End-effector Research Articles

Painting robot with three axis motion

Paint was applied directly to the painted surface while painting with a brush. There are occasionally unsatisfactory outcomes, such as paint stacking or streaks remaining. Spray guns yield better paint results, therefore a robot was developed that can regulate the spray gun's movement in order to apply paint. A painting robot that resembles an arm is one that has three axes of movement. The robot can move left-right on the x axis, up-down on the y axis, and back-front on the z axis. Its end effector is a spray cannon. An ultrasonic sensor is used to measure the distance between components and the spray gun; when the sensor measures near distances, such as between 4 and 8 cm, the test sensor distance measurements have a larger average error. When the test reading distance is increased from 10 cm to 14 cm, the average error reduces. Superior painting outcomes can be achieved with a paint distance of 10 cm, an air pressure of 80 psi, and a spray gun arm movement speed of 25 rpm. With average x-axis movement of 74.6 cm, y-axis movement of 14.8 cm, and z-axis movement of 88.2 cm, the largest volume that can be painted is 97,517.5 cm3. The rotation of the arm is used to determine the angular rotation needed to paint components. Painting the left side of the component at an angle of 10°, the back side at an angle of 100°, the right side at an angle of 190°, and only painting the front side of the component at the required angle of 90° require the rotational movement of the arm to the left.

Accuracy of Event Rate and Effect Size Estimation in Major Cardiovascular Trials

For the design of a randomized clinical trial (RCT), estimation of the expected event rate and effect size of an intervention is needed to calculate the sample size. Overestimation may lead to an underpowered trial. To evaluate the accuracy of published estimates of event rate and effect size in contemporary cardiovascular RCTs. A systematic search was conducted in MEDLINE for multicenter cardiovascular RCTs associated with MeSH (Medical Subject Headings) terms for cardiovascular diseases published in the New England Journal of Medicine, JAMA, or the Lancet between January 1, 2010, and December 31, 2019. Identified trials underwent abstract review; eligible trials then underwent full review, and those with insufficiently reported data were excluded. Data were extracted from the original publication or the study protocol, and a random-effects model was used for data pooling. This review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guideline. The primary outcome was the accuracy of event rate and effect size estimation. Accuracy was determined by comparing the observed event rate in the control group and the effect size with their hypothesized values. Linear regression was used to determine the association between estimation accuracy and trial characteristics. Of the 873 RCTs identified, 374 underwent full review and 30 were subsequently excluded, resulting in 344 trials for analysis. The median observed event rate was 9.0% (IQR, 4.3% to 21.4%), which was significantly lower than the estimated event rate of 11.0% (IQR, 6.0% to 25.0%) with a median deviation of -12.3% (95% CI, -16.4% to -5.6%; P < .001). More than half of the trials (196 [61.1%]) overestimated the expected event rate. Accuracy of event rate estimation was associated with a higher likelihood of refuting the null hypothesis (0.13 [95% CI, 0.01 to 0.25]; P = .03). The median observed effect size in superiority trials was 0.91 (IQR, 0.74 to 0.99), which was significantly lower than the estimated effect size of 0.72 (IQR, 0.60 to 0.80), indicating a median overestimation of 23.1% (95% CI, 17.9% to 28.3%). A total of 216 trials (82.1%) overestimated the effect size. In this systematic review of contemporary cardiovascular RCTs, event rates of the primary end point and effect sizes of an intervention were frequently overestimated. This overestimation may have contributed to the inability to adequately test the trial hypothesis.

Design and Integration of a Robotic Welding Parameterized Procedure for Industrial Applications

This paper explores the development of an effective motion planning strategy for robotics welding in tube to tubesheet joints, a critical process in heat exchanger manufacturing. The research methodology follows an experimental paradigm, investigating two distinct approaches to tackle the intricacies of this task. The initial approach, involving a welding torch affixed to the robotic arm's flange, proved ineffective due to the complexity of continuous 360° orbital welding. This led to the adoption of a custom end effector in the second approach, designed to enhance adaptability and precision. Key tools and materials employed in this research include the Robot Operating System (ROS), Rviz for 3D visualization, MoveIt for motion planning, SolidWorks for CAD modelling, and the xArm7 Robotic Arm. These tools facilitated the creation of a comprehensive planning environment. The motion planning process relies on three essential parameters: tube diameter, type of tube to tubesheet joint (flush or protruding), and the 3D coordinates of tube centers. A Python scripts control the robot's movements, with specific joint state and pose goals for precise positioning. Finally, this study contributes to present a program that orchestrates the robotic arm's motion, simulating the welding process for tube to tubesheet joints. This comprehensive research endeavor contributes to the optimization of motion planning strategies in the context of tube to tubesheet welding, with practical applications in the manufacturing industry.

