Simulation and command of adam robot arm subjected to 3D external force

Robots are used to provide help in unsafe, repetitive and dreary situations. This paper concentrates on a robotic arm which can be utilized for crossing of the dead wire structure to the jumper cable of an electrical transmission line. In order to achieve a good positional accuracy of the end-effector, the robotic arm is modeled and analyzed. Denavit-Hartenberg (DH) representation is used for the kinematic modeling and Lagrangian-Euler (LE) method for the dynamic modeling. Since the kinematic and dynamic modeling of robotic arm is highly nonlinear in nature, the optimum trajectory planning is a challenging issue in almost all obstacle avoidance problems. In trajectory planning, the desired trajectory needs to be followed for end-effector positioning. This paper presents kinematic modeling, dynamic modeling, workspace analysis and trajectory planning of a 5 Degree of freedom (DOF) robotic arm. The robotic arm is designed using SolidWorks 2014 software. The complete analysis of the proposed manipulator is simulated using MATLAB Software.

<p>The accelerated growth of an increasingly automated industry requires the use of autonomous robotic systems. However, the use of these systems commonly requires an enormous amount of sensors. In this paper we evaluate the performance of a new system for visual control of a selective compliance assembly robot arm (SCARA) robotic arm using a monocular depth map that only requires one monocular camera. This system aims to be an efficient alternative to reduce the number of sensors in the robotic arm area while maintaining the effectiveness of traditional vision algorithms that use stereoscopic architectures of cameras. For this purpose, this system is compared with representative state-of the-art vision algorithms focused on the control of robotic arms. The results are statistically analyzed, indicating that the algorithm proposed in this research has competitive performance compared to state-of-the-art robotic arm visual control algorithms only using a single monocular camera.</p>

Kinematic modeling and control of a robot arm using unit dual quaternions

This paper is concerned with the identification problem of two degree of freedom robot arm’s joints’ time-varying stiffness under time-varying torque loads. The industrial robot arms can be simplified into series hinged linkage mechanism. The unrestraint dynamic equation of two degrees of freedom robot arm can be obtained by using analytical mechanics method. The model which was established in Pro/E had been imported into ADAMS for simulation. And the simulation consisted of free vibration and forced vibration. Then by choosing limited memory least square method and using the post-processing measured variables, time-varying stiffness could have been identified. Finally, the calculative stiffness was compared to the “real” stiffness which was simulated in ADAMS. The whole process shows that the robot arm’s dynamic model and the method of identification are both effective. DOI: http://dx.doi.org/10.11591/telkomnika.v10i8.1644 Full Text: PDF

This paper presents research on the integrated dynamics and control methods of a multi-joint robotic arm system installed on a spatial inertial platform. An integrated recursive dynamic model of the posture of the multi-joint robotic arm is established by using the method of dual quaternions. This model can succinctly describe the position and attitude relationships among the joints of the robotic arm. Based on this model, a sliding mode variable structure controller is designed, which can achieve convergence of position and attitude errors even in the presence of external disturbances. The designed controller is validated through simulations, the results of which demonstrate satisfactory control performance, offering reference value for practical engineering applications.

The present study proposes a robot arm that can support precise positioning in operation by a human. The robot arm has passive and actuated joints. The passive joint is not mechanically constrained and can move passively with the motion of the output point of the robot arm. Therefore, a human can move the output point of the robot arm by his will. When a human tries to move the output point along an object path, the displacement of the passive joint is determined passively. Then, if the actuated joint is controlled so that the output point may be on the object path, the human can achieve precise positioning. Namely, using the proposed robot arm, the human can adjust the velocity, acceleration, applied force and so on by his will, while the position is preciously compensated by the actuated joint. This paper describes a mechanical form of such robot arm for drawing of planar figures and shows that the robot arm has specific singularity condition. In order to avoid the singularity condition, the proposed robot arm interchanges the actuated joint and the passive one situationally. The study fabricated the two degrees of freedom robot arm for positioning assistance and described its control method. Furthermore, its availability is confirmed by drawing some figures.

This article addresses the problem of control of robotic arm with a hysteretic joint behaviour. The mechanical design of the one-degree of freedom robotic arm is presented where the joint is actuated by a Shape Memory Alloy (SMA) wire. The SMA wire based actuation of the joint makes the robotic arm lightweight but at the same time introduces hysteresis type nonlinearities. The nonlinear dynamic model of the robotic arm is introduced and an Adaptive control solution is presented to perform the joint reference tracking in the presence of unknown hysteresis behaviour. The Lyapunov stability analysis of the closed loop system is presented and finally proposed adaptive control solution is validated through simulation study on the proposed nonlinear hysteretic robotic arm.

