Control Of Manipulators Research Articles

Viewpoint-sharing method with reduced motion sickness in object-based VR/AR collaborative virtual environment

We propose a viewpoint-sharing method with reduced motion sickness in an object-based remote collaborative virtual environment (CVE). The method is designed with an assumption of asymmetric, object-based CVE where collaborators use non-homogeneous devices, such as immersive virtual reality head-mounted display (VR HMD) and tablet-based augmented reality (AR), and simultaneously interact with 3D virtual objects. Therefore, collaborators interact with different interfaces such as virtual reality (VR) users relying on controllers for virtual locomotion and object manipulation, while AR users perform physical locomotion and multi-touch input for object manipulation. The proposed viewpoint-sharing method allows both users to observe and manipulate the objects in interest from the shared point of view, enabling participants to interact with the objects without the need for virtual/physical locomotion. While viewpoint-sharing, instead of changing point of view, the proposed method performs seamless object transformation to provide a shared point of view, reducing motion sickness and associated discomfort. From our user experiment, the viewpoint-share condition resulted in a 35.47% faster task completion time than the baseline condition which is without proposed viewpoint-sharing. The advantage of viewpoint-sharing regarding system usability was significant, while task workloads were similar in the baseline and viewpoint-sharing conditions. We expect that the proposed viewpoint-sharing method allows users to quickly, efficiently, and collaboratively communicate in an object-based CVE, and represents a step forward in the development of effective remote, asymmetric CVE.

Observer based output feedback repetitive learning control of robotic manipulators

AbstractThis work tackles the tracking control problem of robotic manipulators where the robot dynamics contains uncertain parameters and joint velocity measurements are not available. Specifically when the robotic manipulator is required to perform a periodic task repetitively, as in most industrial applications, a repetitive learning controller is proposed that does not require joint velocity measurements and can compensate the uncertainties of the robot dynamical parameters and additive disturbances caused due to the periodic joint motion. The proposed solution is achieved via the use of a novel learning component in the controller design in conjunction with a novel model‐free joint velocity observer design. The stability of the closed‐loop system and the convergence of both the joint position tracking error and the joint velocity observation error to the origin are guaranteed via Lyapunov based arguments. Experimental results performed on a 2 degree of freedom robot manipulator are presented to demonstrate the performance of the proposed observer–controller couple.

Precision motion control of rotary flexible link manipulators using polynomial input trajectories and feedback control

This paper aims to develop a high-precision controller for a flexible link manipulator using polynomial reference, feedforward input, and feedback control. The control objective is to move the tip of the flexible link to a target position as quickly as possible without exciting its resonant modes. Upon a frequency response-based system identification, a frequency-domain feedback controller is designed for reference tracking. The controller includes a proportional-integral controller, a lead filter, and a set of notch filters to control the rigid body and the flexible link dynamics. A set of polynomial reference and feedforward input trajectories are then designed to improve the tracking performance and minimize residual vibrations. The conventional polynomial reference is generated based on the rigid body motion of the system. A revised polynomial reference generation method is then developed by adding flexible motions to the conventional reference. Simulations and experimental tests indicate significant improvements in the performance of the control system using the revised polynomial trajectories.

Adaptive Fuzzy Integral Sliding Mode Cooperative Control Based on Time-Delay Estimation for Free-Floating Close-Chain Manipulators.

Space manipulators are expected to perform more challenging missions in on-orbit service (OOS) systems, but there are some unique characteristics that are not found on ground-based robots, such as dynamic coupling between space bases and manipulators, limited fuel supply, and working with unfixed bases. This paper focuses on trajectory-tracking control and internal force control for free-floating close-chain manipulators. First, the kinematics and dynamics of free-floating close-chain manipulators are given using the momentum conservation and spatial operator algebra (SOA) methodologies, respectively. Furthermore, an adaptive fuzzy integral sliding mode controller (AFISMC) based on time delay estimation (TDE) was designed for trajectory-tracking control, and a proportional-integral (PI) control strategy was adopted for internal force control. The global asymptotic stability of the proposed controller was proven by using the Lyapunov methodology. Three cases were conducted to verify the efficiency of the controller by using numerical simulations on two six-link manipulators with a free-floating base. The controller presents the desired tracking capability.

