Performance Of Robot Research Articles

Robotics In Gynecology- A Review

This review article focuses on the development of robotics-based operations in the medical field especially in gynecology. The goal of the surgical robot is to improve surgical skills and compensate for human limitations. The robot's performance in performing duties correctly and consistently has been the key to its success. Tubal anastomosis was the first gynecologic surgery performed with Robotic assistance in 2000 followed by first Robotic Hysterectomy in 2002. In 2003 Da Vinci surgical system was developed which was FDA approved for gynecological surgery in 2005. It has gained popularity fast and is already playing a big part in many of the places where it is available. It consists of a high-resolution three-dimensional (3D) vision system adjacent to the patient on a cart with robotic branches. It provides unique technical advancement, with tremor filtration, improved ergonomics and lower muscular load as compared to both laparoscopic and open surgeries. Autonomous camera and energy instrument control, wrist articulation with 7 degree freedom, telestration and dual- console capabilities, overcoming the limitations of conventional laparoscopy, such as counterintuitive hand movements, 2 dimensional visualization, limited degree of motion and tremor amplification. Compared to laparoscopy, robotic assistance has a lower conversion rate to open procedures. Endo wrist movement of an automated machine during myomectomy surgery can perform proper and better suturing than traditional laparoscopy. The automated program is a noticeable improvement over laparoscopic surgery and, if price issues are resolved, can gain popularity among gynecological surgeons around the globe. Not unique to robotic system, the integration of indigo cyanine green fluorescence with the Da Vinci robotic system allows identification of lesions, and assessment of perfusion of bowel and ureter during deep infiltrating endometriosis resection.

Pose Selection Based on a Hybrid Observation Index for Robotic Accuracy Improvement

The problem of the insufficient accuracy performance of industrial robots in high-precision manufacturing is addressed in this paper. Firstly, a kinematic error model based on an M-DH model was presented. Secondly, a hybrid observability index O6 was proposed to select the optimal poses for parameter identification. O6 is the combination of O1 and O3. The optimal poses were obtained by using the IOOPS algorithm. Thirdly, the fitness function for parameter identification was established, and the Levenberg–Marquardt (LM) algorithm was applied for the accurate identification of kinematic parameter errors. Finally, several experiments were conducted to evaluate the performance of the proposed hybrid observability index O6. The average position error and average attitude error of Staubli TX60 robot were reduced by 89% and 49%. The results show that the proposed hybrid observability index O6 has great stability and effectiveness for robot calibration.

Kinematic of 3-Wheels Swerve Drive Using BLDC Motor

The stability of the robot's performance is very important, especially for the wheeled mobile robots that use swerve drives, which need kinematic control to reach the destination point. The study of robot movement known as kinematics is based on an examination of the geometric structure of the robot, with no consideration given to the mass, force, or acceleration that the robot experiences during movement. This study aims to model and simulate the kinematic control design of a wheeled robot that uses a swerve drive. This robot uses BLDC motor actuator so that the robot can reach its destination very quickly and steadily. The test is carried out by simulating and comparing the performance response using BLDC motors and DC motors. According to the testing and trials, the robot can reach its destination by modeling its kinematic control, and BLDC motors are found to be more reliable and efficient for driving and steering than DC motors.

Decentralized control mechanism for limb steering in quadruped robot walking

Quadrupedal mammals adaptively change their direction, and their behavior is remarkable when they initiate such turns. They flexibly alter their interlimb coordination patterns when transitioning from forward to turning motion. However, the turning performance of quadruped robots is low compared to that of animals. We attempted to understand the control mechanism underlying the animal's flexible turning behavior by developing a simple robot model. We hypothesize that animals achieve lateral acceleration during transitions by adjusting the position of their limbs sideways at ground contact. On this basis, we proposed a decentralized control model for limb steering in quadrupedal turning behavior. The results obtained from the robot simulation demonstrated that either a turn initiated by the medial forelimb or the lateral forelimb occurred depending on the timing of the turn command. The fact that the proposed control algorithm could reproduce the behavior observed in animals suggests that a mechanism similar to the algorithm may exist in animals. Biological verification is expected in the future.

