Manipulator Arm Research Articles

Time-optimal trajectory planning for 6R manipulator arm based on chaotic improved sparrow search algorithm

Purpose Trajectory planning is a core aspect of manipulator operation, directly influencing its performance. This paper aims to introduce a chaotic improved sparrow search algorithm (CISSA) to optimize hybrid polynomial-interpolated trajectories, enhancing the efficiency and precision of manipulator trajectory planning. Design/methodology/approach The proposed approach leverages 3-5-3 polynomial interpolation to construct the motion trajectory of a 6R manipulator. To optimize the trajectory over time, the sparrow search algorithm is enhanced with chaotic mapping, a discoverer dispersion strategy, positional limiting mechanisms and Brownian motion. These enhancements collectively reduce the manipulator’s runtime while meeting operational requirements. Findings The proposed method was applied to the AUBO-i5 robot to evaluate its performance. Simulation results demonstrate that CISSA effectively avoids local optima and achieves more accurate solutions compared to similar algorithms. By integrating CISSA into trajectory planning, the robot’s movement time was reduced by 13.99% compared to the original SSA, and the number of algorithm iterations was significantly decreased, ensuring smoother and more efficient task execution in real production. Originality/value A CISSA is proposed and applied to the optimal time trajectory planning of the manipulator, verifying the effectiveness and superiority of the algorithm. Experimental results show that CISSA outperforms comparable algorithms by several orders of magnitude in solving manipulator inverse kinematics, significantly enhancing planning efficiency and reducing trajectory planning time.

Time-Variation Damping Dynamic Modeling and Updating for Cantilever Beams with Double Clearance Based on Experimental Identification

The accuracy of a space manipulator’s end trajectory and stability is significantly affected by joint clearance. Aiming to improve the prediction accuracy of vibration caused by clearance, a dynamic clearance modeling method is developed based on parameter identification in this study. First, a dynamic model framework for manipulator arms is established based on the Hamilton principle and hypothetical mode method with time-variation damping. Then, a multi-resolution identification is performed for identifying the instantaneous frequency and damping ratio to estimate stiffness and damping by the sensors. The quantum genetic algorithm (QGA) is used to optimize the scale factor, which determines the identification accuracy and calculation efficiency. Finally, a case study is conducted to verify the presented model. In comparison with the initial dynamic model based on constant damping, the modal assurance criterion (MAC) of the proposed improved model based on time-variation damping is improved by 43.97%, the mean relative error (MRE) of the frequency response function (FRF) is reduced by 32.6%, and the root mean square error (RMSE) is reduced by 18.19%. The comparison results indicate the advantages of the proposed model. This modeling method could be used for vibration prediction in control systems for space manipulators to improve control accuracy.

Velocity Analysis of a Slider Crank Mechanism for Delta Robot Arm Manipulation: A Computational Approach

In this research, a velocity analysis of the slider crank mechanism's (SCM) for the delta robot arm manipulation was carried out. For the slider crank mechanism, new velocity response models were created. Trigonometric and inverse trigonometric functions are used in the novel Akozietic mathematical method, which is appropriate for kinematic analysis of intricate mechanisms like the four-bar mechanism (slider crank). The equilibrium conditions of the forces in a four-bar mechanism are used in the Akozietic mathematical technique to create simple mathematical models that enable user-written computer programs in Matlab and other programming languages. According to the velocity profile, the mechanism is under the most stress when the crank angle exceeds 300°, and the velocity is lowest at 180° and 0°.The crank angles where the slider shifts direction are the locations where the velocity passes zero. The novel mathematical procedure created in this study outperformed the other two kinematic analysis methods that were currently in use, according to a comparison of spreadsheets and this new mathematical method for mechanism analysis (Akozietic) with standard numerical solutions completed in Mathematica.

Fast adaptive unknown input observer for fault and attack estimations in nonlinear systems: an enhanced LMI approach

This paper deals with the problem of robust fault estimation for the Lipschitz nonlinear systems under the influence of sensor faults and actuator faults. In the proposed methodology, a descriptor system is formulated by augmenting sensor fault vectors with the states of the system. A novel fast adaptive unknown input observer (FAUIO) structure is proposed for the simultaneous estimation of both faults and states of a class of nonlinear systems. A new LMI condition is established by utilising the H ∞ criterion to ensure the asymptotic convergence of the estimation error of the developed observer. This derived LMI condition is deduced by incorporating the reformulated Lipschitz property, a new variant of Young inequality, and the well-known linear parameter varying (LPV) approach. It is less conservative than the existing ones in the literature, and its effectiveness is compared with the other proposed approaches. Further, the proposed observer methodology is extended for the disturbance-affected nonlinear system for the purpose of the reconstruction of states and faults/attacks with optimal noise attenuation. Later on, both designed approaches are validated through an application of a single-link robotic arm manipulator in MATLAB Simulink.