Adaptive iterative learning control of soft robot for beating heart tracking

PurposeSoft robots are known for their excellent safe interaction ability and promising in surgical applications for their lower risks of damaging the surrounding organs when operating than their rigid counterparts. To explore the potential of soft robots in cardiac surgery, this paper aims to propose an adaptive iterative learning controller for tracking the irregular motion of the beating heart.Design/methodology/approachIn continuous beating heart surgery, providing a relatively stable operating environment for the operator is crucial. It is highly necessary to use position-tracking technology to keep the target and the surgical manipulator as static as possible. To address the position tracking and control challenges associated with dynamic targets, with a focus on tracking the motion of the heart, control design work has been carried out. Considering the lag error introduced by the material properties of the soft surgical robotic arm and system delays, a controller design incorporating iterative learning control with parameter estimation was used for position control. The stability of the controller was analyzed and proven through the construction of a Lyapunov function, taking into account the unique characteristics of the soft robotic system.FindingsThe tracking performance of both the proportional-derivative (PD) position controller and the adaptive iterative learning controller are conducted on the simulated heart platform. The results of these two methods are compared and analyzed. The designed adaptive iterative learning control algorithm for position control at the end effector of the soft robotic system has demonstrated improved control precision and stability compared with traditional PD controllers. It exhibits effective compensation for periodic lag caused by system delays and material characteristics.Originality/valueTracking the beating heart, which undergoes quasi-periodic and complex motion with varying accelerations, poses a significant challenge even for rigid mechanical arms that can be precisely controlled and makes tracking targets located at the surface of the heart with the soft robot fraught with considerable difficulties. This paper originally proposes an adaptive interactive learning control algorithm to cope with the dynamic object tracking problem. The algorithm has theoretically proved its convergence and experimentally validated its performance at the cable-driven soft robot test bed.

Seedling-YOLO: High-Efficiency Target Detection Algorithm for Field Broccoli Seedling Transplanting Quality Based on YOLOv7-Tiny

The rapid and accurate detection of broccoli seedling planting quality is crucial for the implementation of robotic intelligent field management. However, existing algorithms often face issues of false detections and missed detections when identifying the categories of broccoli planting quality. For instance, the similarity between the features of broccoli root balls and soil, along with the potential for being obscured by leaves, leads to false detections of “exposed seedlings”. Additionally, features left by the end effector resemble the background, making the detection of the “missed hills” category challenging. Moreover, existing algorithms require substantial computational resources and memory. To address these challenges, we developed Seedling-YOLO, a deep-learning model dedicated to the visual detection of broccoli planting quality. Initially, we designed a new module, the Efficient Layer Aggregation Networks-Pconv (ELAN_P), utilizing partial convolution (Pconv). This module serves as the backbone feature extraction network, effectively reducing redundant calculations. Furthermore, the model incorporates the Content-aware ReAssembly of Features (CARAFE) and Coordinate Attention (CA), enhancing its focus on the long-range spatial information of challenging-to-detect samples. Experimental results demonstrate that our Seedling-YOLO model outperforms YOLOv4-tiny, YOLOv5s, YOLOv7-tiny, and YOLOv7 in terms of speed and precision, particularly in detecting ‘exposed seedlings’ and ‘missed hills’-key categories impacting yield, with Average Precision (AP) values of 94.2% and 92.2%, respectively. The model achieved a mean Average Precision of 0.5 (mAP@0.5) of 94.3% and a frame rate of 29.7 frames per second (FPS). In field tests conducted with double-row vegetable ridges at a plant spacing of 0.4 m and robot speed of 0.6 m/s, Seedling-YOLO exhibited optimal efficiency and precision. It achieved an actual detection precision of 93% and a detection efficiency of 180 plants/min, meeting the requirements for real-time and precise detection. This model can be deployed on seedling replenishment robots, providing a visual solution for robots, thereby enhancing vegetable yield.

