Control Method For Robot Research Articles

A variable structure passivity control method for elastic joint robots based on cascaded high-order state estimation

A variable structure passivity control method for elastic joint robots based on cascaded high-order state estimation

Vision-admittance-based adaptive RBFNN control with a SMC robust compensator for collaborative parallel robots

This paper proposes a novel vision-admittance-based adaptive neural network control method for collaborative parallel robots tracking a predefined trajectory and force command. First of all, the vision-admittance model is creatively developed for coupling visual information and force-sensing information in the image feature space and is applied to generate the image feature reference trajectory online according to the desired trajectory and the command of the interaction force. Secondly, considering the system modelling uncertainties and external disturbances, an adaptive Radial basis functions neural network (RBFNN) controller is created to realize high-precision trajectory tracking. The adaptive tuning laws are designed based on the Lyapunov stability theorem so that the entire system’s stability and the convergence of the weight adaptation can be guaranteed. Moreover, a sliding mode control (SMC)-based robust compensator is added to the RBFNN controller as an auxiliary term to improve the robustness and stability of the control system. Finally, co-simulations between ADAMS/Simulink are performed on a collaborative Five-bar parallel robot to demonstrate the efficacy of the proposed controller.

Anti-Disturbance Path-Following Control for Snake Robots With Spiral Motion

Three-dimensional spiral gait enables a snake robot to climb over obstacles, cross caves, and adapt to complex environments. This paper reports an anti-disturbance path-following control method for a snake robot with a spiral gait. This method reduces the deviation of the robot's position in following the ideal path by estimating the time-varying parameters, the external disturbances, and the viscous friction coefficients. The estimations are used to compensate for the control inputs of the system, which can improve the adaptability of the robot to the environment. Then, the attitude and position errors can rapidly converge to the origin. An appropriate Lyapunov function is adopted to explore the stability of following errors. Experimental results show that the proposed method can accelerate the convergence rate of errors, reduce the fluctuation peak, and improve the following stability of snake robots.

Reinforcement learning-based stable jump control method for asteroid-exploration quadruped robots

Unlike the spherical gravitational field of planets and other large solar system bodies, the gravitational field of asteroids is irregular and weak. It is challenging for a planetary rover to obtain sufficient traction forces in this environment. However, this gravitational environment is suitable for legged robots with jumping ability, but it also imposes higher demands on control methods. Therefore, this study aimed to address the problem of jump control method for asteroid-exploration quadruped robots. As the robot jumps off the surface of an asteroid, it would fly for a certain amount of time because of the low gravitational acceleration. The prolonged flight phase underscores the significance of the robot's take off and attitude control. A model-free stable jumping control method was devised in this study. This method can satisfy the control requirements for takeoff, attitude adjustment, and soft landing by using end-to-end multi-agent reinforcement learning (MARL). MARL is more advantageous than single-agent reinforcement learning in dealing with composite motion control problems under similar observation conditions. A simulated training environment was established, incorporating models of the gravitational field, task partitioning for jumping, and design of reward functions, including jump trajectory planning. The efficacy of the proposed jump control method for a quadruped robot was successfully demonstrated in the gravitational environment of an irregular rod-shaped asteroid, 216 Kleopatra.

Effect of hip abduction assistance on metabolic cost and balance during human walking.

The use of wearable robots to provide walking assistance has rapidly grown over the past decade, with notable advances made in robot design and control methods toward reducing physical effort while performing an activity. The reduction in walking effort has mainly been achieved by assisting forward progression in the sagittal plane. Human gait, however, is a complex movement that combines motions in three planes, not only the sagittal but also the transverse and frontal planes. In the frontal plane, the hip joint plays a key role in gait, including balance. However, wearable robots targeting this motion have rarely been investigated. In this study, we developed a hip abduction assistance wearable robot by formulating the hypothesis that assistance that mimics the biological hip abduction moment or power could reduce the metabolic cost of walking and affect the dynamic balance. We found that hip abduction assistance with a biological moment second peak mimic profile reduced the metabolic cost of walking by 11.6% compared with the normal walking condition. The assistance also influenced balance-related parameters, including the margin of stability. Hip abduction assistance influenced the center-of-mass movement in the mediolateral direction. When the robot assistance was applied as the center of mass moved toward the opposite leg, the assistance replaced some of the efforts that would have otherwise been provided by the human. This indicates that hip abduction assistance can reduce physical effort during human walking while influencing balance.

