Coordinated motion control of robot arm based on virtual internal model

This paper is proposed to solve the inverse kinematic (IK) problem of two-degree-of-freedom planar robot arm using neural networks (NN). Several NN model designs of distinct hidden neurons based on the sum of square error function of joint angle are developed and trained with generalised reduced gradient algorithm. The paper is also intended to demonstrate the modelling process of feed-forward NN topology in spreadsheet environment. The spreadsheet functions as INDEX, SUMPRODUCT, EXP, and SUMSQ; the utilities as name manager, data validation, data table, ActiveX controls, answer report, and charts; and the add-in Solver are utilised to develop the models. With the input parameters of link lengths and end-effector position and orientation, two models with the structures 5-12-1 and 5-10-1 are discovered best-capable in predicting first and second joint angles respectively. This NN-based IK technique contributes significantly to the optimal motion control of robot arm for quality processing and assembly tasks.

A single-master, multiple-slave manipulator system and its task-oriented control using VIM (virtual internal model) are proposed. By using this system, the operator no longer has to consider how to control the slave arms in coordination, so the operator can concentrate on his/her task during the operation. An experimental single-master, multislave manipulator system with two slave arms, each of which has six degrees of freedom, is described, and the concept of the system is illustrated. The experimental system does not have a bilateral function, and a master arm can specify the task-oriented variable up to six degrees of freedom. The use of bilateral feedback as well as task-oriented functions will enable the system to deal with more complex tasks. >

Using results on the topology of moduli space of polygons (Jaggi, Ph.D. Thesis, University of Bern, 1992; Kapovich and Millson, J. Differ. Geom. 42:133---164, 1994), it can be shown that for a planar robot arm with n segments there are some values of the base-length, z (i.e., length of line joining the base of the arm with its end-effector), at which the configuration space of the constrained arm (arm with its end effector fixed) has two disconnected components, while at other values it has one connected component. We first review some of these known results relating the value of z with the connectivity of the constrained configuration space. Then the main design problem addressed in this paper is the construction of pairs of continuous inverse kinematics for arbitrary robot arms, with the property that the two inverse kinematics agree (i.e. return the same configuration) when the constrained configuration space has a single connected component, but they give distinct configurations (one in each connected component) when the configuration space of the constrained arm has two components. This design is made possible by a fundamental theoretical contribution in this paper--a classification of configuration spaces of robot arms such that the type of path that the system (robot arm) takes through certain critical values of the forward kinematics function is completely determined by the class to which the configuration space of the arm belongs. This classification result makes the aforesaid design problem tractable, making it sufficient to design a pair of inverse kinematics for each class of configuration spaces (three of them in total). The motivation for this work comes from a more extensive problem of motion planning for the end effector of a robot arm, in which the ability to continuously sample one configuration from each connected component of the constrained configuration spaces of the arm enables us to dramatically reduce the dimensionality of the space in which the planning has to be performed, without sacrificing algorithmic completeness. We start the paper with the general motivation, but address only the problem of sampling such configurations when there is no obstacle in the environment--a problem that in itself is non-trivial. We demonstrate the simplicity and the low complexity of the presented algorithm through a Javascript + HTML5 based implementation available at http://hans.math.upenn.edu/~subhrabh/nowiki/robot_arm_JS-HTML5/arm.html.

Considers the problem of modeling and controlling the impact of a two-degree-of-freedom planar robot arm against an infinitely rigid and massive surface. For this case study, the basic equations describing the motion of the robot arm are derived for noncontact and contact conditions. A control scheme is proposed on the basis of a reduced-order observer that is able to asymptotically estimate the impact-induced forces and to allow their asymptotic compensation when the robot arm is in contact with the surface. The resulting control system ensures the global asymptotic regulation of the position of the arm with an assigned impact-induced force. The case study is completed by a simulation test.

Camera Holding Robotic Devices in Urology

In this paper we present a spherical soft robotic arm made from fabric. The inflatable arm has a small mass and is pneumatically actuated. A configuration is employed with only three actuators controlling the two rotational degrees of freedom of a spherical joint. This differs from the commonly employed antagonistic pairs, including four actuators for two degrees of freedom. The fabrication procedure of the lightweight and compliant system is discussed in detail and uses commonly available materials and tools. The capability of the robotic arm to adjust the joint stiffness as a function of the actuator pressures is investigated and characterized for different deflection directions. The static mapping from the actuator pressures to the orientation of the robotic arm is identified from data and the inverse mapping is employed in a position controller. The modeling and controller derivation are performed for three different stiffness levels demonstrating the ability of the spherical robotic arm to change the joint stiffness independently of controlling the position. The position tracking performance is experimentally evaluated by tracking a square trajectory. A comparison of the tracking performance for the different stiffness levels shows that accurate tracking is more challenging for the smallest joint stiffness. A gray-box model capturing the interactions of the two degrees of freedom is used in a learning scheme that is applied for the smallest stiffness level. The learning approach reduces interactions between the two degrees of freedom and demonstrates the control performance achievable with the system developed.

