This paper demonstrates that “completely-jigless” assembly of a model product that requires fitting accuracy at the level of industrial products is possible by using a universal hand with four parallel stick fingers mounted on a conventional position-control-based industrial robot. Assuming that each part is taken out of the parts bin and temporarily placed on the work table, the accuracy required for precise fitting cannot be achieved with a vision sensor alone. Introducing an appropriate grasping strategy, the initial position error of the part is absorbed by self-alignment in the process of grasping. Once the alignment is completed, the pose of the grasped part is fixed and jigless assembly is possible with a conventional industrial robot, which has high repeatability. In this paper, we use a gear unit as an example of an industrial product and present some grasping strategies with the universal hand. We also propose some subsequent assembly strategies for shafts and gears. Using those grasping and assembly strategies, it is shown that jigless assembly of the gear unit was successfully completed in the experiment. Although the target product in this paper is specific, the assembly elements in this product, such as shaft screwing, bearing insertion, and gear meshing, are also included in many other products. Therefore, the methods shown in this paper can be applied to other products.

This paper presents a feedforward compensation approach for musculoskeletal systems (MSs). Compared with traditional rigid robots, human arms have the advantages of flexibility and safety in operation in unstructured environments. However, the influence of external unknown disturbances, inner friction effects, and dynamic uncertainties of the MS makes it difficult to model and practically apply. In order to reduce the inner friction effects of the hardware platform and the over-relaxation/tension of the cable-pull drive, a feedforward friction compensation method for the cable-pulled artificial muscle unit is proposed. The method analyzes the friction causes of the hardware structure and establishes a mapping network relationship between the joint variables and the muscle force error in the muscle space. The experimental results show that the method can effectively improve the control accuracy and reduce the artificial muscle over-relaxation/tension instability.

Underwater vehicles are rich systems with attractive and challenging properties such as nonlinearities, external disturbances, and underactuated dynamics. These make the design of an advanced and robust controller quite a challenging task. This paper focuses on designing a model-free high-order sliding mode controller in a six-degree-of-freedom trajectory tracking task. The purpose of the control is accurate trajectory tracking and considerably reducing the chattering phenomenon in situations where the remotely operated vehicle (ROV) works in the presence of external disturbances, measurement errors, and actuator dynamics and nonlinearity, which is not seen in previous research. To demonstrate the stability of the closed-loop system, the Lyapunov theory is employed to ensure the asymptotic stability of tracking errors. A linear Kalman filter for estimating measurement errors is proposed to be used to correct positioning system outputs (speed, position, and attitude). In a hardware-in-the-loop test, the proposed controller for the ROV is tested in a real-time application, considering external disturbances, measurement errors, and actual thrusters. In addition, comparing the outcomes with the performance of the PID controller and the supper twisting controller shows the superiority of the proposed controller. Due to the existence of the measurement noise, spectrum analysis is also performed.

Rehabilitation robots help the treatment of diseases by performing cyclic exercises for a long period of time. These exercises must perform movements of the patient’s limbs; thus, the robots are required to be flexible and safe. Among rehabilitation robots, cable robots are widely used due to their unique properties, such as being lightweight and the possibility of being equipped with magnetic hooks to improve both safety and ease of use. However, the elasticity and flexibility of cables result in vibrations of the payload and hooks. In this paper, the forced vibrations due to rehabilitation exercises are studied. Since the previous studies of the authors showed a weak coupling between longitudinal and transverse vibrations, a two-cable planar model for the study of transverse vibrations is developed. The model makes it possible to study the forced transverse vibrations due to both cable motion and robot motion. Stiffness and damping of the patient’s arm are considered. Results show that the cable system exhibits a simple linear behavior when excited by robot motion and a non-linear behavior when excited by cable motion.

This paper studies the synchronization control of the blanket remote maintenance robot (BRMR) of the China fusion engineering test reactor (CFETR). First, the general state space mathematical model of BRMR was established by using a physical-based method. Second, based on the receding horizon optimization of model predictive control (MPC) and cross-coupling error reduction in cross-coupling control (CCC), the innovative MPC-CCC controller was proposed to realize the single-system and multisystem error convergence and high accuracy transportation of blanket through the high accuracy synchronization control of BRMR. Third, to verify the control effectiveness of the MPC-CCC controller, two types of simulations and experiments were implied compared with the original proportional-integral (PI) controller in Mover. Results showed that simulation and experiments were highly consistent. It is found that the use of an MPC-CCC controller can result in up to a 70% reduction in displacement error and up to a 59% reduction in synchronization error compared to the PI controller. And the accuracy of the MPC-CCC controller satisfies the real requirement of the maintenance process of the blanket. This work provides the theoretical basis and practical experience for the highly stable, safe, and efficient maintenance of blankets in the future.