A decoupled Bayesian method for snake robot control in unstructured environment

This paper presents a method which avoids the common practice of using a complex coupled snake robot model and performing kinematic analysis for control in cluttered environments. Instead, we introduce a completely decoupled dynamical Bayesian formulation with respect to interacted snake robot links and environmental objects, which requires much lower complexity for efficient and robust control. When a snake robot does not interact with obstacles, it runs by a simple serpenoid controller. However, when it exhibits interaction with environments, defined as close proximity or collision with targets and/or obstacles, we extend the conventional Bayesian framework by modeling such interactions in terms of stimuli. The proposed ‘multi-neural-stimulus function’ represents the cumulative effect of both external environmental influences and internal constraints of the snake robot. It implicitly handles the ‘unexpected collision’ problem and thus solve the difficult data association and shape adjustment problems for snake robot control in an innovative way. Preliminary experimental results have demonstrated promising performance of the proposed method comparing with the state-of-the-art.

A Dual-Arm Robot That Manipulates Heavy Plates With the Support of a Vacuum Lifter

A vacuum lifter is widely used to hold and pick up large, heavy, and flat objects. Conventionally, when using a vacuum lifter, a human worker watches the state of a running vacuum lifter and adjusts the object&#x2019;s pose to maintain balance. In this work, we propose using a dual-arm robot to replace the human workers and develop planning and control methods for a dual-arm robot to raise a heavy plate with the help of a vacuum lifter. The methods help the robot determine its actions by considering the vacuum lifer&#x2019;s suction position and suction force limits. The essence of the methods is two-fold. First, we build a Manipulation State Graph (MSG) to store the weighted logical relations of various plate contact states and robot/vacuum lifter configurations, and search the graph to plan efficient and low-cost robot manipulation sequences. Second, we develop a velocity-based impedance controller to coordinate the robot and the vacuum lifter when lifting an object. With its help, a robot can follow the vacuum lifter&#x2019;s motion and realize compliant robot-vacuum lifter collaboration. Real-world experiments are carried out to investigate the proposed planning and control methods. The results show that a robot can effectively and flexibly work together with a vacuum lifter to manipulate large and heavy plate-like objects with the methods&#x2019; support. <i>Note to Practitioners</i>&#x2014;This paper is motivated by the vacuum lifters used for transporting heavy plates in a factory that produces building materials. In the factory, a human worker attaches the suction cup of a vacuum lifter to a plate and controls the vacuum lifter to pull the plate up. Meanwhile, another human worker moves and lifts the plate to a goal pose, following the vacuum lifter while maintaining balance. The job is dangerous as the plate is heavy, and the vacuum lifter is not always strong enough to hold the plate firmly. Inspired by the usage and safety problem, we in this paper develop a planning and control method for a dual-arm robot to replace humans. The robot coordinates its motion to work with the vacuum lifter and performs lifting tasks. The vacuum lifter could remain operated by a human worker or be actuated by signals from the dual-arm robot or other third-party machines. The work is complementary to our previous study that developed planners for robots to use pulley blocks. They together provide extensive knowledge for using low-payload collaboratively robots to manipulate heavy plates.

Optimization of fuzzy rules using neural network to control mobile robot in non-structured environment

It is still a challenge for all authors to control an autonomous mobile robot in an unstructured environment. The purpose of this paper is to propose a new control method for mobile robots in unstructured environments using neuro fuzzy technique. The proposed algorithm reduces the processing time of the fuzzy logic controller (FLC) inference engine. The neural network (NN) will therefore select the optimum rule(s) directly from the inference engine. This means that all of the rules of the inference engine do not need to be processed. As a result, the inference engine process speed will decrease, and the fuzzy logic response will increase. An actual mobile robot with three distance sensors and one virtual orientation angle is used to test the proposed algorithm. Based on the results, the mobile robot is capable of avoiding all obstacles and reaching the target point accurately.

Adaptive detumbling control of dual-arm space robot after capturing non-cooperative target

This research focuses on the detumbling planning and control method for the dual-arm space robot post-capturing a non-cooperative tumbling target, which takes the uncertainty of target’s inertial parameters and generalized input constraints of robotic system into consideration. Firstly, based on the concept of task compatibility, an efficient detumbling strategy without optimization algorithm is proposed, where the target’s desired acceleration is in the opposite direction of its velocity with the magnitude determined by scaling factor. Next, a compliance control scheme is designed to track the desired trajectory and the desired contact forces by establishing impedance control for the target and end-effectors. Finally, considering the target’s dynamic uncertainties, an adaptive controller is included to compensate the effects of uncertainty and ensure stable detumbling. The simulation results are presented for detumbling a target with inertial parameter uncertainties using a 7 degree-of-freedom dual-arm space robot, which demonstrate the effectiveness of the proposed method.