Novel design and motion analysis of an omni-tread snake-like robot for narrow space inspection

Snake-like robots have a slender body and strong environmental adaptability. This paper aims to propose an omni-tread snake-like robot that can adapt to the needs of specific narrow space exploration missions and provide a basis for design work through necessary motion analysis. The robot adopts the form of three modules in series, and the modules are connected through differential driving joints, which increases the joint torque of the robot. The nested omni-tread structure improves the robot's motion efficiency and environmental adaptability. This paper conducts configuration and motion analysis of the robot. The simulation is performed using the optimization solution method to obtain the joint torque and walking torque of the robot, which guides the design of the robot. The experiment is also conducted with the developed prototype to verify the robot's performance. From the simulation results, the joint torque and walking torque are obtained, and the motors are selected. The motion performance and field applicability of the robot are verified through experiment tests. The experiment results further verify the robot design and analysis work. The structural design of robots and optimization solution method in this paper has certain reference values for other researchers.

Disturbance Observer-Based Adaptive Fault Tolerant Control with Prescribed Performance of a Continuum Robot

This paper studies an adaptive fault tolerant control (AFTC) scheme for a continuum robot subjected to unknown actuator faults, dynamics uncertainties, unknown disturbances, and prescribed performance. Specifically, to deal with uncertainties, a function approximation technique (FAT) is employed to evaluate the unknown actuator faults and uncertain dynamics of the continuum robot. Then, a nonlinear disturbance observer (DO) is developed to estimate the unknown compounded disturbance, which contains the unknown disturbances and approximation errors of the FAT. Furthermore, the prescribed error bound is treated as a time-varying constraint, and the controller design method is based on an asymmetric barrier Lyapunov function (BLF), which is operated to strictly ensure the steady-state and transient performance of the continuum robot. Afterwards, the simulation results validate the effectiveness of the proposed AFTC in dealing with the unknown actuator faults, uncertainties, unknown disturbances, and prescribed performance. Finally, the effectiveness of the proposed AFTC scheme is verified through experiments.

A novel framework inspired by human behavior for peg-in-hole assembly

PurposeThe purpose of this paper is to enhance the performance of robots in peg-in-hole assembly tasks, enabling them to swiftly and robustly accomplish the task. It also focuses on the robot’s ability to generalize across assemblies with different hole sizes.Design/methodology/approachHuman behavior in peg-in-hole assembly serves as inspiration, where individuals visually locate the hole firstly and then continuously adjust the peg pose based on force/torque feedback during the insertion process. This paper proposes a novel framework that integrate visual servo and adjustment based on force/torque feedback, the authors use deep neural network (DNN) and image processing techniques to determine the pose of hole, then an incremental learning approach based on a broad learning system (BLS) is used to simulate human learning ability, the number of adjustments required for insertion process is continuously reduced.FindingsThe author conducted experiments on visual servo, adjustment based on force/torque feedback, and the proposed framework. Visual servo inferred the pixel position and orientation of the target hole in only about 0.12 s, and the robot achieved peg insertion with 1–3 adjustments based on force/torque feedback. The success rate for peg-in-hole assembly using the proposed framework was 100%. These results proved the effectiveness of the proposed framework.Originality/valueThis paper proposes a framework for peg-in-hole assembly that combines visual servo and adjustment based on force/torque feedback. The assembly tasks are accomplished using DNN, image processing and BLS. To the best of the authors’ knowledge, no similar methods were found in other people’s work. Therefore, the authors believe that this work is original.

Enhancing Robotic Grasping Performance through Data-Driven Analysis

In automation, reliability in robotic grasping in dynamic environment is still a problem encountered. Further, there is the need to consider deep learning methods, as traditional approaches are not easily flexible in dealing with different objects and situations. In this work, we aim to analyze how well deep neural network models perform in predicting grasp strength based on data collected from the Smart Grasping Sandbox simulation trials. Hence, the proposed approach for analyzing the joint positions, velocities and efforts led to the design of a deep neural network for improving robot grasp performance. The question here could is if deep learning could help better predict grasp stability. To implement the method, we underwent rigorous data pre-processing such as outlier detection, and feature normalization and employed a structured neural network model for training. Our model got a training accuracy of nearly 99% and the test accuracy of nearly 96% demonstrating significant promise. These results were also better than CNN and LSTM where their accuracy rate was 94.12% and 91.81%, respectively. High deep learning performance was proven in robotic grasping, which can contribute to the creation of more flexible and sophisticated robotic platforms. This work also provides the foundation for subsequent research in robotics and automation, with emphasis on the role of data-driven methods in robotic grasping tasks.

