The lack of specialized personnel and assistive technology to assist in rehabilitation therapies is one of the challenges facing the health sector today, and it is projected to increase. For researchers and engineers, it represents an opportunity to innovate and develop devices that improve and optimize rehabilitation services for the benefit of society. Among the different types of injuries, hand injuries occur most frequently. These injuries require a rehabilitation process in order for the hand to regain its functionality. This article presents the fabrication and instrumentation of an end-effector prototype, based on a five-bar configuration, for finger rehabilitation that executes a natural flexion-extension movement. The dimensions were obtained through the gradient method optimization and evaluated through Matlab. Experimental tests were carried out to demonstrate the prototype’s functionality and the effectiveness of a five-bar mechanism acting in a vertical plane, where gravity influences the mechanism’s performance. Position control using fifth-order polynomials with via points was implemented in the joint space. The design of the end-effector was also evaluated by performing a theoretical comparison, calculated as a function of a real flexion-extension trajectory of the fingers and the angle of rotation obtained through an IMU. As a result, controlling the two degrees of freedom of the mechanism at several points of the trajectory assures the end-effector trajectory and therefore the fingers’ range of motion, which helps for full patient recovery.

With the progress of industry, people are facing more and more complicated tasks, which cannot be completed by conventional rigid robot. For this, a deployable robot based on spherical linkage parallel mechanism was proposed to satisfy relevant requirements for the degrees of freedom in this study. Based on the design of robot model and its control box, a mathematical model for the robot was established, and the relationship between motion space and drive space was deduced accordingly. Subsequently, a control system consisting of the upper and lower computers was introduced. Two control modes, that is, Joystick control and remote control, were developed. The upper computer control interface for the robot was completed by MATLAB construction. At last, the two control modes as well as autonomous detection were demonstrated by motion test. This achievement will further advance the applications of deployable robot in job aid and intelligent exploration.

This paper proposes a tracking controller for the formation construction of multiple autonomous surface vessels (ASVs) in the presence of model uncertainties and external disturbances with output constraints. To design a formation control system, the leader-following strategy is adopted for each ASV. A symmetric barrier Lyapunov function (BLF), which advances to infinity when its arguments reach a finite limit, is applied to prevent the state variables from violating constraints. An adaptive-neural technique is employed to compensate uncertain parameters and unmodeled dynamics. To overcome the explosion of differentiation term problem, a first-order filter is proposed to realize the derivative of virtual variables in the dynamic surface control (DSC). To estimate the leader velocity in finite time, a high-gain observer is effectively employed. This approach is adopted to reveal all signals of the closed-loop system which are bounded, and the formation tracking errors are semi-globally finite-time uniformly bounded. The computer simulation results demonstrate the efficacy of this newly proposed formation controller for the autonomous surface vessels.

In recent years, deep learning-based robotic grasping methods have surpassed analytical methods in grasping performance. Despite the results obtained, most of these methods use only planar grasps due to the high computational cost found in 6D grasps. However, planar grasps have spatial limitations that prevent their applicability in complex environments, such as grasping manufactured objects inside 3D printers. Furthermore, some robotic grasping techniques only generate one feasible grasp per object. However, it is necessary to obtain multiple possible grasps per object because not every grasp generated is kinematically feasible for the robot manipulator or does not collide with other close obstacles. Therefore, a new grasping pipeline is proposed to yield 6D grasps and select a specific object in the environment, preventing collisions with obstacles nearby. The grasping trials are performed in an additive manufacturing unit that has a considerable level of complexity due to the high chance of collision. The experimental results prove that it is possible to achieve a considerable success rate in grasping additive manufactured objects. The UR5 robot arm, Intel Realsense D435 camera, and Robotiq 2F-140 gripper are used to validate the proposed method in real experiments.