Automatic vibration control method for grasping end of flexible joint robot

Because flexible robots have flexible components such as reducers, there are problems of accuracy deviation and end vibration in the process of external interference and trajectory tracking. This leads to the proposal of a Sliding Mode Control Approach Based on RBF Neural Network (SMC-RBF) parameter optimization. This method is mainly applied to reduce the end vibration and running position error of flexible robot. Firstly, the Newton-Euler method is used to establish the dynamic model of robot considering joint flexibility. At the same time, the experiment optimizes the Sliding Mode Control (SMC) method through RBF neural network. The experiments verify the control methods of the two-joint flexible robot and the six-joint flexible robot respectively. In the control of two-joint robot, the maximum tracking curve error of SMC is only about 0.25 rad under the interference of pulse signal; And the recovery time is only about 1 s. In the control of 6-joint robot, the maximum error of RBF-sliding mode control method on XYZ axis is 0.7 mm, 0.25 mm and 1.25 mm respectively; The error on three axes is smaller than that of traditional PD control method. The results demonstrate that the tracking error of the improved mode control is small, the chattering phenomenon of the robot system is weakened as well.

Research on Speed Control Methods and Energy-Saving for High-Voltage Transmission Line Inspection Robots along Cable Downhill

To ensure the safe operation of high-voltage transmission line inspection robots during downhill descents without power and extend their range after a single charge, this paper proposes an energy-saving speed control method for the inspection robot’s walking wheel motor on downhill slopes by integrating feedback braking and fuzzy PID control. By combining the parameter equation of the overhead catenary line and the structural characteristics of the overhead transmission line, this paper analyzes the relationship between the driving torque of the inspection robot’s wheels and the horizontal displacement along the transmission ground wire before and after descending. Based on this analysis, a speed control and energy recovery scheme is developed for the inspection robot, which combines front-wheel feedback braking with rear-wheel regenerative braking. The fuzzy PID method is utilized to adjust the PWM duty cycle to achieve energy-efficient speed control of the inspection robot’s rear walking wheels. Additionally, to improve the energy density and specific power of the robot’s energy storage unit, a composite power source consisting of lithium batteries and supercapacitors is employed to recover energy from the front walking wheels through feedback braking. The combined simulation results indicate that, compared to fuzzy control and PID control, fuzzy PID control better regulates the robot’s speed under varying slopes, wind resistance, and cable roughness. A downhill speed control system for the inspection of the robot’s walking wheel motor was designed, and its effectiveness was validated through simulated high-voltage line experiments. The fuzzy PID control was demonstrated to effectively maintain the rear walking wheel speed within the targeted range during downhill descents. When descending along a fixed 30° angle cable, the fuzzy PID control resulted in an increase of 5.28% and 14.26% in the state of charge (SOC) of the supercapacitor compared to PID control and fuzzy control, respectively. Moreover, when descending along fixed angle cables of 10°, 20°, and 30°, as well as a variable angle cable ranging from 30° to 0°, the SOC of the supercapacitor increased by 17.55%, 26.25%, 38.45%, and 31.29%, respectively. This demonstrates the effective absorption of regenerative braking energy during the robot’s downhill movement.

Undulating Propulsion Underwater Robot Control Method Based on ADRC and Fuzzy Logic

The article is devoted to the development a closed-loop depth and course control algorithms for underwater robot with pair undulating fin. The controller was proposed based on the Active Disturbance Rejection Control (ADRC) technique and fuzzy logic. A brief review of the underwater robot with pair undulating fin (AUV) is carried out. The dynamic and kinematic robot model is given, and the robot model with the environment is presented in the Simscape-MATLAB library. To solve the problem of controlling course and depth of the robot, a method of Active Disturbance Rejection Control (ADRC) is proposed. This robust control method based on extension of the system model with an additional and fictitious state variable, representing everything that is not included in the mathematical description of the plant. This method allows to treat the considered system with a simpler model, since the negative effects of modeling uncertainty are compensated in real time. The advantage of the proposed method is that an exact analytical description of the system is not required, since it can be assumed that the unknown parts of the dynamics are internal noise in the installation. The fuzzy control method is used to build a non-linear relationship between controller outputs and fin parameters that determine the generated fin forces. The results of modeling of the problem of heading and depth control using a complete nonlinear dynamic model with six degrees of freedom are presented. The conducted studies confirm the operability, adequacy, and anti-disturbance ability of the ADRC controller.