A comprehensive dynamic model and configuration control of a free-floating soft manipulator-spacecraft system

Space robots are inseparable components of many space missions. However, capabilities of space robots with rigid manipulators are restricted in confronting with unknown and confined environments and situations require high safety. To overcome these defects, the potential of bio-inspired soft robots for space applications has recently been addressed in the literature. Dynamic modeling and control of soft manipulators are challenging due to their infinite degrees of freedom. In this paper, a general comprehensive three-dimensional dynamic modeling framework is developed for a floating soft manipulator-spacecraft system employing the Euler-Lagrange method. This framework derives the coupled equations of motion adapting the generalized Jacobian approach. Consequently, the opportunity of exploiting model-based control methods will be provided. In addition, the proposed model avoids numerical singularities once the bending angles are near zero. Furthermore, the generalized Jacobian matrix is defined for any arbitrary point of the soft manipulator essential for the contact dynamics. Eventually, to verify the proposed dynamic modeling procedure, the dynamic response of a floating soft manipulator-spacecraft system released from an initial configuration is investigated. Then, a sliding mode controller is designed for the floating soft manipulator to track the desired shape and compared to the proportional-derivative control scheme. The obtained results follow the physical principles and fulfill the control objective.

Manipulating the Crystallization of Perovskite via Metal‐Free DABCO‐NH4Cl3 Addition for High Efficiency Solar Cells

AbstractThe performance of perovskite cells closely relies on the quality of films, leading to a special focus on crystallization manipulation and defect control. In this study, a novel approach using 1D metal‐free perovskites, specifically DABCO‐NH4Cl3, is proposed to facilitate the crystallization process of 3D FAPbI3 perovskites, while simultaneously addressing surface defects. Analysis of crystallization kinetics reveals that the introduction of 1D metal‐free perovskites provides numerous nucleation sites, effectively slowing down crystal growth rates and resulting in the formation of uniform, large‐grain perovskite films. Furthermore, the organic groups present in 1D perovskites play a crucial role in passivating defects within the perovskite structure. The synergistic impact of these factors enables the perovskite to achieve an efficiency of 24.72% while demonstrating exceptional stability. This research offers a promising approach for controlling perovskite crystallization, leading to the development of high‐efficiency and stable perovskite materials.

Recurrent neural network robust curvature tracking control of tendon-driven continuum manipulators with simultaneous joint stiffness regulation

Recurrent neural network robust curvature tracking control of tendon-driven continuum manipulators with simultaneous joint stiffness regulation

Interval uncertainty-oriented impedance force control for space manipulator with time-dependent reliability

For specific missions of spacecraft including on-orbit assembly, docking, grasp, etc., the presence of uncertainties leads to difficulties in the analysis of impedance force control systems and evaluations of safety for space manipulators. Therefore, in this study, a novel interval uncertainty-oriented impedance force control method for space manipulators with time-dependent reliability was established. The proposed method can accurately and rapidly characterize uncertainties. Additionally, it can reflect the reliability of a control system based on an overall time history. Once the uncertainties of the dynamic models and state responses are known, their uncertainty bounds can be obtained using an established non-probabilistic propagation method with high accuracy. An interval process model and the first passage theory are employed in the non-probabilistic time-dependent model. A joint reliability index is established based on the single reliability indexes, which can more efficiently evaluate the control system reliability than the individual indexes. A numerical example related to space manipulator is used to study the developed impedance force control method and verify the effectiveness of the method. The results from the response curves reveal validity of the proposed method, while the reduction in time proves efficiency of the method.

Effective Nonlinear Predictive and CTC-PID Control of Rigid Manipulators

Effective nonlinear control of manipulators with dynamically coupled arms, like those with direct drives, is the subject of the paper. The main proposal of the paper are model-based predictive control (MPC) algorithms, with nonlinear state-space models and most recent disturbance attenuation technique. This technique makes controller design and calculations simpler, avoiding necessity of dynamic modeling of disturbances or resorting to additional techniques like SMC. The core of the paper are computationally effective MPC-NPL (Nonlinear Prediction and Linearization) algorithms, where computations at every sample are divided into two parts: prediction of initial trajectories using nonlinear model, then optimization using simplified linearized model. For a comparison a known CTC-PID algorithm, which is also model-based, is considered. It is applied in standard form and also proposed in more advanced CTC-PID2dof version. For all algorithms a comprehensive comparative simulation study is performed, for a direct drive manipulator under disturbances. Additional contribution of the paper is an investigation of influence of sampling period length and computational delay time on performance of the algorithms, which is practically important when using model-based algorithms and fast sampling.

