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Service robots are rapidly transitioning from concept to reality, making significant strides in development. Similarly, the field of prosthetics is evolving at an impressive pace, with both areas now being highly relevant in the industry. Advancements in these fields are continually pushing the boundaries of what is possible, leading to the increasing creation of individual arm and hand prosthetics, either as standalone units or combined packages. This trend is driven by the rise of advanced collaborative robots that seamlessly integrate with human counterparts in real-world applications. This paper presents an open-source, 3D-printed robotic arm that has been assembled and programmed using two distinct approaches. The first approach involves controlling the hand via teleoperation, utilizing a camera and machine learning-based hand pose estimation. This method details the programming techniques and processes required to capture data from the camera and convert it into hardware signals. The second approach employs kinematic control using the Denavit-Hartenbergmethod to define motion and determine the position of the end effector in 3D space. Additionally, this work discusses the assembly and modifications made to the arm and hand to create a cost-effective and practical solution. Typically, implementing teleoperation requires numerous sensors and cameras to ensure smooth and successful operation. This paper explores methods enabled by artificial intelligence (AI) that reduce the need for extensive sensor arrays and equipment. It investigates how AI-generated data can be translated into tangible hardware applications across various fields. The advancements in computer vision, combined with AI capable of accurately predicting poses, have the potential to revolutionize the way we control and interact with the world around us. [ABSTRACT FROM AUTHOR]

Singularities are configurations where the number of degrees of freedom of a robot changes instantaneously. In parallel manipulators, a singularity could reduce the mobility of the end-effector or produce uncontrolled motions of the mobile platform. Thus, a singularity is a critical problem for mechanical design and model-based control. This paper presents a general sensor-based method to identify singularities in the workspace of parallel manipulators with low computational cost. The proposed experimental method identifies a singularity by measuring sudden changes in the end-effector movements and huge increments in the forces applied by the actuators. This paper uses an inertial measurement unit and a 3D tracking system for measuring the end-effector movements, and current sensors for the forces exerted by the actuators. The proposed sensor-based identification of singularities is adjusted and implemented in three different robots to validate its effectiveness and feasibility for identifying singularities. The case studies are two prototypes for educational purposes—a five-bar mechanism and an L-CaPaMan parallel robot—and a four-degree-of-freedom robot for rehabilitation purposes. The tests showcase its potential as a practical solution for singularity identification in educational and industrial robots. [ABSTRACT FROM AUTHOR]

The paper reports on flight tests at hovering of the COLIBRI robot. After a short review of the control model and the stabilization strategy, two different approaches are considered for the attitude reconstruction from the MEMS Inertial Measurement Unit (IMU): the complementary filter and the full-state dynamic observer, implemented in a specially designed flight control board. It is shown that both strategies provide adequate stabilization at hovering in spite of the strong vibration excitation resulting from the flapping of the wings. Moreover, it is shown that the residual wandering due to noise, robot imperfection, etc., can be significantly reduced by a cascade control loop based on the axial and lateral velocities reconstructed by the full-state observer. Experiments show that this approach based on onboard measurements allows for a station keeping as good as that obtained with velocities reconstructed from an external tracking system. The paper also reports endurance tests conducted with two different robot configurations; the maximum flight time observed is 4 min 30 s. [ABSTRACT FROM AUTHOR]

Considering system uncertainties (e.g., gear backlash, unmodeled dynamics, nonlinear friction and parameters perturbation) coupling disturbances weaken the motion performance of robotic systems, an observer-based finite-time prescribed performance sliding mode control with faster reaching law is proposed for robotic manipulators equipped with dual-motor joints (DMJs). In the case where the backlash information is completely unknown, the backlash is maximally eliminated using a simple but efficient dual-motor adaptive anti-backlash strategy. Thus, the design of position tracking controllers for DMJs can be simplified. Then, to deal with the influence of disturbances and residual uncertainties (excluding backlash), a novel finite-time adaptive sliding mode disturbance observer (ASMDO) is proposed to practically estimate the lumped uncertainties where their upper bounds are assumed to be unknown. Finally, a finite-time composite fast non-singular terminal sliding mode (TSM) controller, integrated with the prescribed performance principle, is proposed in this paper. To enhance the convergence rate, a novel TSM-type reaching law has been developed. The controller ensures that the tracking error is not only stabilized within a finite-time convergence rate but also adheres to a predefined maximum transient-steady-state error. The proposed scheme is implemented through simulation and experimental results, demonstrating its superior performance. [ABSTRACT FROM AUTHOR]

With the development of robotics and vision measurement technology, the use of robots with line laser scanners for 3D scanning and measurement of parts has become a mainstream trend in the field of industrial inspection. Traditional scanning and measuring robots mainly use the teach-in scanning method, which has unstable scanning quality and low scanning efficiency. In this paper, the adaptive sampling method for a free-form surface, which can realize the adaptive distribution of surface measurement points according to the curvature features of free-form surfaces, is proposed first. Then, integrated with the proposed adaptive sampling method, the automatic path planning method is proposed. This method consists of adaptive sampling, scanning attitude calculation based on a quaternion, scanning viewpoint planning based on viewable cones, and scan path generation based on bi-directional scanning. Based on the proposed automatic path planning method, the scanning and measuring robot can obtain complete 3D information of the surface to be measured with high measurement accuracy and efficiency. The performance index of the laser scanner can be fully reached. [ABSTRACT FROM AUTHOR]

Robotic legs, such as for lower-limb exoskeletons and prostheses, have bimodal operation: (1) within a task, like for walking (high speed and low force for the swing phase and low speed and higher force when the leg bears the weight of the system); (2) between tasks, like between walking and sit–stand motions. Sizing a traditional single-ratio actuation system for such extremum operations leads to oversized heavy electric motor and poor energy efficiency at low speeds. This paper explores a bimodal actuation concept where a hydrostatic transmission is dynamically reconfigured using custom motorized ball valves to suit the requirements of a robotic leg with a smaller and more efficient actuation system. First, this paper presents an analysis of the mass and efficiency advantages of the bimodal solution over a baseline solution, for three operating points: high-speed, high-force, and braking modes. Second, an experimental demonstration with a custom-built actuation system and a robotic leg test bench is presented. Control challenges regarding dynamic transition between modes are discussed and a control scheme solution is proposed and tested. The results show the following findings: (1) The actuator prototype can meet the requirements of a leg bimodal operation in terms of force, speed, and compliance while using smaller motors than a baseline solution. (2) The proposed operating principle and control schemes allow for smooth and fast mode transitions. (3) Motorized ball valves exhibit a good trade-off between size, speed, and flow restriction. (4) Motorized ball valves are a promising way to dynamically reconfigure a hydrostatic transmission while allowing energy to be dissipated. [ABSTRACT FROM AUTHOR]