OmniCharger: CNN-based Hand Gesture Interface to Operate an Electric Car Charging Robot through Teleconference

The automation of the car charging process is motivated by the rapid development of technologies for self-driving cars and the increasing importance of ecological transportation units. Automation of this process requires the implementation of Computer Vision (CV) techniques. However, it remains challenging to precisely position the charger plug autonomously due to the sensitivity of CV algorithms to lighting and weather conditions. We introduce a novel robotic operation system based on hand gesture recognition through teleconferencing software. The users, connected by teleconference, use their hand gestures to teleoperate the electric plug located on the collaborative robot end-effector. We conducted a user study to evaluate the system performance and suitability using OmniCharger and two baseline interfaces (a UR10 Teach Pendant and a Logitech F710 Wireless Gamepad). Except for two trials, all the users were able to locate the plug inside of a 5 cm target using the interfaces. The distance to the target and the orientation error did not present statistically significant differences ( \(p=0.1099\,{\gt}\,0.05\) and \(p=0.0903\,{\gt}\,0.05\) , respectively) in the use of the three interfaces. The NASA TLX questionnaire results showed low values in all the sub-classes, the SUS results rated the usability of the proposed interface above average (68%), and the UEQ showed excellent performance of the OmniCharger interface in the attractiveness, stimulation, and novelty attributes.

Redundant Space Manipulator Autonomous Guidance for In-Orbit Servicing via Deep Reinforcement Learning

The application of space robotic manipulators and heightened autonomy for In-Orbit Servicing (IOS) represents a paramount pursuit for leading space agencies, given the substantial threat posed by space debris to operational satellites and forthcoming space endeavors. This work presents a guidance algorithm based on Deep Reinforcement Learning (DRL) to solve for space manipulator path planning during the motion-synchronization phase with the mission target. The goal is the trajectory generation and control of a spacecraft equipped with a 7-Degrees of Freedom (7-DoF) robotic manipulator, such that its end effector remains stationary with respect to the target point of capture. The Proximal Policy Optimization (PPO) DRL algorithm is used to optimize the manipulator’s guidance law, and the autonomous agent generates the desired joint rates of the robotic arm, which are then integrated and passed to a model-based feedback linearization controller. The agent is first trained to optimize its guidance policy and then tested extensively to validate the results against a simulated environment representing the motion synchronization scenario of an IOS mission.

Topology Optimization of a Compliant Constant-Force End Effector for Robotic Operations Over Uneven Surfaces

Abstract A compliant constant-force mechanism (CCFM) is a specific type of compliant mechanism that serves as a passive force regulation device. When subjected to a load, it undergoes deformation, resulting in an almost consistent output force regardless of changes in input displacement. Traditional methods used to design CCFMs typically rely on either stiffness combination or parametric optimization based on existing design configurations. To enable the direct synthesis of CCFMs according to desired boundary conditions, this study proposes a systematic topology optimization method. This method includes a new morphology-based scheme designed to ensure the connectivity of the topological results, thereby achieving this objective. Using this approach, a CCFM suitable for end effector applications is designed and manufactured through 3D printing. Four of these CCFMs are then utilized to create an innovative compliant constant-force end effector for robotic operations on uneven surfaces. The experimental results demonstrate that the presented design achieves output force modulation through elastic deformation, eliminating the need for additional sensors and controllers to regulate the output force. The presented design can be mounted on a robotic arm to provide overload protection and maintain a consistent force output during operation when encountering irregular and uneven surfaces.

An Underactuated Cable-Driven Parallel Robot for Marine Automated Launch and Recovery Operations

Abstract This paper presents the feasibility study and preliminary testing of an underactuated cable-driven parallel robot for automated launch and recovery operations on the sea surface. The robot frame is mounted onto a primary vessel (PV) subject to sea-induced motions. During launch, the end-effector (EE) is required to deploy a secondary vessel (SV) by lowering it from the PV onto the sea surface. During recovery, the ee has to track and grasp a SV and the EE-SV assembly needs to be stabilized during lifting from the sea surface to the PV. Sea conditions and the underactuated nature of the ee influence the operational feasibility and the overall robot performance. The paper presents the conceptual design and the robot model, as well as the methodologies for winch dimensioning and robot control. An extensive simulation campaign is conducted to optimize performances and assess the system behavior. Finally, the operations are tested on a scaled prototype in a laboratory environment.