On the simplification of the internal nonlinear robot models for the MPC-based visual servoing

Visual servoing enables tracking and grasping of static and moving objects and locating regions of interest. Its implementation requires the coordination of low-level control tasks with high-level supervision and planning. The use of the PID-based manipulation control addresses only the dynamical response and requires the use of optimization-based techniques at the supervisory level. On the contrary, model predictive control (MPC) simplifies the design as it combines both tasks within a single algorithm. However, successful MPC implementation depends on the quality of the internal model utilized by the algorithm. Robots are intrinsically and significantly nonlinear control objects, so their models are inherently complex. This leads to computational problems. Standard solutions are to shorten the MPC horizons or to parallelize calculations. The approach suggested here returns to the problem roots, i.e. to the model. This study proposes simplified models that allow the use of the MPC. General Hammerstein–Wiener-like model is applied to an arm, which is further simplified into the linear dynamics used as the predictive supervisory control over the torque control. The head uses linear dynamics used by the supervisory MPC working with the base servomotors. It is shown that they are computationally efficient and sufficiently accurate. It is shown that the linear MPC controllers, which use the suggested simplified dynamic robot models, can successfully support visual servoing with accurate control. They are compared with standard PID-based structures and show their superiority. The proposed approach is successfully validated using Matlab and Gazebo robot simulator and is ultimately confirmed by experiments on a real robot.

Tracking control of flexible link manipulator with joint space constraints

Abstract A controller design with system uncertainties, external disturbances, and joint space constraints for a flexible link manipulator (FLM) is presented in this paper. To overcome the lumped disturbance and ensure that the joint space constraints are not violated, a finite-time extended state observer (ESO) and a barrier Lyapunov function are constructed. Using Lyapunov’s theory, the error of the system is proved to be semi-globally ultimately uniformly bounded without violating the constraints. Finally, the rationality of the proposed methodology is verified by numerical simulation.

Adaptive synchronous sliding control for a robot manipulator based on neural networks and fuzzy logic

Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for robot hands is always an attractive topic in the research community. This is a challenging problem because robot manipulators are complex nonlinear systems and are often subject to fluctuations in loads and external disturbances. This article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller ensures that the positions of the joints track the desired trajectory, synchronize the errors, and significantly reduces chattering. First, the synchronous tracking errors and synchronous sliding surfaces are presented. Second, the synchronous tracking error dynamics are determined. Third, a robust adaptive control law is designed, the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy logic. The built algorithm ensures that the tracking and approximation errors are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results. Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is significantly reduced.

Vibration Suppression and Trajectory Tracking Control of Flexible Joint Manipulator Based on PSO Algorithm and Fixed-Time Control

Vibration Suppression and Trajectory Tracking Control of Flexible Joint Manipulator Based on PSO Algorithm and Fixed-Time Control

Inverse-Free DZNN Models for Solving Time-Dependent Linear System via High-Precision Linear Six-Step Method.

Time-dependent linear system (TDLS) is usually encountered in scientific research, which is the mathematical formulation of many practical applications. Different from conventional inverse-need models, by utilizing zeroing neural network (ZNN) method twice, an inverse-free continuous ZNN (CZNN) model is developed for solving TDLS. For conveniently practical use, a discrete model is naturally desired. Superior to conventional discretization methods, a general linear six-step (LSS) method with the seventh-order precision and five variable parameters is proposed for the first time. Constraints about five variable parameters are theoretically analyzed to guarantee the efficacy of the general LSS method. Within constraints, 12 specific LSS methods are further developed. Aided with the general LSS method, an inverse-free discrete ZNN (DZNN) is proposed and termed DZNN-LSS model, and its precision is greatly improved compared with conventional discrete models. For comparison, three conventional discretization methods are also utilized to generate DZNN models. Detailed theoretical analyses are provided to prove the efficacy of relevant models. In addition, a specific TDLS example is considered to show the effectiveness and superiority of the DZNN-LSS model. More than that, applications to manipulator control and sound source localization are conducted to illustrate the applicability of the DZNN-LSS model.