With the excellent characteristic of intrinsic compliance, pneumatic artificial muscle can improve the interaction comfort of wearable robotic devices. This paper resolves the safety tracking control problem of a pneumatically actuated lower limb exoskeleton system. A single-parameter adaptive fuzzy control strategy is proposed with high control precision and full state constraints for the safe gait training tasks. Based on the barrier Lyapunov function, all signals in the closed-loop system can be bounded in finite time, which guarantees the deviation of the exoskeleton’s moving trajectory within a bounded range. Furthermore, with the proposed single-parameter adaptive law, the computational burden and the complexity of the control are reduced significantly. Finally, numerical simulations, no-load tracking experiments, and passive and active gait training experiments with healthy subjects validate the effectiveness of the proposed method.

The time-optimal path following (OPF) problem is to find a time evolution along a prescribed path in task space with shortest time duration. Numerical solution algorithms rely on an algorithm-specific (usually equidistant) sampling of the path parameter. This does not account for the dynamics in joint space, that is, the actual motion of the robot, however. Moreover, a well-known problem is that large joint velocities are obtained when approaching singularities, even for slow task space motions. This can be avoided by a sampling in joint space, where the path parameter is replaced by the arc length. Such discretization in task space leads to an adaptive refinement according to the nonlinear forward kinematics and guarantees bounded joint velocities. The adaptive refinement is also beneficial for the numerical solution of the problem. It is shown that this yields trajectories with improved continuity compared to an equidistant sampling. The OPF is reformulated as a second-order cone programming and solved numerically. The approach is demonstrated for a 6-DOF industrial robot following various paths in task space.

Admittance control of the robot is an important method to improve human–robot collaborative performance. However, it displays poor matching between admittance parameters and human–robot collaborative motion. This results in poor motion performance when the robot interacts with the changeable environment (human). Therefore, to improve the performance of human–robot collaboration, the human-like variable admittance parameter regulator (HVAPR) based on the change rate of interaction force is proposed by studying the human arm’s static and dynamic admittance parameters in human–human collaborative motion. HVAPR can generate admittance parameters matching with human collaborative motion. To test the performance of the proposed HVAPR, the human–robot collaborative motion experiment based on HVAPR is designed and compared with the variable admittance parameter regulator (VAPR). The satisfaction, recognition ratio, and recognition confidence of the two admittance parameter regulators are statistically analyzed via questionnaire. Simultaneously, the trajectory and interaction force of the robot are analyzed, and the performance of the human–robot collaborative motion is assessed and compared using the trajectory smoothness index and average energy index. The results show that HVAPR is superior to VAPR in human–robot collaborative satisfaction, robot trajectory smoothness, and average energy consumption.

In this paper, a new over-constrained parallel driving mechanism (PDM) with planar sub-closed chains is proposed. First, the number of over-constraints on the PDM is calculated. Then, an analysis is conducted as to the kinematics of the hybrid manipulator, including positions, velocities, and accelerations of all bodies. Furthermore, the Newton–Euler approach is taken to deduce the kinematic formula of each link and the formula of inertial force at the center of mass. However, it remains difficult to solve the equation since the number of equations is smaller than that of unknown variables. To solve this problem, the screw theory is applied in the present study to analyze the cause of over-constraints, with the link’s elastic deformation introduced as the supplement of deformation compatibility equations. Moreover, the actuation forces and constrained forces/moments are calculated simultaneously. Finally, the dynamic model is verified through simulation and experimentation. The proposed modeling approach provides a fundamental basis for the structural optimization and friction force computation of the over-constrained PDM.

For greater autonomy of visual control-based solutions, especially applied to mobile robots, it is necessary to consider the existence of unevenness in the navigation surface, an intrinsic characteristic of several real applications. In general, depth information is essential for navigating three-dimensional environments and for the consistent parameter calibration of the visual models. This work proposes a new solution, including depth information in the visual path-following (VPF) problem, which allows the variation of the perception horizon at runtime while forcing the coupling between optical and geometric quantities. A new NMPC (nonlinear model predictive control) framework considering the addition of a new input to an original solution for the constrained VPF-NMPC allows the maintenance of low computational complexity. Experimental results in an outdoor environment with a medium-sized commercial robot demonstrate the correctness of the proposal.