Robotic manipulators play a pivotal role in modern intelligent manufacturing and unmanned production systems, often tasked with executing specific paths accurately. However, the input of the robotic manipulators is trajectory which is a path with time information. The resulting core technology is trajectory planning methods which are broadly classified into two categories: maximum velocity curve (MVC) methods and multiphase direct collocation (MPDC) methods. This paper concentrates on addressing challenges associated with the latter methods. In MPDC methods, the solving efficiency and accuracy are greatly influenced by the number of discretization nodes. When dealing with systems with complex dynamics, such as robotic manipulators, striking a balance between solving time and path discretization errors becomes crucial. We use a mesh refinement (MR) algorithm to find a suitable number of nodes under the premise of ensuring the path discretization error. So, the actual device can effectively implement the planned solutions. Nonetheless, the conventional application of the MR algorithm requires solving the original problem in each iteration; these processes are extremely time-consuming and may fail to solve when dealing with a complex dynamic system. As a result, we propose a sequential optimal trajectory planning scheme to solve the problem efficiently by dividing the original optimal control (OC) problem into two stages: path planning (PP) and trajectory planning (TP). In the PP stage, we employ a DC method based on arc length and an MR algorithm to identify key nodes along the specified path. This aims to minimize the approximation error introduced during discretization. In the TP stage, the identified key nodes serve as boundary conditions for an MPDC method based on time. This facilitates the generation of an optimal trajectory that maximizes motion performance, considering constant velocity in Cartesian space and dynamic constraints while keeping the path discretization error. Simulation and experiment are conducted with a six-axis robotic manipulator, ROCR6, and show significant potential for a wide range of applications in robotics. [ABSTRACT FROM AUTHOR]

This paper shows the dynamic modeling and design of a passivity-based controller for the RV-3SB robot. Firstly, the dynamic modeling of a Mitsubishi RV-3SB robot is conducted using Euler–Lagrange formulation in order to obtain a decoupled dynamic model, considering the actuator orientation besides the position of the analyzed robot. It is important to remark that the dynamic model of the RV-3SB robot is conducted based on kinematic model obtention, which is developed by the implementation of screw theory. Then, the passivity-based controller is obtained by separating the end effector variables and the actuator variables by making an appropriate coordinate transformation. The passivity-based controller is obtained by selecting an appropriate storage function, and by using Lyapunov theory, the passivity-based control law is obtained in order to drive the error variable, which is the difference between the measured end effector position variable and the desired end effector position variable. The passivity-based controller makes the error variable reach the origin in finite time, taking into consideration the dissipation properties of the proposed controller in order to stabilize the desired end effector position. A numerical simulation experiment is performed in order to validate the theoretical results obtained in this research. Using numerical experimentation, it is verified that the proposed control strategy is efficient and effective in driving the error variable to the origin in comparison with other modified techniques found in the literature. Finally, an appropriate discussion and conclusion of this research study are provided. [ABSTRACT FROM AUTHOR]

In this paper, a new socially assistive robot (SARs) called HBS-1.2 is presented, which uses 6-ply twisted and coiled polymer (TCP) artificial muscles in its hand to perform physical tasks. The utilization of 6-ply TCP artificial muscles in a humanoid robot hand is a pioneering advancement, offering cost effective, lightweight, and compact solution for SARs. The robot is designed to provide safer human–robot interaction (HRI) while performing physical tasks. The paper explains the procedures for fabrication and testing of the 6-ply TCP artificial muscles, along with improving the actuation response by using a Proportional-Integral-Derivative (PID) control method. Notably, the robot successfully performed a vision-based pick and place experiment, showing its potential for use in homecare and other settings to assist patients who suffer from neurological diseases like Alzheimer's disease. The study also found an optimal light intensity range between 34 to 108 lumens/m2, which ensures minimal variation in calculated distance with 95% confidence intervals for robust performance from the vison system. The findings of this study have important implications for the development of affordable and accessible robotic systems to support elderly patients with dementia, and future research should focus on further improving the use of TCP actuators in robotics. [ABSTRACT FROM AUTHOR]

Endoscopy has made a significant and noteworthy contribution to the field of medical science and technology. Nevertheless, its potential remains constrained due to the limited availability of rigid or flexible endoscopes. This paper introduces a novel hydraulic actuator based on electrorheological fluid (ERF) as a pivotal advancement in bridging the existing gap within the realm of endoscopy. Following a comprehensive introduction that briefly outlines the electrorheological effect, the subsequent section is dedicated to the elucidation of the actuator's development process. Challenges arise, particularly in terms of miniaturization and the realization of a hydraulically sealed system with integrated valve electrodes. An internal electrorheological valve system consisting of four valves that are controlled using a pulse-width modulated high voltage was suitable for position control of the antagonistic hydraulic actuators. High-precision stereolithography (SLA) printing has proven practical for manufacturing actuator components. For functional testing, a test bench was set up in which the actuator follows a setpoint through a PI control loop. The control deviation ranged from 0.6 to 1 degree, with a response time between 6 and 8 s. The experiments have demonstrated that through the use of ERF and integrated valve electrodes, a miniaturized functional actuator can be constructed. [ABSTRACT FROM AUTHOR]

This paper uses mixed methods to explore the preliminary design of control authority preferences for an Assistive Robotic Manipulator (ARM). To familiarize users with an intelligent robotic arm, we perform two kitchen task iterations: one with user-initiated software autonomy (predefined autonomous actions) and one with manual control. Then, we introduce a third scenario, enabling users to choose between manual control and system delegation throughout the task. Results showed that, while manually switching modes and controlling the arm via joystick had a higher mental workload, participants still preferred full joystick control. Thematic analysis indicates manual control offered greater freedom and sense of accomplishment. Participants reacted positively to the idea of an interactive assistive system. Users did not want to ask the system to only assist, by taking over for certain actions, but also asked for situational feedback (e.g., 'How close am I (the gripper)?', 'Is the lid centered over the jug?'). This speaks to a future assistive system that ensures the user feels like they drive the system for the entirety of the task and provides action collaboration in addition to more granular situational awareness feedback. [ABSTRACT FROM AUTHOR]