Hybrid robust disturbance rejection position-force control to adjust the motion of mobile manipulator in automatic aluminum foundry recycling industries

ABSTRACT This study presents the development of an event-driven hybrid control for position and force tracking applied on a mobile robotic manipulator for metal recycling tasks. The suggested controller operates in a sequenced strategy starting from a fixed spot, moving the mobile device towards a targeted zone ( Ω i r ) from where the i-th piece-to-be-recycled is attainable (considering the arm manipulation). Once the event of entering the zone is completed, the mobile robot is fixed at a position, and the end-effector of the robotic arm is enforced towards the piece-to-be-recycled. When the end-effector touches the piece in a given spot ( Ω i e ), the hybrid control changes to the force tracking intending to carry the piece towards the spot ( Ω i p ) where it ill be processed. Each piece location is identified based on a vision-based system that applies deep learning tools using convolutional neural networks. A multi-physics numerical simulation illustrated the application of the developed controller in a realistic scenario, showing all the elements of the event-driven operation. To validate the suggested controller, the comparison with a robust control that works on a wide range of carrying mass confirms the operational improvement of the event-driven hybrid position and force design.

Methodology for performance assessment of industrial robots

The study of industrial robots is particularly interesting in view of the many advantages that these robots offer in the production line. This paper aims to study manipulator arms in order to better understand the performance of manipulator arms. The recurring issue for the manipulator arms remains the analysis of their operating path patterns in a well-defined working space. We propose a methodology based, on the one hand, on the definition of the functional specifications of manipulator arms necessary for design and on the other hand, on the geometric modelling and control of these manipulator arms in Matlab/Simulink. The model thus constructed is capable of reproducing different trajectory configurations depending on the joint variables in a well-defined working space. The results of our model are consistent with those provided by the mathematical model using the Denavit-Hartenberg convention. According to two scenarios, the analysis of the trajectories of the manipulator arms’s end effector is carried out and especially on the SCARA, cartesian and spherical robots. This analysis generally reveals elliptic trajectories or lines described by the end effector of robots in relation to the plane linked to their base.

An Aircraft-Manipulator System for Virtual Flight Testing of Longitudinal Flight Dynamics

A virtual flight test is the process of flying an aircraft model inside a wind tunnel in a manner that replicates free-flight. In this paper, a 3-DOF aircraft-manipulator system is proposed that can be used for longitudinal dynamics virtual flight tests. The system consists of a two rotational degrees-of-freedom manipulator arm with an aircraft wind tunnel model attached to the third joint. This aircraft-manipulator system is constrained to operate for only the longitudinal motion of the aircraft. Thus, the manipulator controls the surge and heave of the aircraft whilst the pitch is free to rotate and can be actively controlled by means of an all-moving tailplane of the aircraft if required. In this initial study, a flight dynamics model of the aircraft is used to obtain dynamic response trajectories of the aircraft in free-flight. A model of the coupled aircraft-manipulator system developed using the Euler method is presented, and PID controllers are used to control the manipulator so that the aircraft follows the free-flight trajectory (with respect to the air). The inverse kinematics are used to produce the reference joint angles for the manipulator. The system is simulated in MATLAB/Simulink and a virtual flight test trajectory is compared with a free-flight test trajectory, demonstrating the potential of the proposed system for virtual flight tests.

Integrated sliding mode control of robot manipulator based on fuzzy adaptive RBF

To solve the uncertainty of the parameters of the manipulator dynamics model, the control accuracy and convergence rate of the system affected by the joint friction and external interference, a compound control strategy based on the manipulator dynamics model is proposed. Firstly, a modified power-of-two convergence law is used and combined with an integral sliding mode to design a sliding mode control term to shorten the convergence of the tracking error. Secondly, the approximations of the uncertain variables of the dynamical model are accomplished by using the three sets of RBF neural networks and introducing an adaptive mechanism for online self-tuning of weights, the approximation errors of the RBF neural networks are compensated by using the sliding-mode control term designed in the previous section. Finally, the fuzzy controllers are utilized to calculate the coupled joint friction and outside disturbances. The simulation works show that comparing with the chunked RBF neural network to approximate the sliding mode control logy, the proposed hybrid control theory reduces the mechanical arm joint angular rate response time by 39.4%, the largest solid-state error was cut by 76.8%, and the medium-sized solid-state error was cut by 62.7%, improved control preciseness and the responsiveness of the spatial trajectory tracking of the manipulator arm's joints.