A closed-loop kinematic approach to Self-Calibration of joint offset for multi-branched robots

Kinematic accuracy is fundamental for the performance of multi-branched robots, which are composed of multiple branches that collaborate to manipulate. Among those various factors affecting kinematic accuracy, the joint offset is the most significant contributor due to misalignment in component assembly. In this work, we present a general, fast, and low-cost approach based on closed-loop kinematics to calibrate joint offsets of multi-branched robots. A dedicated calibration board connects the distal ends of branches to form closed-loop configurations. The identification model is derived based on the closed-loop kinematic differential model. The observable indexes of multiple closed-loop are integrated to optimize the calibration board’s poses, and joint offsets are identified simultaneously. To enhance the robustness of the calibration outcomes against joint backlash noise, the Kolmogornov-Smirnov(K-S) test is performed on multiple sets of identification results to verify that joint offset obeys normal distribution. Experiments on a quadruped showed significantly improved foot-tip position accuracy.

Wall-climbing performance of gecko-inspired robot with soft feet and digits enhanced by gravity compensation

Gravitational forces can induce deviations in body posture from desired configurations in multi-legged arboreal robot locomotion with low leg stiffness, affecting the contact angle between the swing leg’s end-effector and the climbing surface during the gait cycle. The relationship between desired and actual foot positions is investigated here in a leg-stiffness-enhanced model under external forces, focusing on the challenge of unreliable end-effector attachment on climbing surfaces in such robots. Inspired by the difference in ceiling attachment postures of dead and living geckos, feedforward compensation of the stance phase legs is the key to solving this problem. A feedforward gravity compensation (FGC) strategy, complemented by leg coordination, is proposed to correct gravity-influenced body posture and improve adhesion stability by reducing body inclination. The efficacy of this strategy is validated using a quadrupedal climbing robot, EF-I, as the experimental platform. Experimental validation on an inverted surface (ceiling walking) highlights the benefits of the FGC strategy, demonstrating its role in enhancing stability and ensuring reliable end-effector attachment without external assistance. In the experiment, robots without FGC only completed 3 out of 10 trials, while robots with FGC achieved a 100% success rate in the same trials. The speed was substantially greater with FGC, achieving 9.2 mm s−1 in the trot gait. This underscores the proposed potential of the FGC strategy in overcoming the challenges associated with inconsistent end-effector attachment in robots with low leg stiffness, thereby facilitating stable locomotion even at an inverted body attitude.

Beyond simulation: Unlocking the frontiers of humanoid robot capability and intelligence with Pepper's open-source digital twin

This research paper presents a high-fidelity, open-source digital-twin of the Pepper robot developed within the framework of the Robot Operating System 2 (ROS 2) for better simulation realism in complex tasks of machine learning. We developed a dedicated, custom ROS 2 package with modern simulation tools, such as Gazebo Sim, MoveIt 2, Rviz2, that brings complete, realistic environments in line with the exact behaviors and interactions of robots in reality. Better accuracy of the physical movement of Pepper robot's simulation was shown on the digital twin, validated by the Choregraphe software and real robot performance, to be a strong platform of collaboration and further research by the community. This development greatly pushes the envelope of human-like humanoid robotics further by offering a scaled, flexible, and plausible training environment conducive to integrating complex algorithms of robot learning and interaction capabilities.

Comparison of accuracy, revision, and perioperative outcomes in robot-assisted spine surgeries: systematic review and meta-analysis.

Pedicle screw placement guidance is critical in spinal fusions, and spinal surgery robots aim to improve accuracy and reduce complications. Current literature has yet to compare the relative merits of available robotic systems. In this review, the authors aimed to 1) assess the current state of spinal robotics literature; 2) conduct a meta-analysis of robotic performance based on accuracy, speed, and safety; and 3) offer recommendations for robotic system selection. Following PRISMA guidelines, the authors conducted a systematic literature review across PubMed, Embase, Cochrane Library, Web of Science, and Scopus as of April 28, 2022, for studies on approved robots for placing lumbar pedicle screws. Three reviewers screened and extracted data relating to the study characteristics, accuracy rate, intraoperative revisions, and reoperations. Secondary performance metrics included operative time, blood loss, and radiation exposure. The authors statistically compared the performance of the robots using a random-effects model to account for variation within and between the studies. Each robot was also compared with performance benchmarks of traditional techniques including freehand, fluoroscopic, and CT-navigated insertion. Finally, we performed a Duval and Tweedie trim-and-fill test to assess for the presence of publication bias. The authors identified 46 studies, describing 4670 patients and 25,054 screws, that evaluated 4 different robotic systems: Mazor X, ROSA, ExcelsiusGPS, and Cirq. The weighted accuracy rates of Gertzbein-Robbins classification grade A or B screws were as follows: ExcelsiusGPS, 98.0%; ROSA, 98.0%; Mazor, 98.2%; and Cirq, 94.2%. No robot was significantly more accurate than the others. However, the accuracy of the ExcelsiusGPS was significantly higher than that of traditional methods, and the accuracies of the Mazor and ROSA were significantly higher than that of fluoroscopy. The intraoperative revision rates were Cirq, 0.55%; ROSA, 0.91%; Mazor, 0.98%; and ExcelsiusGPS, 1.08%. The reoperation rates were Cirq, 0.28%; ExcelsiusGPS, 0.32%; and Mazor, 0.76% (no reoperations were reported for ROSA). Operative times were similar for all robots. Both the ExcelsiusGPS and Mazor were associated with significantly less blood loss than the ROSA. The Cirq had the lowest radiation exposure. Robots tended to be more accurate and generally their use was associated with fewer reoperations and less blood loss than freehand, fluoroscopic, or CT-navigated techniques. Robotic platforms perform comparably based on key metrics, with high accuracy rates and low intraoperative revision and reoperation rates. The spinal robotics publication rate will continue to accelerate, and choosing a robot will depend on the context of the practice.