Traditional industrial robots often face challenges in achieving a perfectly polished surface on a workpiece because of their high mechanical rigidity. The active compliance force control device installed at the robotic arm's end enables high-precision contact force control between the grinding tool and the workpiece. However, the complex hysteresis nonlinearity between cylinder air pressure and output force, as well as various random disturbances during the grinding process, can affect the accuracy of the contact force and potentially impact the grinding effect of the workpiece, even causing irreversible damage to the surface of the workpiece. Given the complex random variation of cylinder output force in the actual grinding process, a rate-dependent hysteresis model based on diagonal recurrent neural network and Pradtl–Ishlinskii models named dRNN-PI is designed to compensate for the complex nonlinear hysteresis of the cylinder and calculate the desired air pressure to maintain a steady contact force on the workpiece. The proxy-based sliding mode control (PSMC) is utilized to quickly track the desired air pressure without overshooting. This paper also proves the controller's stability using the Lyapunov-based methods. Finally, the accuracy of the proposed hysteresis compensation model and the effectiveness and robustness of the PSMC are verified by experiment results. [ABSTRACT FROM AUTHOR]

Since robotic arms operating close to people are becoming increasingly common, there is a need to better understand how they can be made safe when unintended contact occurs, while still providing the required performance. Several actuators and methods for improving robot safety are studied and compared in this paper. A robotic arm moving its end effector horizontally and colliding with a person's head is simulated. The use of a conventional electric actuator (CEA), series elastic actuator (SEA), pneumatic actuator (PA) and hybrid pneumatic electric actuator (HPEA) with model-based controllers are studied. The addition of a compliant covering to the arm and the use of collision detection and reaction strategies are also studied. The simulations include sensor noise and modeling error to improve their realism. A systematic method for tuning the controllers fairly is proposed. The motion control performance and safety of the robot are quantified using root mean square error (RMSE) between the desired and actual joint angle trajectories and maximum impact force (MIF), respectively. The results show that the RMSE values are similar when the CEA, SEA, and HPEA drive the robot's first joint. Regarding safety, using the PA or HPEA with a compliant covering can reduce the MIF below the safety limit established by the International Organization for Standardization (ISO). To satisfy this ISO safety limit with the CEA and SEA, a collision detection and reaction strategy must be used in addition to the compliant covering. The influences of the compliant covering's stiffness and the detection delay are also studied. [ABSTRACT FROM AUTHOR]

After a short overview of the COLIBRI project, this paper considers the cycle-averaged flight dynamics of a flapping-wing robot near hovering, taking advantage of the weak coupling between the roll and pitch axes. The system is naturally unstable; it needs to be stabilized actively, which requires an attitude reconstruction. Due to the flapping of the wings, the system is subject to a strong periodic noise at the flapping frequency and its higher harmonics; the resulting axial forces and pitch moments are characterized from experimental data. The flapping noise propagates to the six-axis Inertial Measurement Unit (IMU) consisting of three accelerometers and three gyros. The paper is devoted to attitude reconstruction in the presence of flapping noise representative of flight conditions. Two methods are considered: (i) the complementary filter based on the hovering assumption and (ii) a full-state dynamic observer (Kalman filter). Unlike the complementary filter, the full-state dynamic observer allows the reconstruction of the axial velocity, allowing us to control the hovering without any additional sensor. A numerical simulation is conducted to assess the merit of the two methods using experimental noise data obtained with the COLIBRI robot. The paper discusses the trade-off between noise rejection and stability. [ABSTRACT FROM AUTHOR]

This paper presents a method on how to compute the position of a robotic arm in the 2D and 3D spaces. This method is slightly different from the well-known methods, such as forward or inverse kinematics. The method presented in this paper is an optical method, which uses two video cameras in stereo vision configuration to locate and compute the next move of a robotic arm in space. The method recognizes the coordinates of the markers placed at the joints of the robotic arm using the two video cameras. The coordinate points of these markers are connected with straight lines. Around certain points, circles are drawn. From the tangent to the circles, a non-Cartesian (orthogonal) coordinate system is drawn, which is enough to compute the target position of the robotic arm. All of these drawings are overlaid on the live video feed. This paper also presents another method for calculating the stereo distance using the triangulation method. An alternative method is also presented when a non-Cartesian (orthogonal) 3D coordinate system is created, which is used to compute the target position of the robotic arm in the 3D space. Because the system is in a loop, it can make micro-adjustments of the robotic arm, in order to be exactly in the desired position. In this way, there is no need to make calibrations for the robotic arm. In an industrial system, there is no need to stop the production line, which can be a really big cost saver. [ABSTRACT FROM AUTHOR]

Given the continual rise in mission diversity and environmental complexity, the adept integration of a robot's aerial and terrestrial locomotion modes to address diverse application scenarios has evolved into a formidable challenge. In this paper, we design a reconfigurable airframe robot endowed with the dual functionalities of rolling and flying. This innovative design not only ensures a lightweight structure but also incorporates morphing capabilities facilitated by a slider-crank mechanism. Subsequently, a land-to-air transformation strategy for the robot is introduced, achieved through the coordinated movement of the robotic arm and the servo motor. To ensure stable control of the robot amid external wind disturbances, we leverage the collaboration between a Generative Adversarial Network (GAN)and a Nonlinear Model Predictive Control (NMPC) controller. After the wind force magnitude is predicted through the neural network, the robot's adeptness in flexible trajectory tracking is verified. Under simulated wind conditions of 12.1 m/s, the trajectory error consistently remains within the range of 10–15 cm, affirming the effectiveness of this control method. [ABSTRACT FROM AUTHOR]

Nonskeletal animals such as worms achieve locomotion via crawling. We consider them as an inspiration to design robots that help underline the mechanisms of crawling. In this paper, we aim to identify an approach with the simplest structure and actuators. Our robots consist of cut-and-fold bodies equipped with pneumatically-driven soft actuators. We have developed fabrication techniques for coin-sized robots. Experiments showed that our robots can move up to 4.5 mm/s with straight motion (i.e., 0.1 body lengths per second) and perform cornering and U-turns. We have also studied the friction characteristics of our robots with the ground to develop a multistate model with stick–slip contact conversions. Our theoretical analyses depict comparable results to experiments demonstrating that simple and straightforward techniques can illustrate the crawling mechanism. Considering the minimal robots' structure, this result is a critical step towards developing miniature crawling robots successfully. [ABSTRACT FROM AUTHOR]

