Kinematics Of Robot Arm Research Articles

Objective performance indicators differ in obese and nonobese patients during robotic proctectomy

BackgroundRobotic surgery is perceived to be more complex in obese patients. Objective performance indicators, machine learning–enabled metrics, can provide objective data regarding surgeon movements and robotic arm kinematics. In this feasibility study, we identified differences in objective performance indicators during robotic proctectomy in obese and nonobese patients. MethodsEndoscopic videos were annotated to delineate individual surgical steps across 39 robotic proctectomies (1880 total steps). Thirteen patients were obese and 26 were nonobese. Objective performance indicators during the following steps were analyzed: splenic flexure mobilization, left colon mobilization, pelvic dissection, and rectal transection. ResultsThe following differences were noted during robotic proctectomy in obese patients: during splenic flexure mobilization, more arm swaps, longer camera path length and velocity; during left colon mobilization, longer step time, more arm swaps, higher camera-related metrics (movement, path length, velocity, acceleration, and jerk), greater dominant arm path length, moving time, and wrist articulation; during anterior pelvic dissection, longer energy activation time, camera path length, and moving time; during posterior pelvic dissection, lower nondominant arm velocity, jerk, and acceleration; during left pelvic dissection, longer energy activation time; during right pelvic dissection, greater camera-related metrics (movement, path length, moving time, and velocity); and during rectal transection, longer step time, more arm swaps, master clutch use and camera movements, greater dominant wrist articulation, and longer dominant arm path length. ConclusionWe report step-specific objective performance indicators that differ during robotic proctectomy for obese and nonobese patients. This is the first study to use objective performance indicators to correlate a patient attribute with surgeon movements and robotic arm kinematics during robotic colorectal surgery.

Inverse kinematic solution and singularity avoidance using a deep deterministic policy gradient approach

<p>The robotic arm emerges as a subject of paramount significance within the industrial landscape, particularly in addressing the complexities of its kinematics. A significant research challenge lies in resolving the inverse kinematics of multiple degree of freedom (M-DOF) robotic arms. The inverse kinematics of M-DOF robotic arms pose a challenging problem to resolve, thus it involves consideration of singularities which affect the arm robot movement. This study aims a novel approach utilizing deep reinforcement learning (DRL) to tackle the inverse kinematic problem of the 6-DOF PUMA manipulator as a representative case within the M-DOF manipulator. The research employs Jacobian matrix for the kinematics system that can solve the singularity, and deep deterministic policy gradient (DDPG) as the kinematics solver. This chosen technique offers enhancing speed and ensuring stability. The results of inverse kinematic solution using DDPG were experimentally validated on a 6-DOF PUMA arm robot. The DDPG successfully solves inverse kinematic solution and avoids the singularity with 1,000 episodes and yielding a commendable total reward of 1,018.</p>

Research on Inverse Kinematics of Redundant Robotic Arms Based on Flexibility Index

Research on Inverse Kinematics of Redundant Robotic Arms Based on Flexibility Index

Objective performance indicators during specific steps of robotic right colectomy can differentiate surgeon expertise

BackgroundCurrent surgical assessment tools are subjective and nonscalable. Objective performance indicators, calculated from robotic systems data, provide automated data regarding surgeon movements and robotic arm kinematics. We identified objective performance indicators that significantly differed among expert and trainee surgeons during specific steps of robotic right colectomy. MethodsEndoscopic videos were annotated to delineate surgical steps during robotic right colectomies. Objective performance indicators were compared during mesenteric dissection, ascending colon mobilization, hepatic flexure mobilization, and bowel preparation for transection. ResultsTwenty-five robotic right colectomy procedures (461 total surgical steps) performed by 2 experts and 8 trainees were analyzed. Experts exhibited faster camera acceleration and jerk during all steps, as well as faster dominant and nondominant arm acceleration and dominant arm jerk during all steps except distal bowel preparation. During mesenteric dissection, experts used faster camera and dominant arm velocity. During medial-to-lateral ascending colon mobilization, experts used less-dominant wrist yaw and pitch, faster nondominant arm velocity, shorter dominant arm path length, and shorter moving times for camera, dominant arm, and nondominant arm. During lateral-to-medial ascending colon mobilization, experts had faster dominant and nondominant arm velocity and third-arm acceleration. During hepatic flexure mobilization, experts exhibited more camera movements, greater velocity for camera, dominant and nondominant arms, and faster third-arm acceleration. During distal bowel preparation, experts used greater dominant wrist articulation, faster camera velocity, and longer nondominant arm path length. During proximal bowel preparation, experts demonstrated faster nondominant arm velocity. ConclusionObjective performance indicators can differentiate experts from trainees during distinct steps of robotic right colectomy. These automated, objective and scalable metrics can provide personalized feedback for trainees.