Modeling and Simulation of 4-DOF RRPR Manipulator Robot Using MATLAB

In recent decades, the rapid development of robotic technology has increased the demand for efficient and affordable robots, especially in the manufacturing industry. One type of robot that is commonly used is the robotic arm, which is capable of performing complex tasks with high precision and speed. This research focuses on the modeling and simulation of a 4-DOF RRPR robot manipulator using MATLAB software, including the SimScape Multibody Toolbox and Robotic System Toolbox. This study investigates various aspects of robot performance, such as joint angles, end-effector coordinates, and robot dynamics. With an emphasis on simulation, this research aims to accelerate the development of robotic technology and minimize the risks associated with physical implementation in the field. The simulation results provide valuable insights for improving the efficiency, precision, and reliability of robot manipulators in various applications. Furthermore, this research suggests future research directions, such as the exploration of advanced control systems to dynamically compensate for disturbances and the investigation of robots with higher degrees of freedom for more adaptive technology in challenging operational conditions.

Robotic performance metrics model fellow proficiency in living donor nephrectomy

Robotic performance metrics model fellow proficiency in living donor nephrectomy

AN EXPERIMENTAL INVESTIGATION ON NOVEL SEGMENTED OMNI WHEELED HYBRID MOBILE ROBOT TO ASSESS OBSTACLE CLIMBING CAPABILITY

Abstract This article introduces a novel quadrupedal hybrid mobile robot with a segmented omni wheel design capable of climbing obstacles of height greater than its wheel radius. The unique wheel design comprises of two parallel coaxial 240-degree segmented omni wheels per leg with independent offset angle control. The transformation from wheel to leg mode and vice versa can be seamlessly achieved by controlling the offset angle between the segmented omni wheels. A series of experiments are carried out to evaluate the robot's performance for obstacle climbing capability utilizing various wheel end effector designs, and a comparative analysis is presented. The results indicate that the obstacle climbing capability of segmented omni wheel design with zero offset angle is improved by 40.7% compared to standard wheel and the unique wheel design configuration facilitates seamless staircase climbing capability without any complex control scheme. In addition, statistical studies using Taguchi method and ANOVA are performed on the experimental findings. A linear regression model to predict power consumption has been developed and the significant factors influencing the robot's power consumption and stability are presented.

Particle Filter Enhanced by CNN-LSTM for Localization of Autonomous Robot in Indoor Environment

Particle filters play a crucial role in indoor localization tasks where GPS or similar sensors are unavailable, owing to their adaptability to dynamic environments and contribution to precise navigation. However, despite their effectiveness in non-Gaussian and non-linear settings, particle filters face challenges such as issues with particle initialization and recovery from scenarios like robot kidnapping. These limitations impede the complete autonomy of robots, a critical aspect of their functionality. This research presents a novel solution that integrates particle filters with a CNN-LSTM network to address these challenges. Leveraging the time-series image processing capabilities of CNN-LSTMs, this architecture aids in particle filter initialization and facilitates recovery from challenging situations. The integration enhances the overall performance and autonomy of robots, making them more efficient in indoor navigation tasks.

