Robotic Mechanisms Research Articles

Preliminary Study on Current Research Status and Clinical Application of Lower Limb Rehabilitation Robot Mechanisms

As an assistive device to restore lower limb athletic ability, human lower limb rehabilitation robots are becoming increasingly important in the field of rehabilitation and clinical applications. With the advancement of science and technology, both domestic and foreign research in this field has been developed. This study provides a detailed overview of the development of lower limb rehabilitation robots and reviews the current status of clinical applications, with a focus on mechanism research, from the perspectives of degrees of freedom, workspace, singularity, gait simulation, kinematic simulation and human-robot interaction, etc. The results show that domestic research on lower limb rehabilitation robots focus on the design and optimization of the mechanism configuration, while foreign research focus on the improvement and innovation of the control system and training mode based on human-computer interaction. Finally, the current state of research is combined with an outlook on future trends.

Inverse Kinematics Implementation Techniques in Robotics

Inverse kinematics is crucial for offering precision in controlling robotic mechanisms, making them versatile for intricate roles in fields like manufacturing, medical services, and digital animation. It also facilitates innovations in automation, efficiency, and safety, thereby boosting overall performance in diverse industries. This review aims to present an overview of how deep learning techniques are employed in inverse kinematics, targeting researchers seeking to explore different approaches in this field. When calculating the inverse kinematics using the traditional approach, the complexity and non-linearity of a high degree of freedom robotic systems can pose limitations and lead to suboptimal results. Comparing different models of deep learning, this review focuses on the potential of deep learning as a suitable alternative approach for solving inverse kinematic problems. Also, it provides guidelines for researchers in utilizing deep learning for inverse kinematics applications while emphasizing the ethical and societal implications that arise from these advancements. Further emphasis is on the significance of case studies, insights into real-world applications, the challenges encountered, and future directions for research. Overall, this review covers various aspects of deep learning models' implementation in inverse kinematics. It also informs them about the potential of these models in advancing the field of inverse kinematics, paving the way for more precise and adaptive robotic movements, improved human-robot interactions, and greater autonomy in a wide range of industries.

Design and Reality-Based Modeling Optimization of a Flexible Passive Joint Paddle for Swimming Robots.

Rowing motion with paired propellers is an essential actuation mechanism for swimming robots. Previous work in this field has typically employed flexible propellers to generate a net thrust or torque by using changes in the compliance values of flexible structures under the influence of a fluid. The low stiffness values of the flexible structures restrict the upper limit of the oscillation frequency and amplitude, resulting in slow swimming speeds. Furthermore, complex coupling between the fluid and the propeller reduce the accuracy of flexible propeller simulations. A design of a flexible passive joint paddle was proposed in this study, and a dynamics model and simulation of the paddle were experimentally verified. In order to optimize the straight swimming speed, a data-driven model was proposed to improve the simulation accuracy. The effects of the joint number and controller parameters on the robot's straight swimming speed were comprehensively investigated. The multi-joint paddle exhibited significantly improved thrust over the single-joint paddle in a symmetric driving mode. The data-driven model reduced the total error of the simulated data of the propulsive force in the range of control parameters to 0.51%. Swimming speed increased by 3.3 times compared to baseline. These findings demonstrate the utility of the proposed dynamics and data-driven models in the multi-objective design of swimming robots.

A Magnet‐Driven Soft Bistable Actuator

AbstractBistable morphing structures are widely used as actuation mechanisms in soft actuators, soft robotics, energy absorbers, mechanical computers, optical lenses, metamaterials, and flexible electronics. However, untethered actuators, repetitive actuators, and hybrid‐assembly (containing in‐plane‐assembly and out‐of‐plane‐assembly) actuators remain challenging to realize using existing bistable structures, which hinders the novel application of such actuators in research, engineering, and daily life. This problem is solved by fabricating a magnet‐driven soft bistable actuator (MSBA) unit. The self‐buckling of the circular polydimethylsiloxane (PDMS) sheet ensures the bistability of the actuator and allows it to operate as an independent unit, free of external constraints. The reorientation of neodymium‐iron‐boron (NdFeB) microparticles embedded in the PDMS sheet enables the dome‐shaped actuators to exhibit repetitive snapping under the stimulus of a direction‐switching magnetic field. The potential of this MSBA unit in bionics, electronics, and biomechanics applications is demonstrated in systematic studies involving modification of the buckling deflection and magnetic moment density. The MSBA unit exhibits excellent performance in hybrid designs and intelligent systems.