The paper focuses on design and control of a new anthropomorphic robot arm enabling the torque measurement in each joint to ensure safety while performing tasks of physical interaction with human and environment. When the contact of the arm with an object occurs, local impedance algorithm provides active compliance of corresponding robot arm joint. Thus, the whole structure of the manipulator can safely interact with unstructured environment. A novel variable control strategy was elaborated to increase the robot functionality and to achieve human-like dynamics of interaction. In the paper, we detail the design procedure of 4-DOF robot arm and optical torque sensors. The experimental results of variable joint impedance control show that proposed approach not only provides safe interaction of entire structure of robot arm with a person, but also improves the effectiveness of contact task performance, enables thus to contact with environment in a delicate manner.

Trajectory optimal control for a robotic arm with a high degree of freedom (DOF) is challenging. The design space for that problem is complex and the search for an optimal solution is demanding. The design of a robotic arm's trajectory is based on solving the inverse kinematics problem, considering additional refinements influenced by factors like total rotating angle, reachability time, minimum execution time, obstacle avoidance, and energy consumption minimization. Due to the complexity of the design space, in this paper, genetic algorithm (GA) optimization and whale optimization algorithm (WOA) have been used to achieve robotic arm trajectory control while maintaining a minimum reachability time. To validate the suggested techniques, a case study was conducted on a 6 DOF KUKA KR 4 R600 robot arm to control subject to its constraints. Sets of consecutive points forming four different paths were inputted to the algorithms. The goal was to reach all these points, in order, with a minimum total reachability time. As a result of this paper, we shown that the whale optimization algorithm provides better performance than the genetic algorithm with a factor of more than 2.5 while satisfying the reachability constraints.

Industry 4.0 and digital factories leverage key technologies such as virtual reality (VR) to design, simulate, optimize, and interact with physical production systems remotely or collaboratively. Recent advancements in industrial and aerospace applications demonstrate that the integration of VR with versatile robotic control techniques can unlock new opportunities. VR offers an "extended arm" to physical environments by providing a user-friendly human-machine interface, enhancing sustainability, testability, and more in robotic control systems. This research focuses on a widely used and continuously evolving robotic arm, designed to function in an integrated system operating in both real and virtual environments. A 3-degree-of-freedom (3DOF) robotic arm was initially developed and later upgraded to a 5DOF system. The robotic arm integrated with Virtual Reality (VR) interfaces based on the Robot Operating System (ROS) and Unity3D platforms. In the real-world scenario, inverse kinematics applied for the robot's movement mechanism, while gradient descent algorithms used for the virtual model. Forward kinematics using homogeneous transformation matrices employed to verify the accuracy of the robotic arm's movements. VR headsets and controllers used to manage the virtual robotic arm's control. The goal of this study is to develop integrated systems that enable control of real-world operations via remote observation, addressing challenges like prototyping costs and safety risks. By offering an innovative approach to robotic arm control through VR integration, this work contributes to the advancement of Industry 4.0 technologies. The findings suggest potential applications across various industries, opening up new possibilities for remote operation and training in complex manufacturing environments. DOI: 10.61416/ceai.v28i1.9592

Brain-computer interface (BCI) has great potential to replace lost upper limb function. Thus, there has been great interest in the development of BCI-controlled robotic arm. However, few studies have attempted to use noninvasive electroencephalography (EEG)-based BCI to achieve high-level control of a robotic arm. In this paper, a high-level control architecture combining augmented reality (AR) BCI and computer vision was designed to control a robotic arm for performing a pick and place task. A steady-state visual evoked potential (SSVEP)-based BCI paradigm was adopted to realize the BCI system. Microsoft's HoloLens was used to build an AR environment and served as the visual stimulator for eliciting SSVEPs. The proposed AR-BCI was used to select the objects that need to be operated by the robotic arm. The computer vision was responsible for providing the location, color and shape information of the objects. According to the outputs of the AR-BCI and computer vision, the robotic arm could autonomously pick the object and place it to specific location. Online results of 11 healthy subjects showed that the average classification accuracy of the proposed system was 91.41%. These results verified the feasibility of combing AR, BCI and computer vision to control a robotic arm, and are expected to provide new ideas for innovative robotic arm control approaches.