Modeling errors and external disturbances have significant impacts on the control accuracy of robotic arm trajectory tracking. To address this issue, this paper proposes a novel method, the neural network terminal sliding mode control (ALSSA-RBFTSM), which combines fast nonsingular terminal sliding mode (FNTSM) control, radial basis function (RBF) neural network, and an improved salp swarm algorithm (ALSSA). This method effectively enhances the trajectory tracking accuracy of robotic arms under the influence of uncertain factors. Firstly, the fast nonsingular terminal sliding surface is utilized to enhance the convergence speed of the system and achieve finite-time convergence. Building upon this, a novel multi-power reaching law is proposed to reduce system chattering. Secondly, the RBF neural network is utilized to estimate and compensate for modeling errors and external disturbances. Then, an improved salp swarm algorithm is proposed to optimize the parameters of the controller. Finally, the stability of the control system is demonstrated using the Lyapunov theorem. Simulation and experimental results demonstrate that the proposed ALSSA-RBFTSM algorithm exhibits superior robustness and trajectory tracking performance compared to the global fast terminal sliding mode (GFTSM) algorithm and the RBF neural network fast nonsingular terminal sliding mode (RBF-FNTSM) algorithm. [ABSTRACT FROM AUTHOR]

Traditional rigid grippers that are used for underwater systems lack flexibility and have lower degrees of freedom. These systems might damage the underwater environment while conducting data acquisition and data sampling. Soft robotics, which is mainly focused on creating robots with extremely soft materials are more delicate for the grasping of objects underwater. These systems tend to damage the underwater ecosystem in the least possible way. In this paper, we have presented a simplified design of a soft arm inspired by the octopus arm actuated by coiled Shape Memory Alloys (SMAs) using completely flexible lightweight material. The characterization arm performance under various load and input current conditions is shown. We hope this work will serve as a basis for the future of underwater grasping utilizing soft robotics. [ABSTRACT FROM AUTHOR]

This paper studies the working principles of antagonistic magneto-rheological (MR) actuators, i.e., a combination of an electric motor and a pair of MR clutches in an antagonistic configuration, for compliant actuation in robotics. The study focuses on the unique boundedness property exhibited by MR actuators, which limits the output torques delivered to the load, independent of the received input torque and/or control commands. This inherent property is of significant importance for ensuring human safety in human–robot interaction applications. Through a comprehensive analysis, we provide analytical proof of the inherent output boundedness of antagonistic MR actuators and validate our findings through experimental results. Our research demonstrates that these actuators are well-suited for safe operations in robotic applications, eliminating the need for additional sensor measurements or complex control strategies. This promising capability enables the avoidance of trade-offs between actuator performance, complexity, and cost. The insights gained from this study contribute to advancing compliant actuation technology, paving the way for high-performance and human-safe robotic systems. [ABSTRACT FROM AUTHOR]

In recent years, exterior-rotor brushless DC motors have become increasingly popular in robotics applications due to their compact shape and high torque density. However, these motors were originally used for continuous operation in drones. For applications such as exoskeletons, prostheses, or legged robots, short bursts of high power are often required. Unfortunately, vendors do not typically provide data on the motors' performance under these conditions. This paper presents experimental data on the torque–speed relationship, efficiency, and thermal responses of one of the most widely used outrunner-type brushless motors across its full operating range, including high-power short-duration operation. The results of this study can inform the selection and design of actuators for a range of robotics applications, particularly those that require high power output for brief periods of time. [ABSTRACT FROM AUTHOR]

After years of more rigid and conventional production processes, the traditional manufacturing industry has been moving toward flexible manufacturing and intelligent manufacturing in recent years. After more than half a century of development, robotics has penetrated into all aspects of human production and life, bringing significant changes to the development of human society. At the same time, the key technology of human–machine cooperative operation has become a research hotspot, and how to realize a human–machine integrated safety system has attracted attention. Human–machine integration means that humans and robots can work in the same natural space, coordinating closely and interacting naturally. To realize real human–robot integration under human–robot cooperative operation, the good judgment of intentional interaction and accidental collision and the detection of collision joints and collision points when accidental collision occurs are the key points. In this paper, we propose a method to identify the collision joints by detecting real-time current changes in each joint of the robot and solve the collision point location information of the collision joints through the principle of virtual displacement and the principle of force method using the force sensor data installed at the base of the robot as the known condition. The results show that the proposed method of identifying the collision joints using changes in joint current and then establishing a collision detection algorithm to solve the collision point location is correct and reliable. [ABSTRACT FROM AUTHOR]

Soft-robotics for biomedical applications, such as rehabilitation robots, is a field of intense research activity. Different actuation solutions have been proposed in the last decades, involving study and development of soft actuators of different types and materials. The purpose of the paper is to present procedures for an optimized design, and for easy and low cost production and characterization of monolithic PneuNets soft-actuators. An innovative design approach has been developed. The parameterization of the geometry, combined with FEM simulations is the basis for an optimized design of the actuator, as a function of the obtained bending and of the generated forces. Simple and cheap characterization setup and procedures have been identified for the actuator characterization and for simulation results validation. An easy and low-cost fabrication method based on lost wax core obtained through a silicone based mold has been developed for a monolithic PneuNets soft-actuator. The proposed solution performs well in bending, without the need for a strain limiting layer. Experimental results validated simulations, confirming the feasibility of adopting an optimized simulation-based design approach. [ABSTRACT FROM AUTHOR]

In this paper, a back-drivable and miniature rotary series elastic actuator (RSEA) is proposed for robotic adaptive grasping. A compact arc grooves design has been proposed to effectively reduce the dimension of the RSEA system. The elastic elements could be reliably embedded in the arc grooves without any additional installation structures. The whole RSEA system is characterized as compact, miniature, and modular. The actuating force is controlled via a PI controller by tracking the deformation trajectory of the elastic elements. An underactuated finger mechanism has been adopted to investigate the effectiveness of the RSEA in robotic adaptive grasping. Results reveal that the underactuated finger mechanism could achieve adaptive grasping via the RSEA in a back-drive approach without the requirement of a fingertip force sensor. The RSEA could also exhibit an actuating compliance and a self-sensing characteristic. The actuating compliance characteristic helps in in guaranteeing the safety of human–robot interaction. The RSEA could estimate the external disturbance due to its self-sensing characteristic, which has the potential to replace the fingertip force sensor in grasping force perception applications. [ABSTRACT FROM AUTHOR]