Review of sEMG for Robot Control: Techniques and Applications

Surface electromyography (sEMG) is a promising technology that can capture muscle activation signals to control robots through novel human–machine interfaces (HMIs). This technology has already been applied in scenarios such as prosthetic design, assisted robot control, and rehabilitation training. This article provides an overview of sEMG-based robot control, covering two important aspects: (1) sEMG signal processing and classification methods and (2) robot control strategies and methods based on sEMG. First, the article outlines the general steps in sEMG signal processing and summarizes the commonly used methods for data acquisition, pre-processing, and feature extraction. In addition, machine-learning-based pattern recognition methods have been introduced for sEMG signal classification. Subsequently, user intent-based robot control strategies are classified into three categories: full-human continuous control, semi-autonomous continuous control, and discrete control, and their control methods and applicable scenarios are compared. Finally, this article discusses the advantages, disadvantages, and future development prospects of sEMG-based robot control. This review provides a comprehensive overview of sEMG-based robot control, from signal processing and classification methods to robot control strategies and methods, aiming to guide future research on selecting filters, feature sets, and pattern recognition methods and to assist in establishing sEMG-driven robot control frameworks.

Synchronous PD Control Using a Time Delay Estimator for a Four-Degree-of-Freedom Parallel Robot in Practice

This paper presents a synchronous proportional derivative (PD) control method using a time delay estimator (SPD-TDE) for a four-degree-of-freedom (DOF) parallel robot in practice. The proposed control is a method that is developed from a synchronous PD control method combined with a time delay estimator to guarantee the tracking objectives and synchronous requirements of the robot. Firstly, the synchronous PD control method is designed by defining cross-coupling errors. A cross-coupling error is determined by incorporating the tracking error and deviation of tracking error among two adjacent joints or synchronous errors. Then, the asynchronous problem between the kinematic chains is solved and guarantees that the goal of synchronicity is achieved. Consequently, to improve the tracking performance of the robot, a time delay estimator is used to estimate and eliminate the uncertainty components of the system, such as modeling errors and actuator faults. In addition, the Lyapunov theory is also used to demonstrate the stability and robustness of the proposed control method. Finally, a testbench 4-DOF parallel robot is built, and the controllers are embedded in the control board from MATLAB Simulink using the Waijung block set library to operate the robot preset trajectory tracking. The experimental results of the proposed control method for the 4-DOF parallel robot are compared with those obtained using other controllers to prove its effectiveness.

Manipulator Smooth Control Method Based on LSTM-XGboost and Its Optimization Model Construction

With the rapid development of computer and artificial intelligence technology, robots have been widely used in assembly, sorting, and other work scenarios, gradually changing the human-oriented mechanical assembly line working mode. Traditional robot control methods often rely on application fields and mathematical models, and they cannot meet the emerging requirements of versatility and flexibility in many fields, such as intelligent manufacturing and customized production. Therefore, aiming at the relationship between the manipulator’s smooth control command parameters and the manipulator’s actual motion stability in the multi-step object sorting task, this paper proposes a method for predicting the stability of the manipulator based on Long Short-Term Memory Extreme Gradient Boosting. The acquisition signal of the manipulator vibration is segmented according to the action, and the boost model is used to learn the relationship between the control command parameters and the stability characteristic indexes. Next, the Extreme Gradient Boosting algorithm establishes a feature index-stationarity score prediction model. The minimum Mean Absolute Error predicted by the five indicators is 0.0024 so that the model can predict the manipulator’s motion stability level according to the manipulator’s command parameters.

Convolutional Neural Network-Based Robot Control for an Eye-in-Hand Camera

In past decades, much progress has been obtained in vision-based robot control theories with traditional image processing methods. With the advances in deep-learning-based methods, convolutional neural network (CNN) has now replaced the traditional image processing methods for object detection and recognition. However, it is not clear how the CNN-based methods can be integrated into robot control theories in a stable and predictable manner for object detection and tracking, especially when the aspect ratio of the object is unknown and also varies during manipulation. In this article, we develop a vision-based control method for robots with an eye-in-hand configuration, which can be directly integrated with existing CNN-based object detectors. The task variables are generated based on parameters of the bounding box from the output of any real-time CNN object detector such as you only look once (Yolo). To address the chattering problem of bounding box, long short-term memory (LSTM) is used to provide smoothed bounding box information. A vision-based controller is then proposed following task-space motion control design formulation in order to keep the object of unknown aspect ratio in the center of field of view of the camera. The stability of the overall closed-loop control system is analyzed rigorously using the Lyapunov-like approach. Experimental results are presented to illustrate the performance of the proposed CNN-based robot controller.

