction: Patients suffering from limb movement disorders require more complete rehabilitation treatment, and there is a huge demand for rehabilitation exoskeleton robots. Flexible and reliable motion control of exoskeleton robots is very important for patient rehabilitation. Methods: This paper proposes a novel exoskeleton robotic system for lower limb rehabilitation. The designed lower limb rehabilitation exoskeleton robot mechanism is mainly composed of the hip joint mechanism, the knee joint mechanism and the ankle joint mechanism. The forces and motion of the exoskeleton robot were analyzed in detail to determine its design parameters. The robot control system was developed to implement closed-loop position control and trajectory planning control of each joint mechanism. Results: Multiple experiments and tests were carried out to verify robot's performance and practicality. In the robot angular response experiments, the joint mechanism could quickly adjust to different desired angles, including 15°, 30°, 45°, and 60°. In the trajectory tracking experiments, the exoskeleton robot could complete tracking movements of typical actions such as walking, standing up, sitting down, go upstairs and go downstairs, with a maximum tracking error of ±5°. Robotic wearing tests on normal people were performed to verify the assistive effects of the lower limb rehabilitation exoskeleton at different stages. Discussion: The experimental results indicated that the exoskeleton robot has excellent reliability and practicality. The application of this exoskeleton robotic system will help paralyzed patients perform some daily movements and sports.

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PURPOSE: Intracardiac transcatheter interventions allow for reducing trauma and hospitalization stays as compared to standard surgery. In the treatment of mitral regurgitation, the most widely adopted transcatheter approach consists in deploying a clip on the mitral valve leaflets by means of a catheter that is run through veins from a peripheral access to the left atrium. However, precise manipulation of the catheter from outside the body while copying with the path constraints imposed by the vessels remains challenging. METHODS: We proposed a path tracking control framework that provides adequate motion commands to the robotic steerable catheter for autonomous navigation through vascular lumens. The proposed work implements a catheter kinematic model featuring nonholonomic constraints. Relying on the real-time measurements from an electromagnetic sensor and a fiber Bragg grating sensor, a two-level feedback controller was designed to control the catheter. RESULTS: The proposed method was tested in a patient-specific vessel phantom. A median position error between the center line of the vessel and the catheter tip trajectory was found to be below 2 mm, with a maximum error below 3 mm. Statistical testing confirmed that the performance of the proposed method exhibited no significant difference in both free space and the contact region. CONCLUSION: The preliminary in vitro studies presented in this paper showed promising accuracy in navigating the catheter within the vessel. The proposed approach enables autonomous control of a steerable catheter for transcatheter cardiology interventions without the request of calibrating the intuitive parameters or acquiring a training dataset.

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Recently, magnetically actuated micro/nanorobots hold extensive promises in biomedical applications due to their advantages of noninvasiveness, fuel-free operation, and programmable nature. While effectively promised in various fields such as targeted delivery, most past investigations are mainly displayed in magnetic control of individual micro/nanorobots. Facing practical medical use, the micro/nanorobots are required for the development of swarm control in a closed-loop control manner. This review outlines the recent developments in magnetic micro/nanorobot swarms, including their actuating fundamentals, designs, controls, and biomedical applications. The fundamental principles and interactions involved in the formation of magnetic micro/nanorobot swarms are discussed first. The recent advances in the design of artificial and biohybrid micro/nanorobot swarms, along with the control devices and methods used for swarm manipulation, are presented. Furthermore, biomedical applications that have the potential to achieve clinical application are introduced, such as imaging-guided therapy, targeted delivery, embolization, and biofilm eradication. By addressing the potential challenges discussed toward the end of this review, magnetic micro/nanorobot swarms hold promise for clinical treatments in the future.

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For decades, it has been difficult for small-scale legged robots to conquer challenging environments. To solve this problem, we propose the introduction of a bioinspired soft spine into a small-scale legged robot. By capturing the motion mechanism of rat erector spinae muscles and vertebrae, we designed a cable-driven centrally symmetric soft spine under limited volume and integrated it into our previous robotic rat SQuRo. We called this newly updated robot SQuRo-S. Because of the coupling compliant spine bending and leg locomotion, the environmental adaptability of SQuRo-S significantly improved. We conducted a series of experiments on challenging environments to verify the performance of SQuRo-S. The results demonstrated that SQuRo-S crossed an obstacle of 1.07 body height, thereby outperforming most small-scale legged robots. Remarkably, SQuRo-S traversed a narrow space of 0.86 body width. To the best of our knowledge, SQuRo-S is the first quadruped robot of this scale that is capable of traversing a narrow space with a width smaller than its own width. Moreover, SQuRo-S demonstrated stable walking on mud-sand, pipes, and slopes (20°), and resisted strong external impact and repositioned itself in various body postures. This work provides a new paradigm for enhancing the flexibility and adaptability of small-scale legged robots with spine in challenging environments, and can be easily generalized to the design and development of legged robots with spine of different scales.