Integrating artificial immune systems and multi-layer perceptron-biogeography-based optimization for enhanced inverse kinematics in robotic arm

Determining required joint angles to achieve a desired position in a manipulator’s arm is a complicated problem without simple analytical solutions. This paper researches several computational methods based on artificial intelligence (AI) for calculating the joint positions of the 6-DOF robotic arm. We can extrapolate relevance, for example, to the crucial role that robotic manipulator arms play in industrial and medical applications, where enhanced precision and movement efficiency may sharply boost performance and expand applicability. Here, we investigate the effectiveness of methods, such as the artificial immune system (AIS) and multi-layer perceptron-biogeography-based optimization (MLP-BBO). Those AI-driven methods have been applied to determine joint angles for reaching desired positions through simulations for the robotic arm. The results show that the AIS and MLP-BBO approach can handle the intrinsic complexities of the task, thus testifying to the practicability and dependability of these two methods in this application. From the findings in the study, it was indicated that AI-driven techniques can effectively answer the complex problem of the robotic manipulator arm in finding joint angles.

An intelligent emulsion explosive grasping and filling system based on YOLO-SimAM-GRCNN

For the blasting scenario, our research develops an emulsion explosive grasping and filling system suitable for tunnel robots. Firstly, we designed a system, YOLO-SimAM-GRCNN, which consists of an inference module and a control module. The inference module primarily consists of a blast hole position detection network based on YOLOv8 and an explosive grasping network based on SimAM-GRCNN. The control module plans and executes the robot’s motion control based on the output of the inference module to achieve symmetric grasping and filling operations. Meanwhile, The SimAM-GRCNN grasping network model is utilized to carry out comparative evaluated on the Cornell and Jacquard dataset, achieving a grasping detection accuracy of 98.8% and 95.2%, respectively. In addition, on a self-built emulsion explosive dataset, the grasping detection accuracy reaches 96.4%. The SimAM-GRCNN grasping network model outperforms the original GRCNN by an average of 1.7% in accuracy, achieving a balance between blast holes detection, grasping accuracy and filling speed. Finally, experiments are conducted on the Universal Robots 3 manipulator arm, using distributed deployment and manipulator arm motion control mode to achieve an end-to-end grasping and filling process. On the Jetson Xavier NX development board, the average time consumption is 119.67 s, with average success rates of 87.1% for grasping and 79.2% for filling emulsion explosives.

Innovative Solutions for IK: PROA and Clonal Selection Algorithms Unveiled

Calculating joint angles for sequential manipulators consists of studying the correlation between Cartesian and joint variables. The problem-solving technique encounters two main hurdles described as direct and inverse kinematics. Matrix multiplications usually simplify the direct kinematic problem. However, inverse kinematic problems are harder as they require solving many nonlinear equations and eliminating variables a lot. In our work, we introduce two new methods of handling the complicated inverse kinematic problem for robotic manipulator arms; Poor and Rich Optimization Algorithm and Clonal Selection Algorithm (CSA). These advanced techniques enhance greatly the estimation of various joints in the arm which makes the solution more precise and efficient. To demonstrate the effectiveness, robustness, and potential benefits of these approaches for complicated kinematic problems we present extensive simulation results thereby enabling better performance of robots.

Robot arm grasping based on YOLOv5 in the perspective of automated production

Abstract The rapid development of industrial intelligence has gradually expanded the application of automated production. As a typical automated production equipment, the robotic arm still faces the problems of low grasping efficiency and high control costs when facing highly integrated and miniaturized components. Given this, to improve the grasping level of the robotic arm in complex production environments, this study first constructs a kinematic mathematical model of the robotic arm. Secondly, based on the algorithm that You Only Look Once, improvements are made to its convolution operation and feature extraction modules, ultimately proposing a new type of robotic arm grasping control model. The results showed that the loss test value of the new model was the lowest at 2.75, the average detection error of the captured object was the lowest at 0.003, and the average detection time was the shortest at 1.28 seconds. The highest success rate for grasping six types of industrial parts was 94%, and the lowest average energy consumption was 35.67 joules. Therefore, research models can significantly improve the grasping performance of robotic arms under various complex conditions, thereby achieving efficient manipulation of robotic arms in industrial automation.