Robot dynamics model uncertainty and unpredictable external perturbations are important factors that influence control accuracy and stability. To accurately compensate for the dynamics model in sliding mode control (SMC), a new parallel network (PCR) is proposed in this paper. The network parallelizes the radial basis function and convolutional neural network, which gives it the advantage of making full use of one-dimensional data fitting results and two-dimensional data feature information, realizing the deep learning of multidimensional data and improving the model's compensation accuracy and anti-interference ability. Meanwhile, based on the integration of adaptive control techniques and gradient descent, a new weight update algorithm is designed to realize the online learning of PCR networks under loss-free functions. Then, a new sliding mode controller (PCR-SMC) is established. The model-free intelligent control of the robot is accomplished without knowledge of the predetermined upper bounds. Additionally, the stability analysis of the control system is proved by the Lyapunov theorem. Lastly, robot tracking control simulations are performed on two trajectories. The results demonstrate the high-precision tracking performance of this controller in comparison with the RBF-SMC controller. [ABSTRACT FROM AUTHOR]

An industrial linear stage is a device that is commonly used in robotics. To be precise, an industrial linear stage is an electro-mechanical system that includes a motor, electronics, flexible coupling, gear, ball screw, and precision linear bearing. A tight fit can provide better precision but also generates a difficult-to-model friction that is highly nonlinear and asymmetrical. Herein, this paper proposes an advanced trajectory controller based on a digital twin framework incorporated with artificial intelligence (AI), which can effectively control a precision linear stage. This framework offers several advantages: detection of abnormalities, estimation of performance, and selective control over any situation. The digital twin is developed via Matlab's Simscape and runs concurrently having a real-time controller. [ABSTRACT FROM AUTHOR]

Across different fields within robotics, there is a great need for lightweight, efficient actuators with human-like performance. Linkage-based passive variable transmissions and torque-sensitive transmissions have emerged as promising solutions to meet this need by significantly increasing actuator efficiency and power density, but their modeling and analysis remain an open research topic. In this paper, we introduce the sensitivity between input displacement and output torque as a key metric to analyze the performance of these complex mechanisms in dynamic tasks. We present the analytical model of sensitivity in the context of two different torque-sensitive transmission designs, and used this sensitivity metric to analyze the differences in their performance. Experiments with these designs implemented within a powered knee prosthesis were conducted, and results validated the sensitivity model as well as its role in predicting actuators' dynamic performance. Together with other design methods, sensitivity analysis is a valuable tool for designers to systematically analyze and create transmission systems capable of human-like physical behavior. [ABSTRACT FROM AUTHOR]

Because of their low cost, large workspace, and high flexibility, industrial robots have recently received significant attention in large-scale part machining. However, due to the stiffness limitations in robot joints and links, industrial robots are prone to vibration during milling processes, which leads to poor surface topography. In robotic milling processes, it remains challenging to simulate the surface topography accurately. This paper presents a mathematical model of surface topography combined with the effects of process parameters and tool vibrations in robotic milling. In this method, the kinematic trajectory of the cutting edge is first calculated by considering the cutter geometry, tool eccentricity, tool orientation, and redundancy angle. After that, the posture-dependent dynamic characteristics of the robotic milling system are predicted using an inverse distance-weighted approach. Then, a dynamic model of the robotic milling system is constructed for calculating tool vibration displacements. Finally, the kinematic model of cutting edges is modified using Z-map to incorporate the obtained vibration displacements into the sweep surfaces. In addition, milling experiments are carried out to verify the effectiveness of the proposed method, showing a good agreement between predicted and measured surface roughness. Furthermore, the findings offer valuable insights into the impact of process parameters and robot posture on surface quality. [ABSTRACT FROM AUTHOR]

This paper presents a fault tolerant scheme employing adaptive non-singular fixed-time terminal sliding mode control (AFxNTSM) for the application of robotic manipulators under uncertainties, external disturbances, and actuator faults. To begin, non-singular fixed-time terminal sliding mode control (FxNTSM) is put forth. This control method uses non-singular terminal sliding mode control to quickly reach fixed-time convergence, accomplish satisfactory performance in tracking, and produce non-singular and non-chatter control inputs. Then, without knowing the upper bounds beforehand, AFxNTSM is used as a reliable fault tolerant control (FTC) to estimate actuator faults and unknown dynamics. The fixed-time stability of the closed-loop system is established by the theory of Lyapunov analysis. The computer simulation results of the position tracking, control inputs, and adaptive parameters are presented to verify and illustrate the performance of the proposed strategy. [ABSTRACT FROM AUTHOR]

The contact force between the polishing tool and the workpiece is crucial in determining the surface quality in robotic polishing. Different from rigid end-effectors, this paper presents a novel compliant end-effector (CEE) for robotic polishing using flexible beams. The flexibility of the CEE helps to suppress the excessive displacement caused by the inertia of the polishing robot and avoids damaging the polishing tool and workpiece surface. In addition, the contact force can also be precisely estimated via the measurement of the CEE's displacement using a capacitive position sensor. The design, modeling and experimental validation of the CEE are presented. Firstly, the analytical model of the CEE is established using the stiffness matrix method. Subsequently, the analytical model is verified by finite element analysis. Further, a prototype is manufactured, and its characteristics and performance are experimentally tested. The equivalent stiffness is measured to be 0.335 N/μm, and the first natural frequency along its working direction is 42.1 Hz. Finally, the contact force measurement using the CEE is compared with a force sensor. Under open-loop condition, the resolution of the contact force measurement is found to be 0.025 N, which makes the fine tuning of the contact force possible in robotic polishing. [ABSTRACT FROM AUTHOR]

A powered transfemoral prosthesis is needed as people with transfemoral amputation show 60 percent extra metabolic cost when compared to people with no amputation. Recently, as illustrated in the literature, the most high-torque robotic knee prosthesis utilize harmonic reducers. Despite the advantage of high reduction ratio and efficiency, the harmonic drive cannot be back-driven. Therefore, the harmonic drive is not an optimal solution for prosthetic systems with direct and indirect contact with the environment. In this paper, we outline an initial design of robotic knee prosthesis. The proposed robotic knee prosthesis consists of BLDC motor, cycloidal gear with reduction ratio 13:1, four-bar mechanism, and timing belt transmission with 4:1 reduction ratio. To optimize the torque transmission and range of motion (RoM), a multiobjective optimization problem must be undertaken. The end-effector motion depends on each bar length in the four-bar mechanism. The four-bar mechanism was optimized using particle swarm optimization (PSO). To complete the optimization, a set of 50 steps was collected using wearable sensors. Then, the data of sagittal plan were processed to identify the target profile for PSO. The prototype's computer-aided manufacturing (CAM) was completed using a MarkTwo 3D printer with carbon fiber composite. The overall design can achieve a maximum torque of 84 N.m. However, the current design lacks the elastic component (no spring is added on the actuator output), which is necessary for a functional prosthesis; this limitation will be addressed in future study. [ABSTRACT FROM AUTHOR]