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In recent years, soft robots gain increasing attention as a result of their compliance when operating in unstructured environments, and their flexibility that ensures safety when interacting with humans. However, challenges lie on the difficulty to develop control algorithms due to various limitations induced by their soft structure. In this paper, we introduce a novel technique that aims to perform motion control of a modular bio-inspired soft-robotic arm, with the main focus lying on facilitating the qualitative reproduction of well-specified periodic trajectories. The introduced method combines the notion behind two previously developed methodologies both based on the Movement Primitive (MP) theory, by exploiting their capabilities while coping with their main drawbacks. Concretely, the requested actuation is initially computed using a Probabilistic MP (ProMP)-based method that considers the trajectory as a combination of simple movements previously learned and stored as a MP library. Subsequently, the key components of the resulting actuation are extracted and filtered in the frequency domain. These are eventually used as input to a Central Pattern Generator (CPG)-based model that takes over the generation of rhythmic patterns at the motor level. The proposed methodology is evaluated on a two-module soft arm. Results show that the first algorithmic component (ProMP) provides an immediate estimation of the requested actuation by avoiding time-consuming training, while the latter (CPG) further simplifies the execution by allowing its control through a low-dimensional parameterization. Altogether, these results open new avenues for the rapid acquisition of periodic movements in soft robots, and their compression into CPG parameters for long-term storage and execution.

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Swarms of self-propelled micromotors can mimic the processes of natural systems and construct artificial intelligent materials to perform complex collective behaviors. Compared to self-propelled Janus micromotors, the isotropic colloid motors, also called micromotors or microswimmers, have advantages in self-assembly to form micromotor swarms, which are efficient in resistance to external disturbance and the delivery of large quantity of cargos. In this minireview, we summarize the fundamental principles and interactions for the assembly of isotropic active particles to generate micromotor swarms. Recent discoveries based on either catalytic or external physical field-stimulated micromotor swarms are also presented. Then, the strategy for the reconstruction and motion control of micromotor swarms in complex environments, including narrow channels, maze, raised obstacles, and high steps/low gaps, is summarized. Finally, we outline the future directions of micromotor swarms and the remaining challenges and opportunities.

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The inverse kinematics plays a vital role in the planning and execution of robot motions. In the design of robotic motion control for NAO robot arms, it is necessary to find the proper inverse kinematics model. Neural networks are such a data-driven modeling technique that they are so flexible for modeling the inverse kinematics. This inverse kinematics model can be obtained by means of training neural networks with the dataset. This training process cannot be achieved without the presence of the dataset. Therefore, the contribution of this research is to provide the dataset to develop neural networks-based inverse kinematics model for NAO robot arms. The dataset that we created in this paper is named ARKOMA. ARKOMA is an acronym for ARif eKO MAuridhi, all of whom are the creators of this dataset. This dataset contains 10000 input-output data pairs in which the end-effector position and orientation are the input data and a set of joint angular positions are the output data. For further application, this dataset was split into three subsets: training dataset, validation dataset, and testing dataset. From a set of 10000 data, 60 % of data was allocated for the training dataset, 20 % of data for the validation dataset, and the remaining 20 % of data for the testing dataset. The dataset that we provided in this paper can be applied for NAO H25 v3.3 or later.

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This paper studies the AWC (Active Wafer Centering) algorithm for the movement control and wafer calibration of the handling robot in semiconductor manufacturing to prevent wafer surface contact and contamination during the transfer process. The mechanical and software architecture of the wafer-handling robot is analyzed first, which is followed by a description of the experimental platform for semiconductor manufacturing methods. Secondly, the article utilizes the geometric method to analyze the kinematics of the semiconductor robot, and it decouples the motion control of the robot body from the polar coordinates and joint space. The wafer center position is calibrated using the generalized least-square inverse method for AWC correction. The AWC algorithm is divided into calibration, deviation correction, and retraction detection. These are determined by analyzing the robot's wafer calibration process. In conclusion, the semiconductor robot's motion control and AWC algorithm are verified through experiments for correctness, feasibility, and effectiveness. After the wafer correction, the precision of AWC is <± 0.15 mm, which meets the requirements for transferring robot wafers.