Whole-Body Dynamics for Humanoid Robot Fall Protection Trajectory Generation with Wall Support.

When humanoid robots work in human environments, they are prone to falling. However, when there are objects around that can be utilized, humanoid robots can leverage them to achieve balance. To address this issue, this paper established the state equation of a robot using a variable height-inverted pendulum model and implemented online trajectory optimization using model predictive control. For the arms' optimal joint angles during movement, this paper took the distributed polygon method to calculate the arm postures. To ensure that the robot reached the target position smoothly and rapidly during its motion, this paper adopts a whole-body motion control approach, establishing a cost function for multi-objective constraints on the robot's movement. These constraints include whole-body dynamics, center of mass constraints, arm's end effector constraints, friction constraints, and center of pressure constraints. In the simulation, four sets of methods were compared, and the experimental results indicate that compared to free fall motion, adopting the method proposed in this paper reduces the maximum acceleration of the robot when it touches the wall to 69.1 m/s2, effectively reducing the impact force upon landing. Finally, in the actual experiment, we positioned the robot 0.85 m away from the wall and applied a forward pushing force. We observed that the robot could stably land on the wall, and the impact force was within the range acceptable to the robot, confirming the practical effectiveness of the proposed method.

A memory and attention-based reinforcement learning for musculoskeletal robots with prior knowledge of muscle synergies

PurposeLimited by the types of sensors, the state information available for musculoskeletal robots with highly redundant, nonlinear muscles is often incomplete, which makes the control face a bottleneck problem. The aim of this paper is to design a method to improve the motion performance of musculoskeletal robots in partially observable scenarios, and to leverage the ontology knowledge to enhance the algorithm’s adaptability to musculoskeletal robots that have undergone changes.Design/methodology/approachA memory and attention-based reinforcement learning method is proposed for musculoskeletal robots with prior knowledge of muscle synergies. First, to deal with partially observed states available to musculoskeletal robots, a memory and attention-based network architecture is proposed for inferring more sufficient and intrinsic states. Second, inspired by muscle synergy hypothesis in neuroscience, prior knowledge of a musculoskeletal robot’s muscle synergies is embedded in network structure and reward shaping.FindingsBased on systematic validation, it is found that the proposed method demonstrates superiority over the traditional twin delayed deep deterministic policy gradients (TD3) algorithm. A musculoskeletal robot with highly redundant, nonlinear muscles is adopted to implement goal-directed tasks. In the case of 21-dimensional states, the learning efficiency and accuracy are significantly improved compared with the traditional TD3 algorithm; in the case of 13-dimensional states without velocities and information from the end effector, the traditional TD3 is unable to complete the reaching tasks, while the proposed method breaks through this bottleneck problem.Originality/valueIn this paper, a novel memory and attention-based reinforcement learning method with prior knowledge of muscle synergies is proposed for musculoskeletal robots to deal with partially observable scenarios. Compared with the existing methods, the proposed method effectively improves the performance. Furthermore, this paper promotes the fusion of neuroscience and robotics.

Exploring Kinematic Bifurcations and Hinge Compliance for In‐Hand Manipulation: How Could Thick‐Panel Origami Contribute?

Origami‐inspired mechanisms have found significant applications in end effector design. So far, the exploration of thick‐panel origami has been relatively limited, but it is worth noting that the incorporation of rigid thick panels can introduce unique mechanical properties, showcasing great potential in addressing manipulation challenges. Our previous work has developed a gripper from thick‐panel waterbomb origami, which can pick up a variety of daily objects. Based on the same prototype, this article extends the gripper's function from grasping to in‐hand manipulation, which is attributed to the kinematic bifurcations and compliance of thick‐panel origami. A kinematic study is carried out to investigate the gripper's bifurcated motion modes. The hinge compliance is also taken into account to enhance the gripper's motion dexterity. Theoretical analysis and experiments are conducted to demonstrate both features, thereby paving the foundation for achieving dexterous motions with a simplified control strategy. Aided by a differential mechanism, the gripper can effectively interact with objects with the actuation inputs from only two motors. Objects including balls, cuboids, and cones are explored for in‐hand manipulation under different motion modes, showing varied trajectories. With the integration of tactile sensors at the fingertips, we have also revealed the gripper's potential for classification tasks.