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 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.

This paper aims to exploit the adaptive control robot arm with six degrees of freedom, an adaptive fuzzy control system was established.a fuzzy controller, the robot arm trajectory planning and the D-H algorithm were studied based on the fuzzy controller.Firstly, fuzzy controller and control rules are designed according to a series of characteristics of fuzzy control method.Then, based on the analysis of robot arm parameters, robot arm trajectory plan by the D-H algorithm.Finally, Hardware and software design and experiment were complete.Experimental results show that the D-H algorithm can be used to establish the model of the robot arm, and the MATLAB can be used to understand the data processing.The stability of adaptive control of robot arm with 6 degrees of freedom is good, the expansibility is strong, easy to control and so on.

Brain-computer interface (BCI) for robotic arm control has been studied to improve the life quality of people with severe motor disabilities. There are still challenges for robotic arm control in accomplishing a complex task with a series of actions. An efficient switch and a timely cancel command are helpful in the application of robotic arm. Based on the above, we proposed an asynchronous hybrid BCI in this study. The basic control of a robotic arm with six degrees of freedom was a steady-state visual evoked potential (SSVEP) based BCI with fifteen target classes. We designed an EOG-based switch which used a triple blink to either activate or deactivate the flash of SSVEP-based BCI. Stopping flash in the idle state can help to reduce visual fatigue and false activation rate (FAR). Additionally, users were allowed to cancel the current command simply by a wink in the feedback phase to avoid executing the incorrect command. Fifteen subjects participated and completed the experiments. The cue-based experiment obtained an average accuracy of 92.09%, and the information transfer rates (ITR) resulted in 35.98 bits/min. The mean FAR of the switch was 0.01/min. Furthermore, all subjects succeeded in asynchronously operating the robotic arm to grasp, lift, and move a target object from the initial position to a specific location. The results indicated the feasibility of the combination of EOG and SSVEP signals and the flexibility of EOG signal in BCI to complete a complicated task of robotic arm control.

Significance: The ability of robotic arm system to become teleoperational is essential for advancing remote work. Existing solutions often require complex setups, specialized training, and expensive hardware. Developing a simplified wearable device that can be rapidly integrated and deployed can significantly enhance accessibility where remote control is paramount, including remote surgeries in healthcare and hazardous environments. Aim and approach: This work presents the design and development of a wireless glove-based control system for remotely operating a robotic arm. A reliable, responsive, and user-friendly control system offers scalability, customization options, intuitive interaction, and ergonomic comfort. The approach combines advanced motion sensing using a 9 Degrees of Freedom (DoF) Inertial Measurement Unit (IMU) and real-time data processing. The IMU estimates roll, pitch, and yaw, directly controlling the translational movements (x, y, z) of the robotic arm’s end effector. Depending on button states, these inputs can also switch to control the end-effector rotations. Results: The developed glove demonstrated user compliance. The system is rapid to deploy, and the wireless device successfully captured real-time gesture control data and redirected it to the robotic system with an average latency of 185 ms, enabling the system to perform a range of manipulations, including pick and place, reaching, and grasping objects. Conclusions: This research presents a robust and efficient teleoperation system, offering a viable solution for advancements in remote robotic arm control. This innovation bridges the gap between the full potential of complex robotic systems and user interaction, unlocking new possibilities across various fields.

<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>

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.

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

A most common abnormality that affects the vertebrae is the disc prolapse. These conditions normally transpire in lumbosacral region and enmesh the spinal nerve at that point. A detoriation to the motor and sensory nerve at that point has an immediate consequence on that particular organ connected with the ascending and descending tract. Disc prolapse in thoracic region leads either to nerve root irritation or cord compression and it needs an immediate surgery to remove the disc lesion which is usually done by discectomy technique, an invasive spine surgery. However, the proposed system is to create a minimally invasive procedure to detach the disc lesion of the spine. The controlled and patient specific pre-operative planning is done by MIMIC using 3D model of the MRI of that patient. The new techniques on robotic arm like segmentation and surgical simulation will provide information about the positioning of the end effectors. Using 3D model design, the robotic arm with 3 degree of freedom is done by the SOLIDWORKS 3D CAD design software. SimMechanics is a MATLAB tool used for simulation of the physical model which gives control to the robotic arm. Furthermore, the simulation result gives the force used to produce torque at the joints and angle of joint due to torque produced. Finally, we show that the simulation in SimMechanics reduces the error occurrence in proto type construction.