Currently, most of the robot path planning methods for spray painting are based on slicing the Computer-Aided Design model of the target surface to generate a series of path points. They usually neglect the geometric boundary of the model and the smoothness of the generated path, leading to a non-uniform coating thickness. To ameliorate it, an improved “boundary fitting approach” is proposed. In this method, the upper and lower boundaries of the target surface are firstly detected based on the topology of the stereolithography model. For each pass, several sample points are extracted with a uniform length interval from the intersection points generated by the basic slicing method. The path pass is then described by a fourth-order polynomial curve. It fits the boundary points and sample points for the $z$ - $t$ and $y$ - $t$ relationships, respectively. Based on the spray gun’s motion direction and spray direction, the orientation of each path point is also defined. The parameters of the path pass are optimized by particle swarm optimization to get the optimal uniformity of the resulting coating thickness. Both of the global uniformity and the local uniformity between two adjacent passes are considered. The strength of the proposed approach is validated by comparing the simulation with the basic and other typical algorithms. The results denote that boundary fitting approach could improve the uniformity of coating thickness. It brings about a better performance for the painted workpiece.

A welding path can be planned effectively for spot welding robots using the ant colony algorithm, but the initial parameters of the ant colony algorithm are usually selected through human experience, resulting in an unreasonable planned path. This paper combines the ant colony algorithm with the particle swarm algorithm and uses the particle swarm algorithm to train the initial parameters of the ant colony algorithm to plan an optimal path. Firstly, a mathematical model for spot welding path planning is established using the ant colony algorithm. Then, the particle swarm algorithm is introduced into the ant colony algorithm to find the optimal combination of parameters by treating the initial parameters $\alpha$ and $\beta$ of the ant colony algorithm and as two-dimensional coordinates in the particle swarm algorithm. Finally, the simulation analysis was carried out using MATLAB to obtain the paths of the improved ant colony algorithm for six different sets of parameters with an average path length of 10,357.7509 mm, but the average path length obtained by conventional algorithm was 10,830.8394 mm. Convergence analysis of the improved ant colony algorithm showed that the average number of iterations was 17. Therefore, the improved ant colony algorithm has higher solution quality and converges faster.

A novel couple-constrained parallel wrist with three measuring force flexible fingers is designed for grabbing heavy objects and measuring grabbed forces. Its prototype is developed, its dynamics model is established, and its grabbing forces are measured. First, using the extended formulas of the skew-symmetric matrix, the kinematic formulas are derived for solving the Jacobian/Hessian matrices and the general velocity/acceleration of the moving links in the couple-constrained parallel wrist. Second, a dynamics model is established for solving the dynamic actuation forces, the couple-constrained forces, and the torque in the couple-constrained parallel wrist. Third, the theoretical solutions of the kinematics/dynamics of the couple-constrained parallel wrist are verified using a simulation mechanism. Finally, the grabbing forces of the three flexible fingers are measured and analyzed.

Earth-contact mechanism (ECM), a type of mechanism to keep the system in contact with the earth and to move with the terrain changes. This paper uses the virtual equivalent parallel mechanism (VEPM) to convert the terrain data into the kinematical variables of the moving platform in the VEPM, and further analyzes the performance of the VEPM at each terrain point. Then, the comprehensive performance of the VEPM is chosen as the optimization goal, and a task-oriented dimensional optimization approach combined with the particle swarm algorithm and the neural network algorithm is proposed. This paper conducted a comparative experiment to verify the superiority of the new approach in optimizing the ECM’s comprehensive performance, whose performance analysis also can be applied into the layout design of the ECM. This paper proposed an analysis method to construct the ECM’s performance map based on the digital terrain map, which helps the control system and operator to make the optimal control decision.

Retinal surgery is widely considered to be a complicated and challenging task even for specialists. Image-guided robot-assisted intervention is among the novel and promising solutions that may enhance human capabilities therein. In this paper, we demonstrate the possibility of using spotlights for 5D guidance of a microsurgical instrument. The theoretical basis of the localization for the instrument based on the projection of a single spotlight is analyzed to deduce the position and orientation of the spotlight source. The usage of multiple spotlights is also proposed to check the possibility of further improvements for the performance boundaries. The proposed method is verified within a high-fidelity simulation environment using the 3D creation suite Blender. Experimental results show that the average positioning error is 0.029 mm using a single spotlight and 0.025 mm with three spotlights, respectively, while the rotational errors are 0.124 and 0.101, which shows the application to be promising in instrument localization for retinal surgery.