Dual Performance Optimization of 6-DOF Robotic Arm Trajectories in Biomedical Applications

For the first time, dual-performance perfection technologies were used to kinematically operate sophisticated robots. In this study, the trajectory development of a robot arm is optimized using a dual-performance perfection technique. The proposed approach alters the robot arm's Kinematics by creating virtual points even if the robotic system is not redundant to make it kinematically suitable for biomedical applications. In the suggested method, an appropriate objective function is chosen to raise one or maybe more performance measures while lowering one or more kinematic characteristics of a robot arm. The robot arm's end effector is set in place at the crucial locations, and the dual performance precision algorithm changes the joints and virtual points due to the robot arm's self-motion. As a result, the ideal values for the virtual points are established, and the robot arm's design is changed. Accordingly, this method's ability to visualize modifications made to the processor's design during the optimization problem is one of its benefits. The active robotic arm is used as a case study in this article. The task is defined as choosing the best path based on the input target's position and direction and is used in X-ray robot systems. The outcomes demonstrate the viability of the suggested approach and can serve as a useful prototype for an intelligent X-ray robot.

Design optimization of medical robotic systems based on task performance metrics: A feasibility study for robotic guided vascular laser treatments

AbstractWhile designing an end‐effector tool for a specific task using an already available robotic arm is a common strategy for developing robot‐based solutions, it is subject to the designer's subjective biases, especially in the medical robotics field. Consequently, this approach can result in suboptimal solutions that may limit the performance of the robotic arm and the overall system. This work introduces a novel framework for developing a virtual scenario that enables the robust optimization of design guidelines for robotics‐based medical solutions. The proposed framework aims to address the issue of subjectivity involved in the design process, improving the reliability and accuracy of the robotic system. Specifically, it aims to find the optimized 3D positioning of the Tool Center Point and the robot base that takes full advantage of the robotic arm kinematics. The feasibility of the proposed methodology is validated for designing a system for robotic‐guided vascular laser treatments. First, a simulated treatment environment is constructed to compute the optimal trajectories to treat a wide population of synthetic human models. Next, a design optimization methodology based on robot task performance metrics is developed. Overall, the proposed design guidelines allowed the performance of 94.1% of the treatment trajectories free of collisions with median precision of around 0.5 mm while guaranteeing high manipulability. Thus, proving the added value of the proposed strategy and showing the potential for promoting the development of optimized robotics‐based solutions.

Analysis of Forward Kinematics of 2R Robotic Arm

In Kinematics, the analysis of acceleration and velocity is usually done whereas the forces are neglected. The machine’s smallest unit is the link, which is akin to a cell in the human body. Robotics is built on the foundation of machines. A pair is established by the combination of links which leads to the construction of linkage. A mechanical linkage is a collection of bodies that are connected to control forces and movement. The combination of linkage (also known as Mechanism) generates Machine. The robot’s kinematics equations are used in robotics, video games and animation. Inverse kinematics is the mathematical process of determining variable joint parameters necessary to place an object at the end of a kinematic chain in a specific position and orientation with respect to the start of the chain, such as robot manipulator or skeleton of an animated character. The role of Forward and Inverse Kinematics in a 2R Robotic Arm are studied with successful demonstrations. Forward Kinematics of a 2R Robotic Arm using Matlab and Python confirming similar results are exhibited (Figure 8 and Figure 9). Matlab illustrated 2D Path Tracing for 2R Robotic Arm with Inverse Kinematics. Finally, inverse kinematics of 2R Robotic Arm in Matlab using fuzzy logic is modelled successfully. Thus, it helps in the understanding of kinematics and can be used in the simulation of machines.