Stereo vision allows machines to perceive their surroundings, with plane identification serving as a crucial aspect of perception. The accuracy of identification constrains the applicability of stereo systems. Some stereo vision cameras are cost-effective, compact, and user-friendly, resulting in widespread use in engineering applications. However, identification errors limit their effectiveness in quantitative scenarios. While certain calibration methods enhance identification accuracy using camera distortion models, they rely on specific models tailored to a camera’s unique structure. This article presents a calibration method that is not dependent on any particular distortion model, capable of correcting plane position and orientation identified by any algorithm, provided that the identification error is biased. A high-precision mechanical calibration platform is designed to acquire accurate calibration data while using the same detected material in real measurement scenarios. Experimental comparisons confirm the efficacy of plane pose correction on PCL-RANSAC, with the average relative error of distance reduced by 5.4 times and the average absolute error of angle decreasing by 41.2%.

Modern rehabilitation processes for neurological patients have been widely assisted by robotic structures, with continuous research and improvements. The use of robotic assistance in rehabilitation is a consolidated technique for upper limb training sessions. However, human gait robotic rehabilitation still needs further research and development. Based on that, this paper deals with the development of a novel active body weight support (BWS) system integrated with a serious game for poststroke patients. This paper starts with a brief review of the state of the art of applied technologies for gait rehabilitation. Next, it presents the obtained mathematical model followed by multibody synthesis techniques and meta-heuristic optimization to the proposed device. The control of the structure is designed using proportional integral derivative (PID) controllers tuned with meta-heuristic optimization and associated with a suppression function to perform assist-as-needed actions. Then, the prototype is integrated with a serious game designed specifically for this application. Finally, a pilot study is conducted with the structure and healthy volunteers. The results obtained show that the mobility of the novel BWS is as expected and the proposed system potentially offers a novel tool for gait training.

Human activity recognition (HAR) is an emerging challenge among researchers. HAR has many possible uses in various fields, including healthcare, sports, and security. Furthermore, there are only a few publicly accessible datasets for classifying and recognizing physical activity in the literature, and these datasets comprise fewer activities. We created and compared our dataset with available datasets, that is, NTU-RGBD, UP-FALL, UR-Fall, WISDM, and UCI HAR. The proposed dataset consists of seven activities: eating, exercise, handshake, situps, vomiting, headache, and walking. The activities were collected from 20 people between the ages of 25 and 40 years using Kinect V2 sensor at 30 FPS. For classification, we use deep learning architectures based on convolutional neural network (CNN) and long short-term memory (LSTM). Additionally, we developed a novel hybrid deep learning model by combining a CNN, a bidirectional LSTM unit, and a fully connected layer for activity identification. The suggested model builds unique guided features using the preprocessed skeleton coordinates and their distinctive geometrical and kinematic aspects. Results from the experiment are contrasted with the performance of stand-alone CNNs, LSTMs, and ConvLSTM. The proposed model’s accuracy of 99.5% surpasses that of CNN, LSTM, and ConvLSTM, which have accuracy rates of 95.76%, 97%, and 98.89%, respectively. Our proposed technique is invariant of stance, speed, individual, clothes, etc. The proposed dataset sample is accessible to the general public.