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The prevalence of ineffective corrective surgeries for ocular motor disorders calls for a robotic eye platform in aiding ophthalmologists to better understand the biomechanisms of human eye movement. This letter presents the first hardware design and implementation of a 2-DOF robotic eye driven by super-coiled polymer (SCP) artificial muscles. While our previous work designed and simulated a deep deterministic policy gradient (DDPG) learning-based controller that requires full-state feedback of the SCP-driven robotic eye, measuring the temperature states of the slender SCPs is generally impractical for the ubiquitously aimed robot. To address this predicament, this letter proposes a reduced-order state observer to estimate the temperature of SCPs given the kinematic measurements. Combining the designed observer and the learning-based controller, the closed-loop output feedback control is implemented on the robotic eye prototype to examine its performance on three classical types of eye movements: visual fixation, saccadic pursuit, and smooth pursuit. The experimental results are presented which successfully validate the observer-based output control of the SCP-driven robotic eye.

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A driving-safety-zone-model-oriented motion planning framework (DSZMF) is proposed for autonomous platoons in heterogeneous driving environments with complex driving behaviors and interactions between human-driven and autonomous vehicles. As an extension of the responsibility-sensitive-safety (RSS) model, the driving safety zone model ensures that autonomous truck platoons adhere to explicit and implicit traffic rules as rational traffic participants. It consists of three zones created by safe distances and artificial potential field (APF), namely the restricted zone, the coordinated zone, and pre-cautionary zone. The Rational Traffic Participant (RTP) module is created by using a Finite State Machine (FSM) to provide an optimized platooning behavioral strategy based on the dynamic states of surrounding vehicles. Furthermore, the distributed model predictive controllers are utilized for motion planning, while the H infinity controller is developed to maintain the string stability of the autonomous platoon. The proposed DSZMF generates behavioral decisions by thoroughly considering the driving safety zone model, string stability, and multiple vehicle dynamics constraints. Finally, three critical scenarios are co-simulated for case studies, and the simulation results demonstrate that the DSZMF improves the safe time integration rate over the existing MCF by 18.9%, 11.1%, and 11.6% in three scenarios, respectively. In addition, DSZMF increases the minimum longitudinal and lateral Time to Collision (TTC) values to reduce collision risks. The case studies validate the efficacy of the proposed method for safety assurance and collaborative control of the autonomous platoon.

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Recently, with the trend of redundancy design, the importance of synchronous motion control of multiple motors has been emphasized in various fields such as automotive, construction, and industrial engineering. Therefore, this paper proposed a novel passive decomposition-based robust synchronous control strategy for a multi-motor system, guaranteeing that both the tracking error of each motor and the synchronous error between motors are ultimately and synchronously bounded, even under the presence of parametric uncertainty and unstructured external disturbance. Specifically, a passive decomposition is used to obtain the locked and shape systems from the original system, and then a sliding mode control system along with robust compensations is designed for each decomposed system to achieve the precise synchronous motion control of the n number of motors. Here, the controller for the locked system reduces the tracking errors of motors for a given desired trajectory, while the controller for the shaped system decreases the synchronous errors between motors. Furthermore, the control system is generally and conveniently formulated to adopt the arbitrary n number of motors that must track a given desired trajectory and be synchronized. Compared to other related studies, this work especially focused on increasing the robustness of the entire system using both high-order sliding mode control and two separate compensation terms for model uncertainty and unstructured external disturbance. Finally, to validate the effectiveness of the proposed synchronous control strategy, the extensive experimental studies on two/three/four-geared BLDC motors with a high dead-zone effect were conducted, and we also compared the synchronous control performance of the proposed control strategy with the other representative control approaches, a master-slave control scheme and an independent one to address the superiority of the proposed control system. Regardless of the number of motors, due to the robustness of the control system, it is found that the proposed control ensures the tracking and synchronous errors are less than 1 degree for the sine-wave trajectory while it guarantees that the errors are below 1.5 degree for the trapezoidal trajectory. This control approach can be widely and generally applied to the multiple motor control required in various engineering fields.