This paper deals with the motion trajectory tracking control problem based on output feedback and artificial neural networks for anthropomorphic manipulator robots under disturbed operating scenarios. This class of manipulator robots constitutes nonlinear dynamic systems subjected to disturbance torques induced mainly by work payload. Parametric uncertainty and possible dynamic modeling errors stand for other kind of disturbances that can deteriorate the efficiency and robustness of the tracking of controlled nonlinear robotic system trajectories. In fact, the presence of unknown dynamic disturbances is unavoidable in industrial robotic engineering systems. Therefore, for high-precision applications, such as laser cutting, marking, or welding, effective control schemes should be designed to guarantee adequate motion profile tracking planned on this class of disturbed nonlinear robotic system. In this context, a new adaptive robust motion trajectory tracking control scheme based on output feedback and artificial neural networks of anthropomorphic manipulator robots is presented. Three-layer B-spline artificial neural networks and time-series modeling are properly exploited in the design of novel adaptive robust motion tracking controllers for robotic applications of laser manufacturing. In this way, dependency on detailed nonlinear mathematical modeling of robotic systems is considerably reduced, and real-time estimation of uncertain dynamic disturbances is not required. Furthermore, several cases studies to demonstrate the motion planning tracking control robustness for a class of MIMO nonlinear robotic systems are described. blue Insights for the extension of the introduced output-feedback adaptive neural control design approach for other architecture of nonlinear robotic systems are depicted. [ABSTRACT FROM AUTHOR]

Service robots are rapidly transitioning from concept to reality, making significant strides in development. Similarly, the field of prosthetics is evolving at an impressive pace, with both areas now being highly relevant in the industry. Advancements in these fields are continually pushing the boundaries of what is possible, leading to the increasing creation of individual arm and hand prosthetics, either as standalone units or combined packages. This trend is driven by the rise of advanced collaborative robots that seamlessly integrate with human counterparts in real-world applications. This paper presents an open-source, 3D-printed robotic arm that has been assembled and programmed using two distinct approaches. The first approach involves controlling the hand via teleoperation, utilizing a camera and machine learning-based hand pose estimation. This method details the programming techniques and processes required to capture data from the camera and convert it into hardware signals. The second approach employs kinematic control using the Denavit-Hartenbergmethod to define motion and determine the position of the end effector in 3D space. Additionally, this work discusses the assembly and modifications made to the arm and hand to create a cost-effective and practical solution. Typically, implementing teleoperation requires numerous sensors and cameras to ensure smooth and successful operation. This paper explores methods enabled by artificial intelligence (AI) that reduce the need for extensive sensor arrays and equipment. It investigates how AI-generated data can be translated into tangible hardware applications across various fields. The advancements in computer vision, combined with AI capable of accurately predicting poses, have the potential to revolutionize the way we control and interact with the world around us.

Singularities are configurations where the number of degrees of freedom of a robot changes instantaneously. In parallel manipulators, a singularity could reduce the mobility of the end-effector or produce uncontrolled motions of the mobile platform. Thus, a singularity is a critical problem for mechanical design and model-based control. This paper presents a general sensor-based method to identify singularities in the workspace of parallel manipulators with low computational cost. The proposed experimental method identifies a singularity by measuring sudden changes in the end-effector movements and huge increments in the forces applied by the actuators. This paper uses an inertial measurement unit and a 3D tracking system for measuring the end-effector movements, and current sensors for the forces exerted by the actuators. The proposed sensor-based identification of singularities is adjusted and implemented in three different robots to validate its effectiveness and feasibility for identifying singularities. The case studies are two prototypes for educational purposes—a five-bar mechanism and an L-CaPaMan parallel robot—and a four-degree-of-freedom robot for rehabilitation purposes. The tests showcase its potential as a practical solution for singularity identification in educational and industrial robots.

In this paper, the implementation of a robotic painting system using a sponge and the watercolor painting technique is presented. A collection of tools for calibration and sponge support operations was designed and built. A contour-filling algorithm was developed, which defines the sponge positions and orientations in order to color the contour of a generic image. Finally, the proposed robotic system was employed to realize a painting combining etching and watercolor techniques. To the best of our knowledge, this is the first example of robotic painting that uses the watercolor technique and a sponge as the painting media. [ABSTRACT FROM AUTHOR]

In robotic manipulation, achieving efficient and reliable grasping in cluttered environments remains a significant challenge. This study presents a novel approach that integrates pushing and grasping actions using deep reinforcement learning. The proposed model employs two fully convolutional neural networks—Push-Net and Grasp-Net—that predict pixel-wise Q-values for potential pushing and grasping actions from heightmap images of the scene. The training process utilizes deep Q-learning with a reward structure that incentivizes both successful pushes and grasps, encouraging the robot to create favorable conditions for grasping through strategic pushing actions. Simulation results demonstrate that the proposed model significantly outperforms traditional grasp-only policies, achieving an 87% grasp success rate in cluttered environments, compared to 60% for grasp-only approaches. The model shows robust performance in various challenging scenarios, including well-ordered configurations and novel objects, with completion rates of up to 100% and grasp success rates as high as 95.8%. These findings highlight the model's ability to generalize to unseen objects and configurations, making it a practical solution for real-world robotic manipulation tasks. [ABSTRACT FROM AUTHOR]

Segmenting unseen object instances in unstructured environments is an important skill for robots to perform grasping-related tasks, where the trade-off between efficiency and accuracy is an urgent challenge to be solved. In this work, we propose a fast unseen object instance segmentation (Fast UOIS) method that utilizes predicted center offsets of objects to compute the positions of local maxima and minima, which are then used for selecting initial seed points required by the mean-shift clustering algorithm. This clustering algorithm that adaptively generates seed points can quickly and accurately obtain instance masks of unseen objects. Accordingly, Fast UOIS first generates pixel-wise predictions of object classes and center offsets from synthetic depth images. Then, these predictions are used by the clustering algorithm to calculate initial seed points and to find possible object instances. Finally, the depth information corresponding to the filtered instance masks is fed into the grasp generation network to generate grasp poses. Benchmark experiments show that our method can be well transferred to the real world and can quickly generate sharp and accurate instance masks. Furthermore, we demonstrate that our method is capable of segmenting instance masks of unseen objects for robotic grasping. [ABSTRACT FROM AUTHOR]