Deep Convolutional Generative Adversarial Network for Inverse Kinematics of Self-Assembly Robotic Arm Based on the Depth Sensor

In this study, we propose a new deep convolutional generative adversarial kinematics network (DCGAKN) to establish inverse kinematics of self-assembly robotic arm. We design that the robot system uses a depth sensor detecting an object by you only look once v4 (YOLOv4) algorithm, and then, our proposed DCGAKN is with generator and discriminator of adversarial evolution training inverse kinematics model for controlling self-assembly robotic arm to solve the limited solution space to be more adaptive in the dynamic environment. The following are advancements of our proposed method: 1) generator neural network is trained by few-shot training data to control the self-assembly robotic arm to achieve high-accuracy position; 2) generator is evaluated with discriminator not only depending on training data but also on adaptive evolutionary; 3) the self-assembly robotic arm is like humanoid arm not traditional robotic arm structure and it is easy for self-assembly model to build inverse kinematics without computing inverse kinematics matrix; 4) the object is detected by the depth information based on YOLOv4; and 5) through generator evolutionary, the activity range of robotic arm is not limited with training data range. The proposed DCGAKN is compared with convolutional neural network (CNN) and deep neural network (DNN) that the accuracy rate and distance error achieve 87% and 1.26 cm, respectively. The source code of this work is at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/YiZengHsieh/DCGAKN</uri> .

Thermal Scalar Field for Continuous Three-dimensional Toolpath Strategy Using Wire Arc Additive Manufacturing for Free-form Thin Parts

Wire Arc Additive Manufacturing (WAAM) is revolutionizing the field of Additive Manufacturing (AM) by being the technological solution to manufacture thin-walled structures of large dimensions and medium geometric complexity at reduced cost with an excellent buy-to-fly ratio. Manufacturing parts with this technology is nowadays done through 2.5D strategies. This type of strategy consists in cutting a 3D model using planar layers parallel to each other. This 2.5D technique limits the complexity of the geometries that can be produced in WAAM without taking advantage of height deposit modulation. It also requires several start/stop phases of the arc during the transition from one layer to another, which leads to poor quality. This paper presents a new fast and efficient path planning strategy aiming at creating a continuous manufacturing path, thus increasing poor part quality. This strategy so called “Scalar Thermal Field for Continuous Toolpath” is generating a continuous spiral manufacturing toolpath for thin shaped parts. The modulation of deposition, by controlling the welding torch travel speed at constant wire feed rate, allows continuous deposition of material throughout the manufacturing process. The keypoint of the method is the use of a thermal scalar field associated with a 6-axis robotic arm kinematics which allows the manufacturing of closed parts after optimal closure point determination or direct manufacturing of opened parts with non-planar free edges. Validation of the presented method is performed by manufacturing three distinct parts : an opened, a closed part and a multi-branch part. The fabrication of these parts and their precise measurement have shown the reliability and the restitution capacity of our method which is clearly superior to 2.5D strategies nowadays commonly used in WAAM technology. • Presents a new and easy to compute continuous toolpath generation method. • Describes a path planning strategy using 5-axis with modulation of the deposit. • Uses thermal scalar field for the generation of continuous toolpaths. • Reduces the arc start/stop phases by using a continuous toolpath strategy. • Validates the method by manufacturing and measuring three different types of parts.

Forward kinematic analysis of 5-DOF LYNX6 robotic arm used in robot-assisted surgery

Robotic surgery or robot-assisted surgery, allows doctors to conduct a wide range of complex procedures. It’s precision, flexibility, and control are more efficient than traditional approaches. A robotic arm is the most commonly used clinical robotic surgical system worldwide. A typical robotic arm includes an arm for camera and/ or mechanical arm for variety surgical instruments attached to it. They are like human arms but with improved efficiency of payload handling. The mapping of a kinematic model with accuracy is the most critical part of a robotic manipulator in order to achieve these demanding expectations. Various researchers in the literature have made substantial contributions in this direction. In 1995, Denavit and Hartenberg proposed the famous D-H parameters technique. This approach is used in the kinematic analysis of a five-DOF spatial manipulator called the 5-DOF Lynx6 Robotic Arm, in the current study. The proposed study includes a comparative analysis of forward kinematics of the 5-DOF Lynx6 Robotic Arm using analytical and computational approaches. The analytical method will include the representation of the end-effector using a transformation matrix and the same is compared computationally by using MATLAB. This study derives a comprehensive analytical solution using matrix algebra to the forward and inverse kinematics of a 5-DOF robotic arm. The proposed strategy is simple yet effective to implement in practice. A number of numerical simulations were performed to verify this strategy. A simulation and visualization of the workspace of the robotic arm is also done using MATLAB. This workspace shows us the set of points reached by the end-effector giving us an understanding of the scope of the robot and its reach and mobility for different types of surgery. As an outcome of this study, we found that the Lynx-6 robotic arm can be used in robot assisted laparoscopy and its effectiveness can be further increased with few modifications.