This paper explores finite-time formation control of multi-agent systems (MASs) with high-order nonaffine nonlinear dynamics and saturated input. Based on active disturbance rejection control theory, extended state observer is employed to identify unknown nonaffine nonlinear functions in MASs. The proposed control law consisting of backstepping control, tracking differentiator, and finite-time performance function is adopted for MASs to achieve the desired formation while reaching performance requirements. An auxiliary dynamic compensator is introduced to correct the control deviation caused by input saturation. Lyapunov stability theory is utilized to analyze the stability of the closed-loop system, which guarantees that the formation tracking error can asymptotically converge to an arbitrarily small neighborhood around zero in finite time. Finally, the simulation results show that compared to the adaptive, cooperative learning, and virtual structure methods, the proposed control algorithm has stronger tracking ability and faster setting time (1.8 s) under the influence of nonaffine nonlinear uncertainties. The integral square error for the formation control strategy in this paper is 0.16, which is much smaller than the abovementioned methods and is therefore provided to manifest the validity and feasibility of the proposed control strategy.

The paper addresses a significant challenge in on-orbit robotics servicing and assembly, which is to overcome the saturation setback of force/torque on robot joint and spacecraft actuators during the post-capture stage while controlling a target spacecraft with uncontrolled large angular and linear momentums. The authors propose a novel solution based on two robust and efficient control algorithms: Optimal Control Allocation (OCA) and Non-linear Model Predictive Control (NMPC). Both algorithms aim to minimize joint torques, spacecraft actuator moments, contact forces, and moments of a compound redundant system that includes a common payload (target spacecraft) grasped by dual n-degree space robotics manipulators mounted on a chaser spacecraft. The OCA algorithm minimizes a quadratic cost function using only the current states and the system dynamics, but the NMPC also considers the future state estimates and the control inputs over a specified prediction horizon. It is computationally more involved but provides superior results in reducing joint torques. The literature to date in application of MPC to robotics mainly focuses on linear models but the dual arm coordination is highly non-linear and there is no MPC application on dual arm coordination. The proposed discretized technique offers exact realizations (of a non-linear model) with elegance and simplicity and yet considers the full non-linear model of the dual arm coordinating system. It is computationally very efficient. The computer simulation results show that the proposed algorithms work efficiently, and the minimum torques, contact forces, and moments are realized. The developed algorithm also is very efficient in tracking problems.

With increasing attention to physical human-machine interaction (pHMI), new control methods involving contact force regulation in collaborative and coexistence scenarios have spread in recent years. Thanks to its internal robustness, high dynamic performance, and capabilities to avoid constraint violations, a Model Predictive Control (MPC) action can pose a viable solution to manage the uncertainties involved in those applications. This paper uses an MPC-driven control method that aims to apply a well-defined and tunable force impulse on a human subject. After describing a general control design suitable to achieve this goal, a practical implementation of such a logic, based on an MPC controller, is shown. In particular, the physical interaction considered is the one occurring between the body of a patient and an external perturbation device in a dynamic posturography trial. The device prototype is presented in both its hardware architecture and software design. The MPC-based main control parameters are thus tuned inside hardware-in-the-loop and human-in-the-loop environments to get optimal behaviors. Finally, the device performance is analyzed to assess the MPC algorithm’s accuracy, repeatability, flexibility, and robustness concerning the several uncertainties due to the specific pHMI environment considered.

Accurate tracking and reconstruction of surgical scenes is a critical enabling technology toward autonomous robotic surgery. In endoscopic examinations, computer vision has provided assistance in many aspects, such as aiding in diagnosis or scene reconstruction. Estimation of camera motion and scene reconstruction from intra-abdominal images are challenging due to irregular illumination and weak texture of endoscopic images. Current surgical 3D perception algorithms for camera and object pose estimation rely on geometric information (e.g., points, lines, and surfaces) obtained from optical images. Unfortunately, standard hand-crafted local features for pose estimation usually do not perform well in laparoscopic environments. In this paper, a novel self-supervised Surgical Perception Stereo Visual Odometer (SPSVO) framework is proposed to accurately estimate endoscopic pose and better assist surgeons in locating and diagnosing lesions. The proposed SPSVO system combines a self-learning feature extraction method and a self-supervised matching procedure to overcome the adverse effects of irregular illumination in endoscopic images. The framework of the proposed SPSVO includes image pre-processing, feature extraction, stereo matching, feature tracking, keyframe selection, and pose graph optimization. The SPSVO can simultaneously associate the appearance of extracted feature points and textural information for fast and accurate feature tracking. A nonlinear pose graph optimization method is adopted to facilitate the backend process. The effectiveness of the proposed SPSVO framework is demonstrated on a public endoscopic dataset, with the obtained root mean square error of trajectory tracking reaching 0.278 to 0.690 mm. The computation speed of the proposed SPSVO system can reach 71ms per frame.