The design of a teleoperated 8-DoF redundant robot for Doppler sonography is detailed in this paper. The proposed robot is composed of a 7-DoF robotic arm mounted on a 1-DoF linear axis. This solution has been conceived to allow Doppler ultrasound examination of the entire patient's body. This paper details the design of the platform and proposes two alternative control modes to deal with its redundancy at the torque level. The first control mode considers the robot as a full 8-DoF kinematics chain, synchronizing the action of the eight joints and improving the global robot manipulability. The second control mode decouples the 7-DoF arm and the linear axis controllers and proposes a switching strategy to activate the linear axis motion when the robot arm approaches the workspace limits. Moreover, a new adaptive Joint-Limit Avoidance (JLA) strategy is proposed with the aim of exploiting the redundancy of the 7-DoF anthropomorphic arm. Unlike classical JLA approaches, a weighting matrix is actively adapted to prioritize those joints that are approaching the mechanical limits. Simulations and experimental results are presented to verify the effectiveness of the proposed control modes. [ABSTRACT FROM AUTHOR]

This study presents a novel image-based visual servoing fault-tolerant control strategy aimed at ensuring the successful completion of visual servoing tasks despite the presence of robotic arm actuator faults. Initially, a depth-independent image-based visual servoing model is established to mitigate the effects of inaccurate camera parameters and missing depth information on the system. Additionally, a robotic arm dynamic model is constructed, which simultaneously considers both multiplicative and additive actuator faults. Subsequently, model uncertainties, unknown disturbances, and coupled actuator faults are consolidated as centralized uncertainties, and an iterative learning fault observer is designed to estimate them. Based on this, suitable sliding surfaces and control laws are developed within the super-twisting sliding mode visual servo controller to rapidly reduce control deviation to near zero and circumvent the chattering phenomenon typically observed in traditional sliding mode control. Finally, through comparative simulation between different control strategies, the proposed method is shown to effectively counteract the effect of actuator faults and exhibit robust performance. [ABSTRACT FROM AUTHOR]

This paper fits into the field of research concerning robotic systems for rehabilitation. Robotic systems are going to be increasingly used to assist fragile persons and to perform rehabilitation tasks for persons affected by motion injuries. Among the recovery therapies, the mirror therapy was shown to be effective for the functional recovery of an arm after stroke. In this paper we present a master/slave robotic device based on the mirror therapy paradigm for wrist rehabilitation. The device is designed to orient the affected wrist in real time according to the imposed motion of the healthy wrist. The paper shows the kinematic analysis of the system, the numerical simulations, an experimental mechatronic set-up, and a built 3D-printed prototype. [ABSTRACT FROM AUTHOR]

Currently, the textile industry is a poorly automated sector, due in part to problems in the handling of deformable leather and textile parts during production operations. In this work, several problems in the handling process of leather and textile parts are addressed, introducing methods to increase the automation of the process. A pneumatic actuator designed to pick up textile or leather parts avoiding their deformation during transport has been developed. This actuator maximizes the number of gripping points to improve handling, making it more stable and efficient. Additionally, a vision system has been implemented in the part-picking task which, in conjunction with the CAD information of the part, sends the modified gripping position of the part to the robot. This allows customized handling of each textile or leather part. Finally, validation tests have been carried out on this development, both in simulations and in laboratory conditions, demonstrating its viability and direct applicability in the production line. [ABSTRACT FROM AUTHOR]

Through soft robotics, flexible structures confer an elevated degree of protection and safety in usage, as well as precision and reliability. Using theoretical models while combining different types of soft components opens a wide variety of possibilities for the development of new and better alternatives to rigid robots. Modeling and controlling soft robotic structures is still a challenge and is presented in different ways by the scientific community. The present scientific work aims to combine two of the most popular types of soft actuators, specifically shape memory alloy and pneumatic actuators. The purpose is to observe the interaction between individual entities and the resulting combined dynamics, highlighting the distinctive effects and influences observed in the combined system. An evaluation is conducted from a numerical simulation perspective in the MATLAB environment using representative mathematical models. The tests prove that a structure combining these particular actuators benefits from the advantages of both components and even compensates for individual downsides. [ABSTRACT FROM AUTHOR]

Autonomous tractor–trailer robots possess a broad spectrum of applications but pose significant challenges in control due to their nonlinear and underactuated dynamics. Unlike the tractor, the motion of the trailer cannot be directly actuated, which often results in a deviation from the intended path. In this study, we introduce a novel method for generating and following trajectories that circumvent obstacles, tailored for a tractor–trailer robotic system constrained by multiple factors. Firstly, leveraging the state information of both the obstacles and the desired trajectory, we formulate an improved trajectory for obstacle avoidance using the nonlinear least squares method. Subsequently, we propose an innovative tracking controller that integrates a universal barrier function with a state transformation strategy. This amalgamation facilitates the accurate tracking of the prescribed trajectory. Our theoretical analysis substantiates that the proposed control methodology ensures exponential convergence of the line-of-sight (LOS) distance and angle tracking errors, while enhancing the transient performance. To validate the efficacy of our approach, we present a series of simulation results, which demonstrate the applicability of the developed control strategy in managing the complex dynamics of tractor–trailer robots. [ABSTRACT FROM AUTHOR]

Laminar jamming (LJ) is a method to achieve variable stiffness in robotics that has attracted notable attention because of its simple working principle and potential high stiffness variation. This article reviews the lock/unlock mechanisms of LJ structures. The application of these mechanisms in robotics is discussed, including grippers, continuum robots, wearable robots, robot arms, and more. Furthermore, the performance and limitations of the mechanisms to vary the stiffness of LJ are qualitatively and quantitatively analyzed. This performance analysis focuses mainly on the potential of LJ mechanisms to be applied in robot arms with variable stiffness and their potential to attenuate the impact between human beings and robot arms. The modeling of LJ through analytical and finite element methods is described, and their evolution towards design methodologies is discussed. To conclude, the directions and recommendations that should be followed in research on LJ are discussed. These include the improvement of existing lock/unlock mechanisms, the development of new lock/unlock mechanisms, and the development of more control algorithms for robot arms that incorporate LJ structures. [ABSTRACT FROM AUTHOR]