Machine Learning in Robotic Ultrasound Imaging: Challenges and Perspectives

This article reviews recent advances in intelligent robotic ultrasound imaging systems. We begin by presenting the commonly employed robotic mechanisms and control techniques in robotic ultrasound imaging, along with their clinical applications. Subsequently, we focus on the deployment of machine learning techniques in the development of robotic sonographers, emphasizing crucial developments aimed at enhancing the intelligence of these systems. The methods for achieving autonomous action reasoning are categorized into two sets of approaches: those relying on implicit environmental data interpretation and those using explicit interpretation. Throughout this exploration, we also discuss practical challenges, including those related to the scarcity of medical data, the need for a deeper understanding of the physical aspects involved, and effective data representation approaches. We conclude by highlighting the open problems in the field and analyzing different possible perspectives on how the community could move forward in this research area.

Optimizing the accuracy of an industrial robot: A model for improving positional accuracy

This article investigates methods for improving the positional accuracy of industrial robots, focusing on their movement dynamics, trajectory management, and fundamental principles of precise positioning. The research examines nine critical points within the gripper's working zone, shaped as a parallelepiped, which is essential for defining the accuracy limits of industrial robots. The study addresses the challenge of aligning the capture device with the ninth point by proposing a solution that involves precise movements along the diagonals of the parallelepiped to enhance accuracy. The article provides a detailed analysis of the construction and mechanics of industrial robots, emphasizing how different link configurations impact performance. It highlights the benefits of fewer links, which tend to maintain stable positional accuracy, while also discussing how an increase in the number of links leads to trajectory variations. These variations affect both robots with mobile bases and fixed- base robots, impacting practical applications such as machining where managing these trajectories is crucial to avoid collisions and ensure smooth operation. Additionally, the research explores the role of various coordinate systems in shaping the working zone and basic movements of industrial robots. It covers rectangular, cylindrical, spherical, and angular coordinate systems, each offering different perspectives on the robot's operational area. This comprehensive analysis aims to address the complexities associated with enhancing positional accuracy in industrial robotics.

Design and analysis of anti-overturning mechanism for magnetic wall-climbing robot

Design and analysis of anti-overturning mechanism for magnetic wall-climbing robot

Study on Locomotion Mechanism of Pipe Inspection Robot with Rotating Horn

Study on Locomotion Mechanism of Pipe Inspection Robot with Rotating Horn

Task Accuracy Enhancement for a Surgical Macro-Micro Manipulator With Probabilistic Neural Networks and Uncertainty Minimization

Accurate robot kinematic modelling is a major component for autonomous robot control to guarantee safety and precision during task execution. In surgical robotics complex robotic structures and actuation mechanisms are generally employed, therefore machine learning techniques can be adopted to build the model of the robot. Probabilistic neural networks are a class of learning approaches that provide information about the uncertainty of the learnt models. In this work we compare two different probabilistic neural networks (Bayesian and Evidential Neural Networks) to model the kinematics of a surgical robotic instrument and propose a control strategy based on Hierarchical Quadratic Programming (HQP) capable of exploiting the model uncertainty to improve the accuracy and safety of the controller. Simulation and real world experiments on different autonomous path tracking tasks show that the model uncertainty highly affects the control performances and prove the effectiveness of the proposed controller in improving task execution. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The push towards reducing invasiveness and patient’s traumas in surgery has lead to the requirement of miniaturized and highly articulated robots. This however comes at the cost of having systems that are hard to model and control, which is one of the major limitations for autonomy in surgical robotics. Machine learning has become very effective in modelling complex systems and probabilistic approaches additionally allow estimating the confidence of the learnt model. In robotics field where high precision is required to perform an autonomous task, like in minimally invasive surgery, the robot model needs to be very accurate and controllers need to guarantee safety in performing the desired task, while satisfying additional motion constraints imposed by the application scenario. This work proposes the use of probabilistic neural networks to model the complexity of a surgical robotic instrument and a control strategy capable of ensuring safety by maximizing model’s confidence and guaranteeing satisfaction of imposed motion constraints. In this work a macro-micro manipulator setup is employed, consisting of an articulated surgical robotic instrument connected to a serial-link manipulator. The proposed modelling and control approaches can be used in any other field where controllers need to highly rely on the robot model due to limitations in using external sensors and where leveraging information about model’s confidence can be beneficial. Currently, the work focuses only on pure kinematic modelling and control, thus neglecting any possible interaction with the environment. Future work will focus on addressing this limitation in order to ensure proper force control and effective autonomy.