This paper presents a study on the design and modeling of a novel pneumatic self-repairing soft actuator. The self-repairing soft actuator is composed of driving element, heating element, and repairing element. The driving element completes the deformation of the self-repairing soft actuator. The heating element and the repairing element complete the self-repairing function of the self-repairing soft actuator. A model used to optimize the structure is established, and the structure of the self-repairing soft actuator is determined through finite element analysis and experiment. The self-repairing time model of the soft actuator is established. The influences of different factors on the self-repairing effect and the self-repairing time are analyzed. The self-repairing scheme of the soft actuator is determined. Experiments show that the shortest time for the self-repairing soft actuator to complete the self-repairing process is 83 min. When the self-repairing soft actuator works normally, the bending angle can reach 129.8° and the bending force can reach 24.96 N. After repairing, the bending angle can reach 108.2°, and the bending force can reach 21.85 N. The repaired soft actuator can complete normal locomotion.

In this manuscript, a scheme for neural-learning-enhanced Cartesian Admittance control is presented for a robotic manipulator to deal with dynamic environments with moving remote center of motion (RCM) constraints. Although some research has been implemented to address fixed constrained motion, the dynamic moving movement constraint is still challenging. Indeed, the moving active RCM constraints generate uncertain disturbance on the robot tool shaft with unknown dynamics. The neural-learning-enhanced decoupled controller with disturbance optimisation is employed and implemented to maintain the performance under the kinematic uncertain and dynamic uncertain generated. In addition, the admittance Cartesian control method is introduced to control the robot, providing compliant behaviour to an external force in its operational space. In this proposed framework, a neural-learning-enhanced disturbance observer is investigated to calculate the external factor operating on the end effector premised on generalised momentum in order to ensure accuracy. Finally, the experiments are implemented using a redundant robot to validate the efficacy of the suggested approach with moving RCM constraints.

In this paper, the realization of any specified planar Cartesian compliance for an object grasped by a compliant hand is addressed. The hands considered have 2 or more fingers for which each has 3 modulated elastic joints and predetermined link lengths. Geometric construction-based compliance synthesis procedures are developed. Using these procedures, a large set of compliant behaviors can be realized by a single hand simply by adjusting the configuration of each finger and by adjusting the joint stiffness (using variable stiffness actuation) of each finger joint.

There are few studies on the intelligent guidance of unmanned sailboats, which should coordinate pluralistic tasks at sea in the nature of its maneuvring intractability. To ensure the algorithmic practicability, this paper proposes a path-following and collision-avoidance guidance approach of unmanned sailboats with total formulaic description. The risk-detecting mechanism is fabricated by setting a circular detecting zone and using the time to the closest point of approach. Then, the risk of collision, the path deviation, the speed loss, and the course loss can be judged by constructing the cost functions and applying the distance to closest point of approach. The optimized heading angle is deemed as the one minimizing the aggregate cost functions, which is sought by applying and improving the beetle antennae search (BAS) algorithm. In the proposed modified BAS, the searching step is redesigned to enhance the searching efficiency. To ensure the convergence of the real heading angle to the reference, the backstepping-based control law is fabricated for the high-order sailboat model and in the linear form. The control parameters are offline optimized through the modified BAS. Compared with the adaptive control, this controller can guarantee more computation simplicity and the optimized control performance. Finally, simulation corroborates that the sailboat can successfully complete path following and collision avoidance while encountering multiple static and moving obstacles under the proposed schemes.

As a heavy load is applied to the parallel manipulators, it causes inaccuracies while positioning the end-effector or unbalanced dynamic forces in the legs. Various load-balancing techniques overcome this. However, the disadvantage of most load-balancing mechanisms is that they add inertia to the assembly and decrease the speed of motion. This article studies a new load-balancing method (a passive damper mechanism). The passive balancing mechanism is proposed to negate the inertia effects while countering the static inaccuracies in the parallel mechanism. This is verified by the structural analysis of the mechanism. The impact of the damper element on the dynamics of the mechanism is unknown. Hence, a complete mathematical model for the balancing mechanism has been developed to study its impact on the dynamics of the entire structure. Laplace transformations characterize the system response. The inclusion of a passive damper in a 3-prismatic-prismatic-revolute-spherical system was examined and found to be stable and critically damped. Such a passive damper was envisaged to facilitate additional force transmission for the actuators, and the DC gain from the system response validates the torque support for the actuators.

There are various matrices to represent parallel mechanisms. It is essential to design a kind of approach to not only denote the parallel structures but also disclose the joint directions. In this paper, a novel methodology called the kinematic joint matrix (KJM) is proposed. It possesses the mapping relations with parallel manipulators with three kinds of kinematic joints. The size of such matrix is smaller when compared with that of topology matrix. A series of two to six degrees-of-freedom parallel architectures is denoted by the KJM. A convenient approach using a special block diagram is introduced to distinguish various kinds of kinematic joint matrices. In addition, detailed comparisons between KJM and topology matrix are investigated. Three regulations are proposed for the latter to be applicable to parallel mechanisms.