With the increasing demand for high-precision dynamic interaction tasks in industrial automation, the 7 DDF robotic arm faces the dual challenges of insufficient kinematic modeling accuracy and inadequate end-effector force compliance in complex interaction tasks. This paper focuses on the Realman RM75 seven-degree-of-freedom robotic arm as the subject of study, enhancing its kinematic modeling through an Modified Denavit-Hartenberg (D-H) parameterization method and arm shape angle parameterization technique. Furthermore, it achieves force-compliant control of the robotic arm based on an admittance control strategy. First, a kinematic model of the robotic arm is developed using a Modified D-H parameter approach combined with an arm angle parameterization method to derive a closed-form solution for inverse kinematics. Second, an admittance control framework is designed to adjust the end-effector pose in real time via a virtual spring-damping system, facilitating compliant control in multi-dimensional force interaction scenarios. The experimental results demonstrate the designed control system can achieve good force compliance control at the end of the robotic arm under the action of external contact forces.

KARES (KAIST Rehabilitation Engineering System) is a rehabilitation robotic system with 6 degrees of freedom robot arm mounted on the powered wheelchair, in order to assist the disabled and the elderly for independent livelihood. Human machine interaction is very important in this system. The direct control of the robot arm takes a high cognitive load on the user's part while physically disabled persons may have difficulties operating joysticks or push buttons for delicate movements. Therefore, a certain degree of autonomy of the robotic system is needed. To perceive the environment, color vision and force/torque sensors are mounted on the end-effector of the robotic arm of KARES. Four basic tasks are tested: picking up a cup from a table, picking up a pen from the floor, moving an object to the user's face, and operating a switch on a wall. These tasks are performed autonomously in the semi-structured environment.

This paper introduces the isokinetic measurement in randomly applied on the upper arms spasticity patient. Isokinetic is the stroke patient exercise with assist by the medical robotic technologies to therapeutic, diagnostic and laboratory assessment in constant movement. The fatigue of the spasticity patient is ideally vital to understand to make sure the robotic arms is not over drive during exercise. The repetition exercise may cause of medially fatigue. The model propose can predict early fiber muscle action and effect of a range of pulse durations on muscle fatigue. The Spasticity robotic arms was introduced to maximum in medially fatigue can cause damage of the fiber muscle tissue. Flexor Carpi Ulnaris (FCU), Palmanis Longus (PL), Extensor Digitorum (ED) and Extensor Carpi Radials (ECR) is the important muscle body to analyze. The Spasticity Robot Arms (SRA) was design to assists stroke patient in order to understand the capability of user and to provide high impact rehabilitation exercise. The goal of this research is to propose suitable algorithm in order to control multiple degree of freedom (DOF) to achieve a limited number of pre-defined motor synergies. A set of experiments will be conducted on four number of subjects to verify the Fugl Meyer assessment of the proposed algorithm.This paper introduces the isokinetic measurement in randomly applied on the upper arms spasticity patient. Isokinetic is the stroke patient exercise with assist by the medical robotic technologies to therapeutic, diagnostic and laboratory assessment in constant movement. The fatigue of the spasticity patient is ideally vital to understand to make sure the robotic arms is not over drive during exercise. The repetition exercise may cause of medially fatigue. The model propose can predict early fiber muscle action and effect of a range of pulse durations on muscle fatigue. The Spasticity robotic arms was introduced to maximum in medially fatigue can cause damage of the fiber muscle tissue. Flexor Carpi Ulnaris (FCU), Palmanis Longus (PL), Extensor Digitorum (ED) and Extensor Carpi Radials (ECR) is the important muscle body to analyze. The Spasticity Robot Arms (SRA) was design to assists stroke patient in order to understand the capability of user and to provide high impact rehabilitation exercise. The goal ...

To solve the problems of response lag and limited generalization of a single algorithm in traditional control methods for robotic arm trajectory tracking, a hierarchical control architecture is proposed. The upper layer adopts a soft actor critic algorithm optimized by multi actor critics to generate robust trajectories. The lower layer achieves precise tracking through the sparrow search algorithm proportional integral derivative feedforward compensation controller. The results show that the improved soft actor critic algorithm achieves average reward values of 1165 and 1087 in obstacle free scenes, with success rates of 88.65% and 83.54%, respectively, which are superior to other compared algorithms. Meanwhile, the root mean square error of the algorithm in different scenarios is only 0.18 rad/s and 0.24 rad/s, respectively, and the single trajectory time is only 0.68 s and 0.77 s, achieving high-precision and high-efficiency robust trajectory generation. In addition, the sparrow search algorithm proportional integral derivative feedforward compensation controller has the highest degree of joint angle tracking coincidence, and its Z-axis contact force fluctuates around the expected value, with a lower fluctuation range than other methods, ranging from 6.78 N-7.36 N in scenario 1 and 6.52 N-8.13 N in scenario 2. This architecture achieves the synergy of global optimization and precise tracking, suitable for 3-6 axis serial industrial robotic arms, providing important reference for high-precision trajectory tracking of multi degree of freedom robotic arms.