Task driven feedback control of robot arms: A step toward intelligent control

The paper concerns with the intelligent control of an over-actuated robotic arm. First, it will be presented the original mechanical design of the redundant, cable-driven robot arm with 7 degrees of freedom, whose prototype is being built. Then, it will be synthesized, at the high-control level, an intelligent fuzzy controller that manages activation of the redundant number of robot arm actuators. Structure of the fuzzy controller is given in the paper. Then, the intelligent controller will be applied in the frame of a mathematical model of the considered robotic arm. Performance verification of the synthesized intelligent controller of robotic arm will be accomplished by means of several simulation experiments. Corresponding results are presented and analyzed at the end of the paper. Future directions of research and development in this field will be emphasized as well as the expected results and applications to be planned.

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.

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

1. This research aims to study the edge computing devices with a faster processing and signal connecting function based on development of the physical layer and magnetic sensing devices. It is important matter for smart robot in multi-axis motor control. Based on the variable robot properties, it includes humanoid robots, wheeled machines, and fixed robots, respectively, are contented. This research is constructed as follows: to establishing an edge computing device system, the image vision system with neural-network learning theory to control robot arm path, multiple magnetic sensors in physical layer to analyze the signals of the motor, voltage, current, temperature, etc., are transmitted signal to the edge computing device and IoTs computing server system.2. IntroductionThe Industry 4.0 concept represents the trend of data exchange and automation in the manufacturing industry. It is defined by cloud computing, cyber-physical systems and the IoTs. In Industry 4.0, computers and automation technologies are integrated with robots and their components [1,2]. These robots and other components are remotely connected to a computer system, and the computer system is equipped with algorithms that can control and learning robots with minimal human intervention. For the motor device, the robot composes the control of the DC motor and relate to the accessories vital role. However, there are many factors that can misguide the controller's work, to make an unable to accurately control and/or have the desired dynamic behavior. For example, the resistance (Ohmic) of the coil winding of the motor is changed due to the temperature is different. If the vibration level is high and the wear of the rotor ball bearing becomes larger, the friction coefficient will also be affected. Therefore, the cloud computing environment of industrial factories, a continuous processing is required to re-evaluate the precise values of motor parameters and update the corresponding controller settings. In addition, it should be noted that the torque and speed sensors of the motor must have the correct selection to assist for achieving the correctness of the program.3. Theory and methods: multi-axis motor sensing and visual images of robotic armsCollaborative robots focus on intelligent motor control. Based on this construction, multiple sensor component signals are added. The physical information such as voltage, current, vibration, electric heating, magnetic flux sensing, and motor position is diagnosed through integrated control. This study, the artificial intelligent motor with the sensing signals, is used to control motor speed, positioning, constant horsepower, constant torque, force feedback, vibration resistance, overheating, overload, and instantaneous short circuit, as shown in Figure 1. The electromagnetic control of this paper is as follows:A. Single robotic arm function automation: Directly control the robot motor, at the same time, it has higher precision and speed to perform specific tasks repeatedly, but most of them cannot clearly actuate without an electromagnetic sensor.B. Part of the arm automation: It assists decision-making through environmental sensing input signals. In this study, robots use vision and sensors to determine the decision-making process, which is not easy to deal with accidental arm interference.C. Conditional autonomous robot: Control the arm according to the environmental monitoring behavior, but the external server can operate in real time.D. Highly autonomous robot: an environment that needs to be defined in an area or any situation without human operation.4. Experimental results and discussionBased on the motor control of intelligent robots, this research proposes a better and faster algorithm structure, which is a neural network-like combined with particle swarm method, which is used to calculate the convergence result of a lower loss function, which can accurately control the motor parameters and combine the IoTs, as shown in Figure 2. First, the robotic arm establishes multi-input sensing values, using the above algorithm to evaluate the robotic arm motor parameters, the prevention of interference from peripheral physical sensing, and the signal processing of external visual images. The issues raised based on past research experience include:(1) Establish edge and fog calculations to control the voltage and current of the motor, (2) establish neural network algorithm to calculate motor parameter, (3) construct the image vision system, (4) establish a smart robot platform, including wheel-shaped and human-shaped robots as experiments, which uses the above-mentioned deep learning theory to construct the test of the robot's motor motion and signal transmission response, (5) to use the multiple sensors, motor electromagnetic signals, piezoelectric sensors, and load signals, as the force feedback of the robot to calculate and judge. Finally, (6) to integrate the edge computing of the sensing layer, the fog computing of the signal processing layer, and faster computing layer through the IoTs cloud server. The IoTs chip is to use the dual-core Linkit-7697 (Taiwan’s MediaTek Inc). The chip can perform as a cloud computing and remote device, which indeed meet the requires of providing multi-sensing force feedback for cooperative robot handling objects. **