Safe physical human-robot interaction: A quasi whole-body sensing method based on novel laser-ranging sensor ring pairs

Non-contact detection is a competitive method for ensuring safety in close human-robot interaction (HRI). However, existing sensor configuration methods have difficulty satisfying the requirements for covering a wide range of sensing zones with fewer spots and a low-cost, low-complexity processing system. In this study, we developed a novel laser-ranging sensor configuration comprising sensors mounted on ring pairs, thereby implementing quasi whole-body sensing and cooperative location with fewer sensors. Each sensor ring pair was composed of double sensor rings mounted at opposite ends of robot arms. Based on the cone detection zone of each laser-ranging sensor, an optimisation of the configuration parameters showed that the maximum sensing volume coverage could be obtained when the laser-ranging sensors on each sensor ring were mounted in a tilted and biased form at certain angles. According to detection data and robot arm kinematics, a cooperative location model was also established to estimate the intervention position. Furthermore, we presented a self-occlusion geometric detection method for the robot, so as to identify cases in which the robot body entered the detection zone of the sensor ring pairs. To validate the efficacy of the proposed methods, experiments on quasi whole-body sensing, cooperative location, and self-occlusion detection were conducted with a UR10 robot. The experimental results show that the proposed sensor ring pair and detection method can effectively detect an approaching human and effectively estimate the intervention position and self-occlusion, allowing for efficient and safe cooperative performance with humans.

A novel quantum model of forward kinematics based on quaternion/Pauli gate equivalence: Application to a six-jointed industrial robotic arm

A novel quantum-based kinematic model for computing the position and the orientation of the six-jointed IRB 140 ABB robotic arm is presented. Position is determined through successive elementary translations and rotations from the end effector down to the base frame, whereas orientation is derived from quaternion representation. The model is based on the equivalence between quaternions and Pauli matrices or gates. Based on comparison to experimental results, the quantum kinematic model is valid. Its main advantage is that it uses only one qubit to model both position and orientation, which represents a major saving in terms of computing resources and time. This model could guide development of other quantum models and simulation software to be used in future quantum computers and could also inform classical computing theory for robotic arm kinematics modeling.

Modeling of Industrial Robot Kinematics Using a Hybrid Analytical and Statistical Approach

Abstract Industrial robots are highly desirable in applications including manufacturing and surgery. However, errors in the modeling of the kinematics of robotic arms limit their positional accuracy in industrial applications. Specifically, analytical kinematic models of the robot arm suffer from errors in coefficient calibrations and the inability to account for effects including gear backlash. However, statistical modeling methods require an extensive amount of points for calibration, which is infeasible in practical industrial environments. Hence, this paper describes, develops, and experimentally validates a hybrid modeling methodology combining both analytical and statistical methods to describe the robot kinematics in an intuitive manner that is easily adaptable for small- and medium-sized industries. By formulating an explicitly described analytical kinematic model as a prior mean distribution of a Gaussian process, the prior distribution can be updated with experimental data using statistical Bayesian Inference, thus enabling more accurate description of the robot kinematics with fewer data points. The hybrid model is demonstrated to outperform an analytical model, a neural network model, and a Gaussian Process Regression model with no prior distribution in predicting both the forward and inverse kinematics of a UR5 and UR10 robot arm. Also, the error propagation of the inverse kinematic solutions is studied. In addition, the testing framework used in this work can be used as a standardized benchmark to evaluate alternative kinematic models.

Algebraic models based on trigonometric and Cramer's rules for computing inverse kinematics of robotic arm

This study postulates applicable and high-performing solutions for the inverse kinematic (IK) problem of two-segmented robotic arm using algebraic models. The former section of models is acquired from the manipulation of trigonometric rules, specifically the sum and Pythagorean identities to solve the second joint angles. The latter part of models is drawn by exploiting a matrix mechanics called Cramer's rule for the results of first joint angle. For verification, the precision of solutions yielded are cross-checked with the manipulator's direct kinematics and tested with the statistical measure of minimum square error while tracking cubic Hermite spline, cubic Bezier, cubic B-spline, ellipse and circle curves. For validation, a spreadsheet-based IK application utilising built-in front-end capabilities including Visual Basic for applications, Math and Trig function library, name manager, data validation, ActiveX controls, and charts is developed to accommodate these models and simulate the feasible joint angles and orientations of robotic arm.