Cognitive Exercise for Persons with Alzheimer's Disease and Related Dementia Using a Social Robot.

Reminiscence therapy (RT) can improve the mood and communication of persons living with Alzheimer's Disease and Alzheimer's Disease related dementias (PLWD). Traditional RT requires professionals' facilitation, limiting its accessibility to PLWD. Social robotics has the potential to facilitate RT, enabling accessible, home-based RT. However, studies are needed to investigate how PLWD would perceive a robot-mediated RT (RMRT) and how to develop RMRT for positive user experience and successful adoption. In this paper, we developed a prototype of RMRT using a humanoid social robot and tested it with 12 participants (7 PLWD, 2 with mild cognitive impairment, and 3 informal caregivers). The robot automatically displayed a memory trigger on its tablet and engaged participants in a relatable conversation during RMRT. A mixed-method approach was employed to assess its acceptability and usability. Our results showed that PLWD had an overall positive user experience with the RMRT. Participants laughed and sang along with the robot during RMRT and demonstrated intention to use it. We additionally discussed robot control method and several critical problems for RMRT. The RMRT can facilitate both verbal and nonverbal social interaction for PLWD and holds promise for engaging, personalized, and efficient home-based cognitive exercises for PLWD.

A stable control method for planar robot with underactuated constraints via motion planning and intelligent optimization

For the planar robot with underactuated constraints, a stable control method is presented on the foundation of motion planning method and intelligent optimization, which includes two stages. (1) Designing the controllers to control the actuated manipulators to given target states. (2) Planning the motion trajectory combined with the underactuated constraints between all links, using the intelligent algorithm to find the adaptable trajectory parameters, and tracking such planned trajectories to control full manipulators to the given states simultaneously. At last, multigroup simulations demonstrate the validity of the proposed method.

Reduced-order model based dynamic tracking for soft manipulators: Data-driven LPV modeling, control design and experimental results

We propose a generic nonlinear reduced-order tracking control method for elastic soft robots. To this end, a new linear parameter varying (LPV) control framework is developed using the data collected from the soft robots. Specifically, for LPV modeling we first derive a nonlinear robot model, which is large-scale by nature, using finite element methods (FEM). Then, a proper orthogonal decomposition (POD) algorithm is used to generate a set of linearized reduced-order models, representing the local behaviors of the soft robot at different operating points within the workspace. Via a unified POD projector, not only the order of these linearized models can be significantly reduced but also their mechanical structure and stability properties can be preserved for LPV modeling and control design. Next, using radial basis function (RBF) networks, we propose an iterative training method to build the LPV robot model by interpolating a subset of selected linearized models with a specified interpolation error. For LPV control design, the equivalent-input-disturbance (EID) concept is exploited to develop a dynamic tracking control scheme, which is composed of three core components: feedforward control, disturbance-estimator control and feedback control. The feedforward control is designed to account for the effects of the trajectory reference and the time-varying affine term, issued from the FEM-based model linearization. The disturbance-estimator control is obtained from a generalized proportional integral LPV observer, which also provides the estimated reduced-order states for feedback control. The observer-based feedback control design is reformulated as a convex optimization problem under linear matrix inequality (LMI) constraints. The globally uniformly ℓ∞ stability of the closed-loop LPV robot model is guaranteed by means of Lyapunov stability theory. Experimental tests are conducted with a soft Trunk robot, inspired by the elephant trunk, under several scenarios with small and large deformations to show the effectiveness of the proposed LPV tracking control framework. A comparative study is also performed with a recent linear EID-based controller and an iterative learning controller to emphasize the interests this nonlinear control method for soft elastic robots. This paper is complemented with a series of demonstration videos: https://bit.ly/3D8C4Vd.

Control technologies of lower limb rehabilitation exoskeleton robots based on surface electromyography: A review

The aging population is accompanied by a decline in human body function, leading to an increasing number of people with lower limb dysfunction, which has become a global public health challenge today. The lower limb rehabilitation exoskeleton robot based on surface electromyography is a current research hotspot. It can help people with lower extremity dysfunction perform better rehabilitation training. This review presents the analysis and processing of surface electromyography, feature extraction and recognition, as well as the control methods for lower limb rehabilitation exoskeleton robots.