Stepping towards independence: a creative low-cost robotic ankle-foot mechanism for post-stroke rehabilitation

Stepping towards independence: a creative low-cost robotic ankle-foot mechanism for post-stroke rehabilitation

Pole Climbing Robot for Fire Fighting and Metal Detection

Abstract: A new pole climbing robot mechanism is proposed and the structure and features of the mechanism are demonstrated. The robot's processes of pulling the pole, dropping the pole, active work, climbing the pole, fixing the pole and passing obstacles were analyzed. The main idea obtained by the pole climbing robot was explained in detail and the control system of the pole climbing robot was established. The analysis shows that the mechanism has features such as compact structure, easy control and efficient movement.

Implementation and Recognition of Waste Management in Smart Cities Using IOT

Abstract: The Internet of Things (IoT) is enabled by the proliferation of devices such as RFIDs, sensors, and actuators. As an expected infrastructure for the envisioned concept of a Smart City, the Internet of Things (IoT) opens up new possibilities for city management. The Internet of Things vision presents intriguing and cost-effective alternatives for big data collection. The municipal rubbish bins are overflowing in many places, and they must be cleaned on a regular basis. Recently, it has been observed that dustbins in public places such as hospitals, educational institutes, and industries are overflowing. Overflowing garbage cans produce unsanitary conditions that can spread disease. Furthermore, the rapid increase in population waste leads to inefficient waste management. We designed a new method, the "Smart City Garbage Collection Monitoring System," to avert this issue. Urbanisation has increased dramatically in recent decades. At the same time, trash output is increasing. Waste management has been an important subject to consider. This system is a means of achieving this noble goal. The smart bin in this article is developed on a microcontroller-based platform Arduino UNO board, which is interfaced with a GSM modem and Ultrasonic sensor, as well as the weight Sensor, which is used to calculate the weight of the dustbins. Arduino will be programmed so that when the dustbin is filled, the remaining height from the threshold height will be displayed. Once the garbage reaches the threshold level, the ultrasonic sensor will trigger the location data, continuously alerting the required authority until the trash in the dustbin is squashed. According to the location, the power will send the message to the respective operator; a garbage vehicle can collect the garbage, which is done with the help of a robot mechanism.

Управление статически неустойчивым шагающим аппаратом при ограниченном трении в опоре

A non-trivial problem in the biped walking is the preservation of stability under external influences without slowing down velocity. A dynamic walking control algorithm with a hierarchical structure is proposed in the article. The lower control level is organized according to the principle of tracking drives and ensures the position of the robot`s body using all given possible coordinates. The problem of traffic stabilization is solved at the upper level. An ideal mechanism — a flat inverted mathematical pendulum is considered in the feedback circuit. Its equations are synchronized by the moment of the step ending. Particular attention is paid to the interaction of the robot foot with the support surface to determine the conditions for the absence of slippage. Proposed control approach makes it possible to control the current position of the robot`s structure in the space in different walking modes without slowing down velocity. Critically assessing the results and prospects of the research, we can conclude about their use-fulness in the development of artificial intelligence cooperation with humans, about possible applications in everyday life, a specific environment and industry. The proposed approach may be applicable to the control of the walking mechanisms of similar mobile robots.

