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In this paper, two finite-time active fault-tolerant controllers for a robot manipulator, which combine a synchronous terminal sliding mode control with an extended state observer, are proposed. First, an extended state observer is adopted to estimate the lumped uncertainties, disturbances, and faults. The estimation information is used to compensate the controller designed in the following step. We present an active fault-tolerant control with finite-time synchronous terminal sliding mode control, largely based on a novel finite-time synchronization error and coupling position error. We also present an active fault-tolerant control that does not use a coupling position error. By using synchronization control, the position error at each joint can simultaneously approach toward zero and toward equality, which may reduce the picking phenomenon associated with the active fault-tolerant controller strategy. Finally, simulation and experimental results for a three degree-of-freedom robot manipulator verify the effectiveness of the two proposed active fault-tolerant controllers.

Individuals with reduced mobility, including the growing elderly demographic and those with spinal cord injuries, often face significant challenges in daily activities, leading to a dependence on assistance. To enhance their independence, we propose a robotic system that facilitates greater autonomy. Our approach involves a functional assistive robotic implementation for picking, placing, and delivering containers using the TIAGo mobile manipulator robot. We developed software and routines for detecting containers marked with an ArUco code and manipulating them using the MoveIt library. Subsequently, the robot navigates to specific points of interest within a room to deliver the container to the user or another designated location. This assistance task is commanded through a user interface based on a web application that can be accessed from the personal phones of patients. The functionality of the system was validated through testing. Additionally, a series of user trials were conducted, yielding positive feedback on the performance and the demonstration. Insights gained from user feedback will be incorporated into future improvements to the system.

This study presents a combination of the robust passivity-based controller (RPBC) and an extended state observer (ESO) for the tracking control of a three degree-of-freedom PUMA 500 robot manipulator under parameter variations and external disturbances. The dynamic model of the PUMA robot and its structural properties are analysed. The extra state in the ESO estimates the parameter variations and external disturbances in the control system. Then the RPBC cancels them in the control law. The stability of the proposed control system and the convergence of the observation errors are analysed. Simulations prove that the proposed controller is robust and tracks well under parameter variations, and external disturbances, validating the proposed control scheme.

Biosignals will play an important role in building communication between machines and humans. One of the types of biosignals that is widely used in neuroscience are electrooculography (EOG) signals. An EOG has a linear relationship with eye movement displacement. Experiments were performed to construct a gaze motion tracking method indicated by robot manipulator movements. Three operators looked at 24 target points displayed on a monitor that was 40 cm in front of them. Two channels (Ch1 and Ch2) produced EOG signals for every single eye movement. These signals were converted to pixel units by using the linear relationship between EOG signals and gaze motion distances. The conversion outcomes were actual pixel locations. An affine transform method is proposed to determine the shift of actual pixels to target pixels. This method consisted of sequences of five geometry processes, which are translation-1, rotation, translation-2, shear and dilatation. The accuracy was approximately 0.86° ± 0.67° in the horizontal direction and 0.54° ± 0.34° in the vertical. This system successfully tracked the gaze motions not only in direction, but also in distance. Using this system, three operators could operate a robot manipulator to point at some targets. This result shows that the method is reliable in building communication between humans and machines using EOGs.

This paper investigates the high-precision sliding mode based tracking control of robot manipulators with uncertain dynamics and external disturbances. Different from most currently used fast nonsingular terminal sliding mode surfaces (FNTSMs) which use linear sliding mode (LSM) to avoid singularity, a new recursive terminal sliding mode surface (RTSM) is firstly constructed with a recursive structure to avoid the singularity problem in this paper. The RTSM can improve convergence precision and speed near the equilibrium point compared with FNTSMs. Then a sliding mode based controller has been designed to stabilize the closed-loop system. To cope with model uncertainties, frictions and external disturbances, a novel adaptive sliding mode disturbance observer (ASMDO) has been constructed, which can estimate lumped uncertainties and feed them to the controller to achieve disturbance-rejection control. Compared with traditional disturbance observer with asymptotic stability, the observation error of ASMDO can be driven into a sufficiently small set centered on zero within a predefined fixed time, which means fast observation speed and high observation accuracy. The upper bounds of uncertainties and their derivatives are not needed in observer design. Abundant simulations and experiments also verified the effectiveness and superior properties of the proposed scheme.