Inverse kinematics solution and control method of 6-degree-of-freedom manipulator based on deep reinforcement learning

The advent of Industry 4.0 has significantly promoted the field of intelligent manufacturing, which is facilitated by the development of new technologies are emerging. Robot technology and robot intelligence methods have rapidly developed and been widely applied. Manipulators are widely used in industry, and their control is a crucial research topic. The inverse kinematics solution of manipulators is an important part of manipulator control, which calculates the joint angles required for the end effector to reach a desired position and posture. Traditional inverse kinematics solution algorithms often face the problem of insufficient generalization, and iterative methods have challenges such as large computation and long solution time. This paper proposes a reinforcement learning-based inverse kinematics solution algorithm, called the MAPPO-IK algorithm. The algorithm trains the manipulator agent using the MAPPO algorithm and calculates the difference between the end effector state of the manipulator and the target posture in real-time by designing a reward mechanism, while considering Gaussian distance and cosine distance. Through experimental comparative analysis, the feasibility, computational efficiency, and superiority of this reinforcement learning algorithm are verified. Compared with traditional inverse kinematics solution algorithms, this method has good generalization and supports real-time computation, and the obtained result is a unique solution. Reinforcement learning algorithms have better adaptability to complex environments and can handle different sudden situations in different environments. This algorithm also has the advantages of path planning, intelligent obstacle avoidance, and other advantages in dynamically processing complex environmental scenes.

Pairtools: From sequencing data to chromosome contacts.

The field of 3D genome organization produces large amounts of sequencing data from Hi-C and a rapidly-expanding set of other chromosome conformation protocols (3C+). Massive and heterogeneous 3C+ data require high-performance and flexible processing of sequenced reads into contact pairs. To meet these challenges, we present pairtools-a flexible suite of tools for contact extraction from sequencing data. Pairtools provides modular command-line interface (CLI) tools that can be flexibly chained into data processing pipelines. The core operations provided by pairtools are parsing of.sam alignments into Hi-C pairs, sorting and removal of PCR duplicates. In addition, pairtools provides auxiliary tools for building feature-rich 3C+ pipelines, including contact pair manipulation, filtration, and quality control. Benchmarking pairtools against popular 3C+ data pipelines shows advantages of pairtools for high-performance and flexible 3C+ analysis. Finally, pairtools provides protocol-specific tools for restriction-based protocols, haplotype-resolved contacts, and single-cell Hi-C. The combination of CLI tools and tight integration with Python data analysis libraries makes pairtools a versatile foundation for a broad range of 3C+ pipelines.

Transcultural Appropriation and Aesthetic Breakthrough of Hollywood Film Noir in Contemporary Taiwan Suspense Thriller Films: A Case Study of Who Killed Cock Robin (2017)

The production of suspense thriller films has recently surged in Taiwan. These films adopt narrative techniques and visual aesthetics reminiscent of classic and neo-noir Hollywood cinema but also address social issues in Taiwan and represent transcultural aesthetic appropriation of film noir. This article employs a case study approach to examine the narrative and visual style of the Taiwanese suspense thriller Who Killed Cock Robin (2017), using film narratology as a textual analytical framework. This study considers themes, characters, visual style, and narrative structures, focusing on fundamental characteristics of classic film noir and neo-noir. This study reveals that the selected film both appropriates and deviates from the aesthetics of Hollywood film noir. It effectively incorporates aesthetic elements from classic Hollywood film noir and neo-noir, enriching the intricacies of storytelling and character depiction, while also localizing them through complex narrative strategies and nuanced Taiwanese cultural and social elements. The film brings attention to several prevalent issues in Taiwan’s media landscape, including truth manipulation, sensationalism, tabloidization, and conglomerate and political control. The film portrays Yi-Chi as a morally compromised character embodying the detective archetype with classic noir traits, while also reflecting the “Eastern mentality” in Taiwan journalism. Despite his moral compromises, Yi-Chi partly retains traditional virtues, presenting a nuanced view of human nature that blends pessimism and optimism in Taiwan. This approach creates a distinct cross-cultural narrative that resonates emotionally with Taiwanese audiences, while also contributing to the broader global cinematic discourse on film noir.