Model predictive control for space robot manipulator modeled by switched systems: A data‐driven approach

AbstractThis article investigates the data‐driven based model predictive control (MPC) problem for a class of space robot manipulator (SRM). First, the SRM system is modeled as a class of discrete‐time switched system to reflect more system characteristics. Second, only the input‐state data are used for end‐to‐end controller design, and the system identification process is avoided. In order to meet the real‐time requirements of the manipulator arm angles, a kind of constrained predictive control method is designed to prevent the overheating or damage of SRM motor, optimizing the cost function for each decision cycle under the safe control input constraints. Then, based on the average dwell time method, the asymptotic stability is assured for the presented SRM system. The design conditions for data‐driven based MPC are provided in the form of linear matrix inequalities. Finally, the effectiveness and advantage for the developed data‐driven based MPC strategy are validated via a case study of SRM system.

Practical Probabilistic Model-Based Reinforcement Learning by Integrating Dropout Uncertainty and Trajectory Sampling.

This article addresses the prediction stability, prediction accuracy, and control capability of the current probabilistic model-based reinforcement learning (MBRL) built on neural networks. A novel approach to dropout-based probabilistic ensembles with trajectory sampling (DPETS) is proposed, where the system uncertainty is stably predicted by combining the Monte Carlo dropout (MC Dropout) and trajectory sampling in one framework. Its loss function is designed to correct the fitting error of neural networks for more accurate prediction of probabilistic models. The state propagation in its policy is extended to filter the aleatoric uncertainty for superior control capability. Evaluated by several Mujoco benchmark control tasks under additional disturbances and one practical robot arm manipulation task, DPETS outperforms related MBRL approaches in both average return and convergence velocity while achieving superior performance than well-known model-free baselines with significant sample efficiency. The open-source code of DPETS is available at https://github.com/mrjun123/DPETS.

Exploring artificial neural networks for the forward kinematics of a SCARA robotic manipulator using varied datasets and training optimizers

Abstract Although analytical methods are traditionally employed, the solution to the Forward Kinematics (FK) problem for Selective Compliance Assembly Robot Arm (SCARA) manipulator robots can prove intricate and computationally demanding. Recognizing this challenge, this study endeavors to introduce an intelligent approach by leveraging Artificial Neural Networks (ANNs) to address the FK problem specifically tailored for a four-degree-of-freedom (4-DoF) SCARA robot. To train the ANNs, we employ three distinct datasets, one with a fixed step size, one with a random step size, and one based on a sinusoidal signal. Moreover, the objective is to scrutinize the ANNs performance under the influence of three distinct training algorithms: Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG). Through a systematic comparison of various ANN models, diverse training algorithms, and the three chosen datasets, the investigation reveals that optimal Mean Squared Error (MSE) results are achieved with random step size datasets for models with two hidden layers using the LM algorithm (MSE = 8.6099e-05). For the BR algorithm, the best MSE (8.0535e-05) was obtained with sinusoidal datasets and three hidden layers. For the SCG algorithm, the optimal MSE (1.1144e-04) was achieved with fixed step size datasets and one hidden layer. The accuracy of the ANN model is significantly influenced by the dataset, the choice of training optimizer, and the configuration of hidden layers. Consequently, further research could explore hybrid approaches that integrate evolutionary algorithms to leverage their respective strengths and improve overall ANN model performance.

Robust Adaptive Sliding Mode Control Using Stochastic Gradient Descent for Robot Arm Manipulator Trajectory Tracking

This paper presents an innovative control strategy for robot arm manipulators, utilizing an adaptive sliding mode control with stochastic gradient descent (ASMCSGD). The ASMCSGD controller significant improvements in robustness, chattering elimination, and fast, precise trajectory tracking. Its performance is systematically compared with super twisting algorithm (STA) and conventional sliding mode control (SMC) controllers, all optimized using the grey wolf optimizer (GWO). Simulation results show that the ASMCSGD controller achieves root mean squared errors (RMSE) of 0.12758 for θ1 and 0.13387 for θ2. In comparison, the STA controller yields RMSE values of 0.1953 for θ1 and 0.1953 for θ2, while the SMC controller results in RMSE values of 0.24505 for θ1 and 0.29112 for θ2. Additionally, the ASMCSGD simplifies implementation, eliminates unwanted oscillations, and achieves superior tracking performance. These findings underscore the ASMCSGD’s effectiveness in enhancing trajectory tracking and reducing chattering, making it a promising approach for robust control in practical applications of robot arm manipulators.