The use of industrial robots is widespread in diverse manufacturing fields. Hence, there have been attempts to use robot for machining processes instead of machine tools. However, limited machining accuracy has been a major obstacle hampering the adoption of robotic machining systems. Recently, substantial research has been carried out to address this issue. In this paper, recent progress in robotic machining has been summarized, such as kinematic calibration and compliance error compensation to improve the accuracy of robotic machining. Auxiliary units for improving the performance of robotic machining systems are also discussed.

Efficiently solving inverse kinematics (IK) of robot manipulators with offset wrists remains a challenge in robotics due to noncompliance with Pieper criteria. In this paper, an improved method to solve the IK for 6-DOF robot manipulators with offset wrists is proposed. This method is based on the Newton iteration technique, but it does not require a selection of initial estimation of joint variables. The solution is divided into two parts: the first part is to reconstruct a simplified structure with analytical IK solution, and the second part is to obtain a numerical solution by iteration. Further, a robot manipulator HSR-BR606 with an offset wrist is used as an example to specifically elaborate the mathematical procedure of the method and to investigate the algorithm in terms of accuracy, efficiency, and application of motion planning. A comparative experiment is conducted with a typical IK algorithm, which demonstrates a higher accuracy and shorter calculation time of the proposed method. The mean calculation time for a single IK solution required for this algorithm is only 4% of the comparison algorithm.

In recent years, a large number of manipulator robots have been deployed to replace or assist humans in many repetitive and dangerous tasks. Yet, these robots have complex mechanisms, resulting in their non-linearity of kinematics and dynamics as well as intensive computations. Therefore, relying on soft computing techniques are a common and alternative key to model and control these systems. In particular, fuzzy logic approaches have proven to be simple, efficient, and superior to relevant well-known methods and have sparked greater interest in robotic applications. To help researchers meet their needs easily and quickly in finding relevant research works on fuzzy-based solutions, this article adapted to provide an in-depth review of the currently updated fuzzy logic approaches for collision-free path planning of serial manipulator robots operating in complex and cluttered workspaces. In addition to a comprehensive description of fuzzy hybridization with other artificial intelligence techniques description. Further, this article attempts to present the main solutions with a summary and visualization of all basic approaches that path-planning problems may subtend in the decision-making process. Finally, the paper suggests some potential challenges and explores research issues for future work.

Manipulator robots hold significant importance for the development of intelligent manufacturing and industrial transformation. Manufacturers and users are increasingly focusing on fault diagnosis for manipulator robots. The voltage, current, speed, torque, and vibration signals of manipulator robots are often used to explore the fault characteristics from a frequency perspective, and temperature and sound are also used to represent the fault information of manipulator robots from different perspectives. Technically, manipulator robot fault diagnosis involving human intervention is gradually being replaced by new technologies, such as expert experience, artificial intelligence, and digital twin methods. Previous reviews have tended to focus on a single type of fault, such as analysis of reducers or joint bearings, which has led to a lack of comprehensive summary of various methods for manipulator robot fault diagnosis. Considering the needs of future research, a review of different fault types and diagnostic methods of manipulator robots provides readers with a clearer reading experience and reveals potential challenges and opportunities. Such a review helps new researchers entering the field avoid duplicating past work and provides a comprehensive overview, guiding and encouraging readers to commit to enhancing the effectiveness and practicality of manipulator robot fault diagnosis technologies.

This paper presents a novel approach to real-time obstacle avoidance based on Dynamical Systems (DS) that ensures impenetrability of multiple convex shaped objects. The proposed method can be applied to perform obstacle avoidance in Cartesian and Joint spaces and using both autonomous and non-autonomous DS-based controllers. Obstacle avoidance proceeds by modulating the original dynamics of the controller. The modulation is parameterizable and allows to determine a safety margin and to increase the robot’s reactiveness in the face of uncertainty in the localization of the obstacle. The method is validated in simulation on different types of DS including locally and globally asymptotically stable DS, autonomous and non-autonomous DS, limit cycles, and unstable DS. Further, we verify it in several robot experiments on the 7 degrees of freedom Barrett WAM arm.