Whole Body Control of Mobile Manipulators With Series Elastic Actuators for Cart Pushing Tasks.

Human-centered environments provide affordance for the use of two-handed mobile manipulators. Yet robots designed to function in and physically interact with such environments are not yet capable of meeting human users' requirements. This work proposes a whole body control framework of a two-handed mobile manipulator driven by series elastic actuators (SEAs) for cart pushing tasks. A whole body dynamic model for an integrated mobile platform and on-board arms is revealed, which takes into account the interaction forces with the cart. Then, the explicit force/position control of the mobile manipulator is performed. It enables the robot to interact dynamically with the environment while providing motion, i.e., the manipulators provide both output force control and motion control for pushing a cart. To cope with the highly nonlinear system dynamics and parameter variation of a SEA-driven mobile manipulator, this work proposes an adaptive robust controller based on a novel integral barrier Lyapunov function for cart pushing tasks by considering model uncertainty. The proposed controller enables the mobile manipulator to complete cart pushing tasks by regulating the position and output force of the mobile base and arms. The experimental results show the effectiveness of this approach in cart pushing tasks.

Event-triggered consensus tracking control of flexible manipulators with nonlinear time-varying fault-tolerant actuators and input constraint

This paper introduces a novel event-triggered consensus control strategy for a multi-agent system composed of underactuated flexible manipulators. Most existing research on consensus tracking control assumes that the model targeted is fully actuated, the control signal is continuous, and that both time-varying actuator failures and input constraints are managed separately. These challenges were tackled in this study within the context of multi-agent systems, addressing input constraints and nonlinear time-varying actuator failures simultaneously. Moreover, an event-triggered mechanism is proposed to reduce signal communication. A double closed-loop consensus tracking method is designed based on the multi-agent topology, enabling all flexible manipulators to track the angle and angular velocity of the leader and an adaptive control law is designed to solve actuator faults. Finally, the simulation results demonstrate the effectiveness of the proposed control scheme.

Model-free visual servoing based on active disturbance rejection control and adaptive estimator for robotic manipulation without calibration

PurposeVisual feedback control is a promising solution for robots work in unstructured environments, and this is accomplished by estimation of the time derivative relationship between the image features and the robot moving. While some of the drawbacks associated with most visual servoing (VS) approaches include the vision–motor mapping computation and the robots’ dynamic performance, the problem of designing optimal and more effective VS systems still remains challenging. Thus, the purpose of this paper is to propose and evaluate the VS method for robots in an unstructured environment.Design/methodology/approachThis paper presents a new model-free VS control of a robotic manipulator, for which an adaptive estimator aid by network learning is proposed using online estimation of the vision–motor mapping relationship in an environment without the knowledge of statistical noise. Based on the adaptive estimator, a model-free VS schema was constructed by introducing an active disturbance rejection control (ADRC). In our schema, the VS system was designed independently of the robot kinematic model.FindingsThe various simulations and experiments were conducted to verify the proposed approach by using an eye-in-hand robot manipulator without calibration and vision depth information, which can improve the autonomous maneuverability of the robot and also allow the robot to adapt its motion according to the image feature changes in real time. In the current method, the image feature trajectory was stable in the camera field range, and the robot’s end motion trajectory did not exhibit shock retreat. The results showed that the steady-state errors of image features was within 19.74 pixels, the robot positioning was stable within 1.53 mm and 0.0373 rad and the convergence rate of the control system was less than 7.21 s in real grasping tasks.Originality/valueCompared with traditional Kalman filtering for image-based VS and position-based VS methods, this paper adopts the model-free VS method based on the adaptive mapping estimator combination with the ADRC controller, which is effective for improving the dynamic performance of robot systems. The proposed model-free VS schema is suitable for robots’ grasping manipulation in unstructured environments.