End Effect Reduction by Modification Design of End Teeth for Linear Induction Motor

Using linear induction motors for magnetic trains has challenges in maintaining stability and safety, namely the emergence of holding forces. The force arises due to the magnetic flux interaction between the permanent magnet and the iron core. One source of resisting force is due to the dotted lines, which creates a finishing effect and generates vibration and noise from the system. Previous research has carried out several works, one of which is designing the shape of a gear at the end of a tooth with a convex curved shape in a linear synchronous motor which has succeeded in reducing the effect of the tip of the tooth. This study aims to determine the results of the effectiveness of calculations using MATLAB, which can produce much less resistance. This research uses descriptive research with a qualitative approach. In this study the calculations to redesign the shape of the dental arch at the end of the track were carried out by making a model with a parabolic approach. Plane dimensions X, Y, Z 660mm x 360mmx 360 mm. Magnetic density data were collected using Ansys software and 10 mm meshing, while the magnitude of the resistive force based on mathematical equations was carried out using MATLAB software. Investigation of the effect of curvature for the final effect value has been carried out using 6 models with different arcs. From the simulation, it is concluded that the sharpener for bow curvature gives the smallest resistance force value. The smallest result from the calculation A=A_x+y^' A_y gives the smallest detention force value. The smallest computation of A=A_x+y^' A_y with the sharpest curvature is a challenge to build and use for real activities.

Active vibration control in robotic grinding using six-axis acceleration feedback

Industrial robots endure severe vibrations in the grinding process due to factors such as joint flexibility, low damping, and contact force fluctuations. Additionally, the motion control of the robot primarily relies on feedback signals from the motor-side encoders, while lacking motion state feedback from the load side. This exacerbates the robot vibrations, severely compromising the surface quality of the workpieces. To address this problem, an active damping control method for robotic grinding using six-axis acceleration feedback is proposed. This method realizes a simple, reliable, and efficient active vibration control without altering the robot body. A multi-sensor fusion Cartesian space six-axis accelerometer is designed and integrated into the robot end effector, achieving accurate feedback on the robot load side vibration state. The strategy of six-axis acceleration decoupling and principle of the vibration suppression with the acceleration feedback are explained. Compared with conventional sensing schemes, this reduces joint acceleration approximation errors and end vibrations. The robotic grinding experiments are carried out on aeronautic structural parts. Extensive case studies demonstrate that the proposed approach can effectively control the vibration caused by joint flexibility in the robot grinding process, and greatly improve the surface quality of workpieces. This method has great potential application in vibration suppression during robotic grinding and polishing.

Soft octopus-inspired suction cups using dielectric elastomer actuators with sensing capabilities

Bioinspired and biomimetic soft grippers are rapidly growing fields. They represent an advancement in soft robotics as they emulate the adaptability and flexibility of biological end effectors. A prominent example of a gripping mechanism found in nature is the octopus tentacle, enabling the animal to attach to rough and irregular surfaces. Inspired by the structure and morphology of the tentacles, this study introduces a novel design, fabrication, and characterization method of dielectric elastomer suction cups. To grasp objects, the developed suction cups perform out-of-plane deflections as the suction mechanism. Their attachment mechanism resembles that of their biological counterparts, as they do not require a pre-stretch over a rigid frame or any external hydraulic or pneumatic support to form and hold the dome structure of the suction cups. The realized artificial suction cups demonstrate the capability of generating a negative pressure up to 1.3 kPa in air and grasping and lifting objects with a maximum 58 g weight under an actuation voltage of 6 kV. They also have sensing capabilities to determine whether the grasping was successful without the need of lifting the objects.