Robotic ultrasound scanning has excellent potential to reduce physician workload, obtain higher-quality imaging, and reduce costs. However, the traditional admittance control strategy for robotics cannot meet the high-precision force control requirements for robots, which are critical for improving image quality and ensuring patient safety. In this study, an integral adaptive admittance control strategy is proposed for contact force control between an ultrasound probe and human skin to enhance the accuracy of force tracking. First, a robotic ultrasound scanning system is proposed, and the system's overall workflow is introduced. Second, an adaptive admittance control strategy is designed to estimate the uncertain environmental information online, and the estimated parameters are used to modify the reference trajectory. On the basis of ensuring the stability of the system, an integral controller is then introduced to improve the steady-state response. Subsequently, the stability of the proposed strategy is analysed. In addition, a gravity compensation process is proposed to obtain the actual contact force. Finally, through a simulation analysis, the effectiveness of the strategy is discussed. Simultaneously, a series of experiments are carried out on the robotic ultrasound scanning system, and the results show that the strategy can successfully maintain a constant contact force under soft uncertain environments, which effectively improves the efficiency of scanning. [ABSTRACT FROM AUTHOR]

This paper analyzes the lift-production system in hovering of the flapping wing robot COLIBRI of the size of a hummingbird. The paper first examines the flapping wing mechanism for which a new gear transmission is proposed to reduce the friction and facilitate the assembly. Next, a sensitivity analysis is performed on the wing size. Then, the paper discusses several options for the gearbox, various DC motors and two battery configurations (a single battery or two batteries in series) to minimize the heat generation and increase the flight time. The configuration involving two batteries has been found more effective. The flight time is predicted using Shepherd's discharge model and it is confirmed by an experiment. The robot sustains an endurance of nearly 5 min to produce a lift force equal to the weight of the robot. [ABSTRACT FROM AUTHOR]

In the quest for intelligent robots, it is essential to enable them to understand tasks beyond mere manipulation. Achieving this requires a robust parsing mode that can be used to understand human cognition and semantics. However, the existing methods for task and motion planning lack generalization and interpretability, while robotic knowledge bases primarily focus on static manipulation objects, neglecting the dynamic tasks and skills. To address these limitations, we present a knowledge-based framework for hierarchically understanding various factors and knowledge types in robotic manipulation. Using this framework as a foundation, we collect a knowledge graph dataset describing manipulation tasks from text datasets and an external knowledge base with the assistance of large language models and construct the knowledge base. The reasoning tasks of entity alignment and link prediction are accomplished using a graph embedding method. A robot in real-world environments can infer new task execution plans based on experience and knowledge, thereby achieving manipulation skill transfer. [ABSTRACT FROM AUTHOR]

A rigid in-plane matrix of suction cups is widely used in robotic end-effectors to grasp objects with flat surfaces. However, this grasping strategy fails with objects having different geometry e.g., spherical and cylindrical. Articulated rigid grippers equipped with suction cups are an underinvestigated solution to extend the ability of vacuum grippers to grasp heavy objects with various shapes. This paper extends previous work by the authors in the development of a novel underactuated vacuum gripper named Polypus by analyzing the impact of dynamic effects and grasping location on the vacuum force required during a manipulation cycle. An articulated gripper with suction cups, such as Polypus, can grasp objects by adhering to two adjacent faces, resulting in a decrease of the required suction action. Moreover, in the case of irregular objects, many possible grasping locations exist. The model explained in this work contributes to the choice of the most convenient grasping location that ensures the minimum vacuum force required to manipulate the object. Results obtained from an extensive set of simulations are included to support the validity of the proposed analytical approach. [ABSTRACT FROM AUTHOR]

Robotic manipulators play a pivotal role in modern intelligent manufacturing and unmanned production systems, often tasked with executing specific paths accurately. However, the input of the robotic manipulators is trajectory which is a path with time information. The resulting core technology is trajectory planning methods which are broadly classified into two categories: maximum velocity curve (MVC) methods and multiphase direct collocation (MPDC) methods. This paper concentrates on addressing challenges associated with the latter methods. In MPDC methods, the solving efficiency and accuracy are greatly influenced by the number of discretization nodes. When dealing with systems with complex dynamics, such as robotic manipulators, striking a balance between solving time and path discretization errors becomes crucial. We use a mesh refinement (MR) algorithm to find a suitable number of nodes under the premise of ensuring the path discretization error. So, the actual device can effectively implement the planned solutions. Nonetheless, the conventional application of the MR algorithm requires solving the original problem in each iteration; these processes are extremely time-consuming and may fail to solve when dealing with a complex dynamic system. As a result, we propose a sequential optimal trajectory planning scheme to solve the problem efficiently by dividing the original optimal control (OC) problem into two stages: path planning (PP) and trajectory planning (TP). In the PP stage, we employ a DC method based on arc length and an MR algorithm to identify key nodes along the specified path. This aims to minimize the approximation error introduced during discretization. In the TP stage, the identified key nodes serve as boundary conditions for an MPDC method based on time. This facilitates the generation of an optimal trajectory that maximizes motion performance, considering constant velocity in Cartesian space and dynamic constraints while keeping the path discretization error. Simulation and experiment are conducted with a six-axis robotic manipulator, ROCR6, and show significant potential for a wide range of applications in robotics.

As a critical component of the sugarcane harvester, the primary function of the crop dividers is to lift the lodged sugarcane (LS) and reduce the loss rate of the sugarcane harvest. In this study, a rigid-flexible coupling simulation method is proposed to improve the lifting efficiency of the crop dividers on severely LS and analyze the nature of interaction between the sugarcane stalk and the crop dividers. The model's accuracy was verified using field experiments, and the operational performance of the crop dividers on sugarcane in different lodging postures was investigated. The results showed that the curve of the vertical height of the center (VHC) fluctuated more and slipped with highest frequency during the lifting process of side and forward LS. The speed of VHC was fastest during the lifting operation of side LS. The effect of side angle on the lifting effect of sugarcane was significant; the qualified values of the VHC of sugarcane being lifted in different lodged postures were: side and reverse lodged > side lodged > side and forward lodged. The coupling method and experimental results described in this paper can provide guidance for the optimal design and field operation of the crop dividers. [ABSTRACT FROM AUTHOR]