Entertainment robots based on swarm intelligence algorithm applied in remote dance performances

With the continuous development of technology, the application of entertainment robots in the field of dance performances is receiving increasing attention. This article focuses on the application of entertainment robots based on swarm intelligence algorithms in remote dance performances, aiming to design an intelligent dance performance control system to achieve precise and smooth dance performances of robots. The study introduces the basic artificial fish swarm algorithm and combines it with mobile robot path planning, proposing a mobile robot path planning method based on swarm intelligence algorithm. By simulating the behavior of fish in schools, the optimization of path planning is achieved. The control system process of the entertainment robot intelligent dance performance control system includes preparation work before the dance performance, data transmission and dance posture control during the performance process, and post performance organization work. The experiment verified the effectiveness and reliability of the intelligent dance performance control system for entertainment robots. The experimental results show that the system can achieve precise, smooth, and diverse dance performances, providing a novel and interesting form of dance performance for the audience.

Design and movement mechanism analysis of a multiple degree of freedom bionic crocodile robot based on the characteristic of “death roll”

AbstractThis paper introduces a multi‐degree of freedom bionic crocodile robot designed to tackle the challenge of cleaning pollutants and debris from the surfaces of narrow, shallow rivers. The robot mimics the “death roll” motion of crocodiles which is a technique used for object disintegration. First, the design incorporated a swinging tail mechanism using a multi‐section oscillating guide‐bar mechanism. By analyzing three‐, four‐, and five‐section tail structures, the four‐section tail was identified as the most effective structure, offering optimal strength and swing amplitude. Each section of the tail can reach maximum swing angles of 8.05°, 20.95°, 35.09°, and 43.84°, respectively, under a single motor's drive. Next, the robotic legs were designed with a double parallelogram mechanism, facilitating both crawling and retracting movements. In addition, the mouth employed a double‐rocker mechanism for efficient closure and locking, achieving an average torque of 5.69 N m with a motor torque of 3.92 N m. Moreover, the robotic body was designed with upper and lower segment structures and waterproofing function was also considered. Besides, the kinematic mechanism and mechanical properties of the bionic crocodile structure were analyzed from the perspectives of modeling and field tests. The results demonstrated an exceptional kinematic performance of the bionic crocodile robot, effectively replicating the authentic movement characteristics of a crocodile.

Research on the Influence of Radial Variation of Centroid on the Motion of Spherical Robot

Through the pendulum mechanism inside the spherical shell, the centroid can be varied circumferentially, enabling the spherical robot to achieve omnidirectional flexible movement. Additionally, the radial variation ability of the centroid enables spherical robots to adopt two distinct driving modes: the traditional lower pendulum driving mode and the inverted pendulum driving mode. There are two manifestations of radial variation in the centroid: having different radial positions of the centroid and achieving radial movement of the centroid. Focusing on these two manifestations, experimental data are obtained through different motion velocities and different motion slopes to conduct research on the influence of radial variation in the centroid on the motion of spherical robots. Based on the experimental data, multiple indicators are analyzed, including response speed, convergence speed, stability, and overshoot, as well as steering ability, climbing ability, and output power. The impact of the radial variation ability of the centroid on the control performance, locomotion capability, and energy consumption of spherical robots is summarized, and the correlation model relating the motion requirements to the radial position of the centroid is established, providing a theoretical basis for the selection of driving modes and centroid positions for spherical robots facing complex task requirements.

Design and Development of a Package Delivery Robot

This research focuses on the design and development of a package delivery robot, comprising an electronic housing and a storage unit. The robot is remotely controlled through a website, equipped with obstacle detection capabilities, and capable of unlocking the storage unit. However, movement is limited when the storage unit is installed on the electronic housing. The objective of this project was to build a package delivery robot that could operate in organised environments, such as universities and estates. The project commenced with extensive research on existing delivery robots, analysing their strengths and weaknesses. Based on this analysis, a set of design requirements was formulated, emphasising compact size, lightweight construction, and user-friendly operation. A custom control system was developed, enabling robot navigation, while sensors were integrated to facilitate obstacle avoidance and pedestrian safety. The first chassis of the robot was constructed using lightweight sheet metal, but turned out to be too heavy for the motors to drive, so the chassis was reconstructed using corrugated cardboard. To evaluate its performance, the robot underwent rigorous testing in a simulated urban environment. The robot has a speed of 0.5 m/s, and receives commands at a speed of 0.0068 nano seconds. It also consumes a power of 120 watts. While the robot demonstrated below-average performance in these aspects, limitations were identified, such as the inability to make left or right turns due to the shaft that connects the tyre and the motor being short. In conclusion, this research introduces a package delivery robot that has the potential to significantly enhance the customer delivery experience in urban environments. The robot represents progress in delivery system development and provides valuable insights into the challenges and opportunities within the field of mechatronics. The introduction, methodology, results, and conclusion framework used in this project offers a comprehensive understanding of the research and development process, the robot's performance, and its impact on the field.