Primary component segmental design to suppress the normal force ripple for the permanent magnet linear synchronous motor

AbstractThere is not only thrust but also normal force between the primary and secondary components for the single‐side flat plate permanent magnet linear synchronous motor (PMLSM). The normal force will fluctuate periodically, which will cause friction perturbation and finally affect the positioning accuracy of linear feed system. For the single‐side flat plate PMLSM, the variation law of normal force ripple under the coupling effect of multiple effects is studied, and the method of primary component segmental design to suppress it is proposed. Firstly, the influence of the cogging effect, the end effect and the armature reaction on the normal force ripple is analysed. Meanwhile the mathematical model of normal force ripple is established. Secondly, the PMLSM with segmental primary component is introduced and its suppression mechanism to the normal force ripple is expounded. On this basis, a scheme combining the non‐integer pole slot matching with the primary component segmental design is proposed to further suppress the normal force ripple. Finally, an 18‐slot 96/5‐pole prototype is taken as an example to carry out the finite element simulation and experimental test, of which the results verify the research theory in this paper.

An adaptive electropneumatic end effector for constant force robot grinding with steel wire brush

An adaptive electropneumatic end effector for constant force robot grinding with steel wire brush

Movement-goal relevant object shape properties act as poor but viable cues for the attribution of motor errors to external objects.

When a context change is detected during motor learning, motor memories-internal models for executing movements within some context-may be created or existing motor memories may be activated and modified. Assigning credit to plausible causes of errors can allow for fast retrieval and activation of a motor memory, or a combination of motor memories, when the presence of such causes is detected. Features of the movement-context intrinsic to the movement dynamics, such as posture of the end effector, are often effective cues for detecting context change whereas features extrinsic to the movement dynamics, such as the colour of an object being moved, are often not. These extrinsic cues are typically not relevant to the motor task at hand and can be safely ignored by the motor system. We conducted two experiments testing if extrinsic but movement-goal relevant object-shape cues during an object-transport task can act as viable contextual cues for error assignment to the object, and the creation of new, object-shape-associated motor memories. In the first experiment we find that despite the object-shape cues, errors are primarily attributed to the hand transporting the object. In a second experiment, we find participants can execute differing movements cued by the object shape in a dual adaptation task, but the extent of adaptation is small, suggesting that movement-goal relevant object-shape properties are poor but viable cues for creating context specific motor memories.

Robotic Arm Trajectory Tracking Using Image Processing and Kinematic Equations

This study presents a novel approach for scanning and obtaining object contours within a workspace using a camera and subsequently following these 2-D contours to another region with the assistance of an end effector. The process begins with the determination of boundary lines of object images through advanced image processing methodologies. Subsequently, a conversion from camera pixel coordinates to robot metric coordinates is performed to facilitate trajectory planning for the robotic arm. The trajectory of the robot arm, and consequently, the path followed by the end effector, is determined based on these metric coordinates. The article provides a detailed exposition of the methodology, including the mathematical background and experimental study, showcasing the efficacy and accuracy of the proposed approach in real-world scenarios. This research contributes to the advancement of robotic systems capable of precise trajectory tracking and manipulation tasks in diverse applications.

Chorda Dorsalis System as a Paragon for Soft Medical Robots to Design Echocardiography Probes with a New SOM-Based Steering Control.

Continuum robots play the role of end effectors in various surgical robots and endoscopic devices. While soft continuum robots (SCRs) have proven advantages such as safety and compliance, more research and development are required to enhance their capability for specific medical scenarios. This research aims at designing a soft robot, considering the concepts of geometric and kinematic similarities. The chosen application is a semi-invasive medical application known as transesophageal echocardiography (TEE). The feasibility of fabrication of a soft endoscopic device derived from the Chorda dorsalis paragon was shown empirically by producing a three-segment pneumatic SCR. The main novelties include bioinspired design, modeling, and a navigation control strategy presented as a novel algorithm to maintain a kinematic similarity between the soft robot and the rigid counterpart. The kinematic model was derived based on the method of transformation matrices, and an algorithm based on a self-organizing map (SOM) network was developed and applied to realize kinematic similarity. The simulation results indicate that the control method forces the soft robot tip to follow the path of the rigid probe within the prescribed distance error (5 mm). The solution provides a soft robot that can surrogate and succeed the traditional rigid counterpart owing to size, workspace, and kinematics.