Design robust controller for uncertain nonlinear systems most of time can be a challenging work.One of the most active research areas in this field is control of the robot arm.The control strategies for robotics are classified in two main groups: classical and non-classical methods, where the classical methods use the conventional control theory and nonclassical methods use the artificial intelligence theory such as fuzzy logic, neural networks and/or neuro-fuzzy.Control robot arm using classical controllers are often having lots of problems because robotic systems are always highly nonlinear.Accurate robot manipulator is difficult because some dynamic parameters such as compliance and friction are not well understood and some robot parameters such as inertia are difficult to measure accurately.Artificial control such as Fuzzy logic, neural network, genetic algorithm, and neurofuzzy control have been applied in many applications.Therefore, stable control of a nonlinear dynamic system such as a robot arm is challenging because of some mentioned issues.In this paper the intelligent control of robot arm using Adaptive Fuzzy Gain scheduling (AFGS) and comparison to fuzzy logic controller (FLC) and various performance indices like the RMS error, and Steady state error are used for test the controller performance.

: The first Defense Nuclear Agency Conference on Arms Control and Verification Technology provided an international forum for over 200 individuals from the arms control verification technology and national security communities for discussion on the future of arms control verification and technology developments. Papers were presented in the following sessions: Future Arms Control Initiatives, Interface between Intelligence and Arms Control, Lessons Learned Proliferation in a Changing World Technologies - Roles and Applications, and Economics of Arms Control. Plenary sessions were held for general presentations on the future role of verification technology and negotiating and implementing verification measures. The conference papers will be published separately.

<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>

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.

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.

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

Simultaneous acquisition of electrooculogram, jaw electromyogram, electroencephalogram, and head movement via consumer-grade wearable devices has become possible. Such devices offer new opportunities to deploy practical biosignal-based interfaces for assistive robots; however, they also pose challenges related to the available signals and their characteristics. In this proof-of-concept study, we demonstrate the possibility of successful control of a 5 + 1 degrees-of-freedom robot arm based on a consumer wireless headband in the form of four control modes predicated on distinct signal combinations. We propose a control approach hybrid at two levels, which seeks a compromise between robot controllability and maintaining the user goal rather than being process-focused. First, robot arm steering combines discrete and proportional aspects. Second, after the robot has been steered toward the approximate target direction, a sparse approach is followed and the user only needs to issue a single command, after which steering adjustment and grasping are performed automatically under stereoscopic vision guidance. We present in detail the associated algorithms, whose implementation is publicly available. Within this framework, we also demonstrate the control of arm posture and grasping force based, respectively, on object visual features and user input. We regard the interface proposed herein as a viable blueprint for future work on controlling wheelchair-mounted and meal-assisting robot arms.

This paper presents a methodology for off-line trajectory generation that guarantees high-speed and precise end-effector point-to-point (positioning) control of industrial robot arms, within the bounds of the most relevant constraints. The constraints being addressed are the saturation of joint torque and the limit of Cartesian velocity. In the proposed methodology, shortest path of travel and maximum-joint torque/acceleration utility is guaranteed so that the true minimum-time trajectory is generated. The minimum-time trajectory is then compensated for the delay dynamics using a feedforward compensator. An illustrative implementation is presented using the Performer MK-3s robot arm, with which attractive results have been obtained. Being an off-line algorithm, the proposed method can be conveniently introduced into existing servo control systems of industrial robot arms, and to enhance their performance.

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.

Multiple coordinated robot arms are modeled by considering the arms as closed kinematic chains and as a force-constrained mechanical system working on the same object simultaneously. In both formulations, a novel dynamic control method is discussed. It is based on feedback linearization and simultaneous output decoupling technique. By applying a nonlinear feedback and a nonlinear coordinate transformation, the complicated model of the multiple robot arms in either formulation is converted into a linear and output decoupled system. The linear system control theory and optimal control theory are used to design robust controllers in the task space. The first formulation has the advantage of automatically handling the coordination and load distribution among the robot arms. In the second formulation, it was found that by choosing a general output equation it became possible simultaneously to superimpose the position and velocity error feedback with the force-torque error feedback in the task space. >

The present paper proposes, as an educational activity, the design of a system that allows the control of a robotic manipulator by using a sensory glove and an arm sleeve. The robotic manipulator features a robotic arm and hand, with a total of 10 degrees of freedom, using ten servo motors as actuators. On the other hand, the controller component features six flex sensors and two Inertial Measurement Units (IMUs). Due to the low force exerted by the motors, along with an issue regarding the reference frame of one of the IMUs, the robotic hand and wrist were detached from the robotic arm and both were tested separately. The results from real-time testing showed that the system successfully replicated the movements performed by the user. However, the system requires further improvements, mainly due to its lack of precision and limited degrees of freedom, along with other hardware issues. Regardless, an optimized design of the proposed system can have various applications, such as the manipulation of objects in hazardous conditions.