Collision avoidance is critical in multirobot systems. Most of the current methods for collision avoidance either require high computation costs (e.g., velocity obstacles and mathematical optimization) or cannot always provide safety guarantees (e.g., learning-based methods). Moreover, they cannot deal with uncertain sensing data and linguistic requirements (e.g., the speed of a robot should not be large when it is near to other robots). Hence, to guarantee real-time collision avoidance and deal with linguistic requirements, a distributed and hybrid motion planning method, named Fuzzy-VO, is proposed for multirobot systems. It contains two basic components: fuzzy rules, which can deal with linguistic requirements and compute motion efficiently, and velocity obstacles (VOs), which can generate collision-free motion effectively. The Fuzzy-VO applies an intruder selection method to mitigate the exponential increase of the number of fuzzy rules. In detail, at any time instant, a robot checks the robots that it may collide with and retrieves the most dangerous robot in each sector based on the predicted collision time; then, the robot generates its velocity in real-time via fuzzy inference and VO-based fine-tuning. At each time instant, a robot only needs to retrieve its neighbors’ current positions and velocities, so the method is fully distributed. Extensive simulations with a different number of robots are carried out to compare the performance of Fuzzy-VO with the conventional fuzzy rule method and the VO-based method from different aspects. The results show that: Compared with the conventional fuzzy rule method, the average success rate of the proposed method can be increased by 306.5%; compared with the VO-based method, the average one-step decision time is reduced by 740.9%.

Robots and inertial measurement units (IMUs) are typically calibrated independently. IMUs are placed in purpose-built, expensive automated test rigs. Robot poses are typically measured using highly accurate (and thus expensive) tracking systems. In this paper, we present a quick, easy, and inexpensive new approach to calibrate both simultaneously, simply by attaching the IMU anywhere on the robot’s end-effector and moving the robot continuously through space. Our approach provides a fast and inexpensive alternative to both robot and IMU calibration, without any external measurement systems. We accomplish this using continuous-time batch estimation, providing statistically optimal solutions. Under Gaussian assumptions, we show that this becomes a nonlinear least-squares problem and analyze the structure of the associated Jacobian. Our methods are validated both numerically and experimentally and compared to standard individual robot and IMU calibration methods.

Robots with human-like appearances and structures are usually well accepted in the human–robot interaction. However, compared with human-like appearances and structures, the human-like motion plays a much more critical role in improving the efficiency and safety of the human–robot interaction. This paper develops a human-like motion planner based on human arm motion patterns (HAMPs) to fulfill the human–robot object handover tasks. First, a handover task is divided into two sub-tasks, that is, pick-up and delivery, and HAMPs are extracted for these two sub-tasks separately. The resulting HAMPs are analyzed, and a method is proposed to select HAMPs that can represent the characteristics of the human arm motion. Then the factors affecting the duration of the movement primitives are analyzed, and the relationship between the duration of the movement primitives and these factors is determined. Based on the selected HAMP and the computed duration of the movement primitives, a human-like motion planning framework is developed to generate the human-like motion for the robotic arms. Finally, this motion planner is verified by the human–robot handover experiments using a KUKA IIWA robot. It shows that the resulting trajectories can correctly reflect the relative relationship between the joints in the human arm motion and are very close to the recorded human arm trajectories. Furthermore, the proposed motion planning method is compared with the motion planning method based on minimum total potential energy. The results show that the proposed method can generate more human-like motion.

This paper presents a novel cable-driven parallel robot with the aim of rehabilitating/exercising young and disabled children in drawing and writing tasks. The pyramidal topology was identified as providing the required three active translational Degrees of Freedom with a redundant set of five cables in a compact portable shape. In addition, this robot was designed with new features, as it is inexpensive, easy to control, easy to move to any place at home or school as it is fitting a home desk or classroom table. In this paper, we present the main steps for designing the proposed cable-driven pyramidal parallel robot. Specific attention is addressed to the design of the structure and the end effector as well as to establishing proper simulation models. Several simulations are proposed by implementing both kinematic and dynamic models to demonstrate the feasibility of multiple writing/drawing tasks. A specific user interface is proposed allowing both continuous and intermittent trajectories. A sliding mode control is implemented to achieve suitable tracking accuracy on a desired path. Finally, an experimental validation is successfully carried out by considering multiple trajectories for demonstrating the engineering feasibility and effectiveness of the proposed design and simulation models.