Algebraic models based on trigonometric and Cramer's rules for computing inverse kinematics of robotic arm

Algebraic models based on trigonometric and Cramer's rules for computing inverse kinematics of robotic arm

A Robot Arm Design Optimization Method by Using a Kinematic Redundancy Resolution Technique

Redundancy resolution techniques have been widely used for the control of kinematically redundant robots. In this work, one of the redundancy resolution techniques is employed in the mechanical design optimization of a robot arm. Although the robot arm is non-redundant, the proposed method modifies robot arm kinematics by adding virtual joints to make the robot arm kinematically redundant. In the proposed method, a suitable objective function is selected to optimize the robot arm’s kinematic parameters by enhancing one or more performance indices. Then the robot arm’s end-effector is fixed at critical positions while the redundancy resolution algorithm moves its joints including the virtual joints because of the self-motion of a redundant robot. Hence, the optimum values of the virtual joints are determined, and the design of the robot arm is modified accordingly. An advantage of this method is the visualization of the changes in the manipulator’s structure during the optimization process. In this work, as a case study, a passive robotic arm that is used in a surgical robot system is considered and the task is defined as the determination of the optimum base location and the first link’s length. The results indicate the effectiveness of the proposed method.

Vision-Guided Recognition Method for Working State of Robot Arm based on Machine Learning

To realize the capture of the target in any position within the working range of the robot arm, a vision-guided target recognition and positioning control method based on machine learning was proposed to improve the accuracy of the working state recognition. The system grabs four different contours of workpieces (triangle, pentagon, round, square and place in the designated area as the task, and by using the MATLAB to process the image information, and all the connected domains are marked bythe neighborhood areamarking algorithm. Later, the logarithmic coordinates-Fourier transform template matchingmethod is adopted to identify workpiece types and extract their centroid as the positioning reference coordinates.Combined with the 3-DOF robot arm, the standard D-H parametric method is usedto establishthe robot arm kinematics model, and through the inverse operation of the robot armand according to the position of the workpiece coordinates,the joint angle of each robot arm can be obtained. Later, it is sent to the lower microcontroller Arduino through a serial port, and then the control instruction is completed by the Arduino torealize the capture and placement of the workpiece and complete the work state recognition task.The experimental results show that the working state recognition system can meet the design requirements.

Kinematic Modeling and Simulation of Dual-Arm Robot

In recent years, dual-arm robots have attracted more and more attention due to their advantages such as strong cooperation ability and high flexibility. With the improvement of real-time requirement of dual-arm cooperation, the inverse kinematics solution of robot becomes a key problem to be solved urgently. To solve the time-consuming problem of inverse kinematics of robot arm, a closed inverse kinematics solution algorithm for humanoid dual-arm robot was proposed. The effectiveness of the algorithm was verified by simulation.

Robot arm trajectory planning study for a table tennis robot

The 4 degree of freedom robot arm of a table tennis robot has a variety of trajectories. In order to improve the response and the success rate of the shots, we used the joint space trajectory planning method to establish a kinematic model with the robot arm joints as variables, and by combining it with the robot arm kinematics, we obtained the relevant parameters for each joint of the robot arm. Simulation tests and physical tests were carried out to obtain a more accurate trajectory of the robot arm.

Position Forward Kinematics of 6-DOF Robotic Arm

The paper describes the construction and verification of a kinematic model of a robotic arm position, which should be composed of special modules (URM). The concept of modularity plays a fairly important role here, as it is possible to assemble from individual modules machines with different movement options and several degrees of freedom. The degrees of freedom of the arm are facilitated by six rotating links, which are, thank to those modules, unlimited. The actual implementation of the robotic arm's kinematic model into the software environment occurred in two environments. In order to check the correctness of the calculation of the individual parts of the kinematic structure's position in space, two different models were intentionally created. A mathematical model in Matlab - Simulink and a mechanical model built in the Matlab - Simscape environment.