A Hierarchical Control Method for Trajectory Tracking of Aerial Manipulators Arms

To address the control challenges of an aerial manipulator arm (AMA) mounted on a drone under conditions of model inaccuracy and strong disturbances, this paper proposes a hierarchical control architecture. In the upper-level control, Bézier curves are first used to generate smooth and continuous desired trajectory points, and the theory of singular trajectory lines along with a Radial Basis Function Neural Network (RBFNN) is introduced to construct a highly accurate multi-configuration inverse kinematic solver. This solver not only effectively avoids singular solutions but also enhances its precision online through data-driven methods, ensuring the accurate calculation of joint angles. The lower-level control focuses on optimizing the dynamic model of the manipulator. Using a Model Predictive Control (MPC) strategy, the dynamic behavior of the manipulator is predicted, and a rolling optimization process is executed to solve for the optimal control sequence. To enhance system robustness, an RBFNN is specifically introduced to compensate for external disturbances, ensuring that the manipulator maintains stable performance in dynamic environments and computes the optimal control commands. Physical prototype testing results show that this control strategy achieves a root mean square (RMS) error of 0.035, demonstrating the adaptability and disturbance rejection capabilities of the proposed method.

The supernumerary robotic limbs of brain-computer interface based on asynchronous steady-state visual evoked potential

Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) have attracted much attention in the field of intelligent robotics. Traditional SSVEP-based BCI systems mostly use synchronized triggers without identifying whether the user is in the control or non-control state, resulting in a system that lacks autonomous control capability. Therefore, this paper proposed a SSVEP asynchronous state recognition method, which constructs an asynchronous state recognition model by fusing multiple time-frequency domain features of electroencephalographic (EEG) signals and combining with a linear discriminant analysis (LDA) to improve the accuracy of SSVEP asynchronous state recognition. Furthermore, addressing the control needs of disabled individuals in multitasking scenarios, a brain-machine fusion system based on SSVEP-BCI asynchronous cooperative control was developed. This system enabled the collaborative control of wearable manipulator and robotic arm, where the robotic arm acts as a "third hand", offering significant advantages in complex environments. The experimental results showed that using the SSVEP asynchronous control algorithm and brain-computer fusion system proposed in this paper could assist users to complete multitasking cooperative operations. The average accuracy of user intent recognition in online control experiments was 93.0%, which provides a theoretical and practical basis for the practical application of the asynchronous SSVEP-BCI system.

Motion planning method and experimental research of medical moxibustion robot of double manipulator arms

Motion planning method and experimental research of medical moxibustion robot of double manipulator arms

Soft Robotic System with Continuum Manipulator and Compliant Gripper: Design, Fabrication, and Implementation

This paper presents the design, construction, and implementation of a soft robotic system comprising a continuum manipulator arm equipped with a compliant gripper. Three main objectives were pursued: (1) developing a soft silicone gripper as an alternative to expensive and rigid steel grippers, enabling safe and precise handling of delicate or irregular objects such as fruits, glassware, and irregular shapes; (2) fabricating a continuum manipulator arm with robotic joints inspired by vertebrae, allowing for smooth, non-linear motion and more excellent maneuverability compared to traditional rigid arms, enabling access to hard-to-reach areas; and (3) integrating the compliant gripper with the continuum manipulator and implementing a control system for the soft gripper and remote bending arm using a microcontroller. The soft gripper, manipulator arm vertebrae, and other components were fabricated using 3D printing with PLA material for the molds. The gripper construct used hyperelastic silicone (Ecoflex 00.30). The continuum manipulator achieved a higher degree of freedom and mobility, while simulations and experiments validated the design’s effectiveness. The comparison shows that the close agreements differ by only 2.5%. In practical experiments involving lifting objects, the gripper was able to carry items with a greater mass. The proposed soft, integrated robotic system outperformed traditional rigid approaches, offering safe and flexible handling capabilities in unstructured environments. The nature-inspired design enabled a compliant grip and enhanced maneuverability, making it suitable for various applications requiring dexterous manipulation of delicate or irregularly shaped objects.