Thanks to recent developments in service robotics technologies, precision agriculture (PA) is becoming an increasingly prominent research field, and several studies were made to present and outline how the use of mobile robotic systems can help and improve farm production. In this paper, the integration of a custom-designed mobile base with a commercial robotic arm is presented, showing the functionality and features of the overall system for crop monitoring and sampling. To this aim, the motion planning problem is addressed, developing a tailored algorithm based on the so-called manipulability index, that treats the base and robotic arm mobility as two independent degrees of motion; also developing an open source closed-form inverse kinematics algorithm for the kinematically redundant manipulator. The presented methods and sub-system, even though strictly related to a specific mobile manipulator system, can be adapted not only to PA applications where a mobile manipulator is involved but also to the wider field of assistive robotics.

Industrial robots are widely used in the painting industry, such as automobile manufacturing and solid wood furniture industry. An important problem is how to improve the efficiency of robot programming, especially in the current furniture industry with multiple products, small batches and increasingly high demand for customization. In this work, we propose an outer loop adaptive control scheme, which allow users to realize the practical application of the zero-moment lead-through teaching method based on dynamic model without opening the inner torque control interface of robots. In order to accurately estimate the influence of joint friction, a friction model is established based on static, Coulomb and viscous friction characteristics, and the Sigmoid function is used to represent the transition between motion states. An identification method is used to quickly identify the dynamic parameters of the robot. The joint position/speed command of the robot’s inner joint servo loop is dynamically generated based on the user-designed adaptive control law. In addition, the zero-moment lead-through teaching scheme based on the dynamic model is applied to a spray-painting robot with closed control system. In order to verify our method, CMA GR630ST is used to conduct experiments. We identified the parameters of the dynamic model and carried out the zero-moment lead-through teaching experiment to track the target trajectory. The results show that the proposed method can realize the application of modern control methods in industrial robot with closed control systems, and achieve a preliminary exploration to improve the application scenarios of spray-painting robots.

An accurate dynamic model of a robot is fundamentally important for a control system, while uncertainties residing in the model are inevitable in a physical robot system. The uncertainties can be categorized as internal disturbances and external disturbances in general. The former may include dynamic model errors and joint frictions, while the latter may include external payloads or human-exerted force to the robot. Disturbance observer is an important technique to estimate and compensate for the uncertainties of the dynamic model. Different types of disturbance observers have been developed to estimate the lumped uncertainties so far. In this paper, we conducted a brief survey on five typical types of observers from a perspective of practical implementation in a robot control system, including generalized momentum observer (GMO), joint velocity observer (JVOB), nonlinear disturbance observer (NDOB), disturbance Kalman filter (DKF), and extended state observer (ESO). First, we introduced the basics of each observer including equations and derivations. Two common types of disturbances are considered as two scenarios, that is, constant external disturbance and time-varying external disturbance. Then, the observers are separately implemented in each of the two simulated scenarios, and the disturbance tracking performance of each observer is presented while their performance in the same scenario has also been compared in the same figure. Finally, the main features and possible behaviors of each type of observer are summarized and discussed. This survey is devoted to helping readers learn the basic expressions of five typical observers and implement them in a robot control system.

Flexible cables in cable-driven parallel robots (CDPRs) are easy to be excited and vibrate. Cable vibration will react on the end-effector, causing attitude deviation of the end-effector. The main objective of this study is to accurately model axially moving flexible cables and characterize the dynamic behaviors of associated compliant CDPRs. Firstly, a model for transverse vibration of the axially moving length-variable cable is developed. On this basis, an original nonlinear dynamic model of the CDPRs able to capture the vibration of the cables and the dynamics of the end-effector is proposed. Secondly, the frequency–amplitude relationship of the CDPR is obtained. Moreover, the significance of the excitation effect caused by the axially moving length-variable cables is demonstrated, by comparing the results with and without excitation effect at different frequencies. It turns out that, as the oscillation frequency of the end-effector increases, the end-effector and cables exhibit the dynamics process from steady state to unstable large-amplitude vibration and finally to stable small-amplitude vibration. This indicates that the dynamics of the CDPR exhibit non-linear characteristics, due to the influence of flexible cables. Finally, the proposed dynamic model of compliant CDPRs is validated by experiments performed in the laboratory.