Derivation of high-contracted topology graphs for the type synthesis of complex closed robotic mechanisms with more mechanical advantages

AbstractChallenges/shortages of existing research: Both a topology graph TG and a contracted graph CG are the important tool for the type synthesis of mechanisms and have been studied. Let (b, t, q, p, h) be (binary, ternary, quaternary, pentagonal, hexagonal) link, respectively. Existing TG may include (b, t, q, p, h, …), and existing CG may include (t, q, p, h, …) and exclude b. Therefore, their derivation and isomorphism identification are quite complicated.The significance of this research: A novel high-contracted topology graph Gj/i including n (q, p, h, …) is proposed in this paper. Since Gj/i excludes (b, t), Gj/i must be more simple than complex TG and CG, and the derivation and isomorphism identification of various Gj/i must be more easily than that of the complex TG and CG.This paper focuses on the derivation and isomorphism identification of various Gj/i for deriving TG and type synthesis of the complex closed robotic mechanisms with more mechanical advantages and useful functions. First, the conceptions for deriving Gj/i and the relations between Gj/i and the basic links are explained, the relative rules are determined for representing Gj/i and reducing isomorphism of Gj/i using the character strings and the digit groups, and many different Gj/i are represented based on determined rules. Second, many different Gj/i (j = 0, …, 17) are constructed using a circle, several composite curves, and vertex points on the circle based on the character strings and the digit groups. Third, a condition for identifying isomorphism Gj/i is discovered and proved using the composite curves and their connections in Gj/i. Some isomorphism Gj/i are identified from constructed different Gj/i. Finally, different Gj/i (G7/7, G11/11) are illustrated to derive TGs, several complex closed robotic mechanisms are synthesized using derived TGs for creating robotic gripper, a long-stroke robotic pumping unit, and a tunnel robotic excavator.

Design and Optimization Analysis of Downhole Robot Support Mechanism Based on Finite Element Method

To increase the traction force of the telescopic downhole robot, the support mechanism of the downhole robot was optimized, and a slider-spring transmission mechanism was introduced to solve the problem of jamming. A mathematical model of the support mechanism was established, covering the relationship between the contact normal pressure and the hydraulic cylinder load. The finite element simulation model of the friction block of the support mechanism was established based on Ansys software. Stress analysis of the casing under different contact normal pressures was carried out, and the frictional characteristics of the support mechanism friction block were obtained. The results show that the support mechanism composed of four linkages and friction locking blocks proposed in this paper can adapt well to casing of different diameters. The slider spring, as a transmission mechanism, has the elasticity and buffering effect, which makes the support mechanism have a certain flexibility, alleviating the vibration impact during obstacle crossing and reducing the impact on logging instruments. It enables the robot to passively adjust during obstacle crossing, effectively improving the traction capability of the downhole robot. Through finite element simulation, it was found that the depth of the plow groove produced by the friction block under normal pressure is basically proportional to the normal pressure. The greater the normal pressure, the deeper the plow groove, and the plow groove friction coefficient increases continuously with the increase of normal pressure, but the change in the plow groove friction coefficient gradually decreases as the pressure increases.

Design and kinematic analysis of a rope-driven linkage frog-like swimming robot with multidirectional controllable motion

In this paper, a rope-driven linkage frog swimming robot is constructed in accordance with the delicate structure of the frog swimming mode. First, a rope-driven linkage limb propulsion mechanism is proposed. This mechanism not only enables the robot to achieve the superior motion characteristics and mechanical performance of the four limbs of a frog swimming in water but also makes the limb structures more flexible and lightweight. Using the D-H parametric method with geometric relations, the swimming kinematic model of this robotic limb propulsion mechanism is established, and the stability margin and recovery moment equations are derived. Second, based on the above mathematical model, the motion space and stability analysis of the robot limb ends are simulated by MATLAB software. Finally, an experimental prototype with a size of 18 cm in length × 7 cm in width × 7 cm in height, combined with a control system, is developed. The experimental result verifies the rationality and correctness of the robot's structural design, theoretical derivation and control system and proves that it has superior motion capability with a straight-line swimming speed of 0.54 m/s, a horizontal surface turning speed of 100°/s (turning radius of approximately 0.18 m), and an upwards and downwards submerged swimming speed of 9 cm/s during a single swim in the water.

Fuzzy Controller Implemented for Movement of a Tendon-Driven 3D Robotic Lumbar Spine Mechanism.

Notable efforts have been devoted to the development of biomechanical models of the spine, so the development of a motion system to control the spine becomes expressively relevant. This paper presents a fuzzy controller to manipulate the movement of a 3D robotic mechanism of the lumbar spine, which is driven by tendons. The controller was implemented in Matlab/Simulink R2023a software, MathWorks (Brazil), considering mathematical modeling based on the Lagrangian methodology for simulating the behavior of the lumbar spine dynamic movement. The fuzzy controller was implemented to perform movements of two joints of the 3D robotic mechanism, which consists of five vertebrae grouped into two sets, G1 and G2. The mechanism's movements are carried out by four servomotors which are driven by readings from two sensors. For control, the linguistic variables of position, velocity and acceleration were used as controller inputs and the torque variables were used for the controller output. The experimental tests were carried out by running the fuzzy controller directly on the 3D physical model (external to the simulation environment) to represent flexion and extension movements analogous to human movements.

Bio-Inspired Untethered Robot-Sensor Platform for Minimally Invasive Biomedical Sensing.

Conventional catheter- or probe-based in vivo biomedical sensing is uncomfortable, inconvenient, and sometimes infeasible for long-term monitoring. Existing implantable sensors often require an invasive procedure for sensor placement. Untethered soft robots with the capability to deliver the sensor to the desired monitoring point hold great promise for minimally invasive biomedical sensing. Inspired by the locomotion modes of snakes, we present here a soft kirigami robot for sensor deployment and real-time wireless sensing. The locomotion mechanism of the soft robot is achieved by kirigami patterns that offer asymmetric tribological properties that mimic the skin of the snake. The robot exhibits good deployability, excellent load capacity (up to 150 times its own weight), high-speed locomotion (0.25 body length per step), and wide environmental adaptability with multimodal movements (obstacle crossing, locomotion in wet and dry conditions, climbing, and inverted crawling). When integrated with passive sensors, the versatile soft robot can locomote inside the human body, deliver the passive sensor to the desired location, and hold the sensor in place for real-time monitoring in a minimally invasive manner. The proof-of-concept prototype demonstrates that the platform can perform real-time impedance monitoring for the diagnosis of gastroesophageal reflux disease.

Development of a Robotic Soft Tissue Collection Mechanism for Disease Detection

This research presents the comprehensive design, development, and testing of an innovative robotic mechanism tailored for the collection of soft tissue samples intended for disease detection applications. The proposed system integrates advanced technologies, such as hydrogels, chambers, and sensors, to enable meticulous and minimally invasive tissue manipulation while concurrently mitigating tissue damage. This paper showcases simulated tissue tests and validates the mechanism on chicken liver tissue. The integration of air sensors, strain gauge sensors, and chambers furnishes real-time feedback on applied forces, tissue interaction dynamics, and alterations in tissue dimensions. The presented work lays the foundation for further advancements in the realm of minimally invasive tissue collection, crucial for precise disease detection.

Design, proof-of-concept of single robotic hair transplant mechanisms for both harvest and implant of hair grafts

The design and development of a prototype for a singular robotic hair transplant system capable of harvesting and implanting hair grafts were executed in this study. To establish a proof-of-concept for hair transplant procedures involving harvesting and implantation, a test system using a spherical phantom of the scalp was selected. The developed prototype of the robotic hair transplant system demonstrates the potential to reduce the duration that grafts remain without a blood supply, thereby minimizing hair graft damage. Additionally, the overall operation time for follicular unit extraction is comparatively shorter than that of conventional systems. Results from the robot vision tests indicate an 89.6% accuracy for hair graft detection with a 4 mm hair length phantom and 97.4% for a 2 mm hair length phantom. In the robot position control test, the root mean square error was found to be 1.268°, with a standard error of the mean of 0.203°. These outcomes suggest that the proposed system performs effectively under the conditions of a spherical phantom with a 2 mm hair length and a 5 mm distance between harvests.