POSITIONING ACCURACY CHARACTERIZATION OF PRECISION MICROROBOT OVER THE INTERNET

Modern agricultural machinery such as harvesting robotic arms are necessary to optimize the semi-automated agricultural processes to meet the ever-growing food needs of the population of the world. Production of low cost and highly efficient robotic arms are needed to meet the needs of the small-scale growers of underdeveloped countries. In this regard, mechanical design, sensor placement, and object identification are essential to optimize the performance of agricultural robots. This research is conducted to design a fully automated robotic arm for fruit picking in hydroponics system and research farm using machine vision to help the smallholders. For this purpose, a robotic arm with 1.3 m in height is designed using aluminum (18-gauge) and mild steel (MS-16) as fabrication material for the mechanical structure. The prototype design is tested for stress estimation at various payloads using SolidWorks simulations. The proposed robotic arm is based on a four degrees of freedom (4-DoF) design with the gripper having an opening range of 5.5 to 12 cm depending upon the task to handle various objects. The robotic arm is installed on an expandable vertical mobile platform that enables an expanded operational workspace with lower energy consumption. The total weight of the robotic arm is 60 kgs housing a payload capacity ranging between 8 and 10 kgs in accordance with industry-standard load-bearing specifications. The inverse kinematic algorithm using DH-table is computed with an accuracy of up to 95 % and target height of gripper is cross checked by mounting the ultrasonic sensor at the base of robotic arm. The YOLOv8 algorithm is used for object detection using the depth sense camera having angle range of 0.3 m to 3 m with the precision of up to 96 %. The time needed for recognition of fruits and pitching was about 15 s, with a success rate of up to 90 %. This research produces a low-cost and efficient solution for stallholders by developing a fully automated robotic arm for fruit picking. It optimizes agricultural productivity through improved mechanical design, sensor accuracy, and object detection which reduces labor costs by increasing efficiency of semi-automated farming systems. Further research is required to optimize the overall weight of the system and automation of the movement of robotic arm system between the consecutive rows to build a fully autonomous fruit picking operation in hydroponics.

The selection of orders is the method of gathering the parts needed to assemble the final products from storage sites. Kitting is the name of a ready-to-use package or a parts kit, flexible robotic systems will significantly help the industry to improve the performance of this activity. In reality, despite some other limitations on the complexity of components and component characteristics, the technological advances in recent years in robotics and artificial intelligence allows the treatment of a wide range of items. In this article, we study the robotic kitting system with a Robotic Mounted Rail Arm System (RMRAS), which travels narrowly to choose the elements. The objective is to evaluate the efficiency of a robotic kitting system in cycle times through modeling of the elementary kitting operations that the robot performs (pick and room, move, change tools, etc.). The experimental results show that the proposed method enhances the performance and efficiency ratio when compared to other existing methods. This study with the manufacturer can help him assess the robotic area performance in a given design (layout and picking a policy, etc.) as part of an ongoing project on automation of kitting operations.

Robotic systems have significantly transformed the landscape of minimally invasive surgery, offering enhanced precision, reduced recovery times, and improved patient outcomes. These systems have evolved from simple mechanical tools to sophisticated platforms integrating advanced control algorithms and real-time feedback mechanisms. This paper provides a comprehensive overview of the role of robotic systems in minimally invasive surgery, exploring the various benefits, inherent risks, and the challenges associated with their adoption and development. We examine the technical aspects of robotic control systems, such as kinematics, force control, and haptic feedback, which enable greater surgical accuracy. Despite these advantages, challenges such as system complexity, high costs, and ethical concerns remain significant barriers to widespread adoption. The future of robotic surgery lies in overcoming these obstacles, with advancements in system miniaturization and further refinement of control systems poised to enhance the efficacy and accessibility of robotic platforms. This paper explores the role of robotic systems in minimally invasive surgery, focusing on three key areas: bronchoscopy and ureteroscopy. By reviewing the benefits, risks, case studies, and future directions of these technologies, the paper seeks to provide an in-depth understanding of how robotic systems are shaping the future of surgery. Key words: Robotic Surgery, Minimally Invasive Surgery, Robotic Control Systems, Surgical Precision, PID Control, Kinematics, Inverse Kinematics, Forward Kinematics, Dynamic Control, Force Control, Impedance Control, Surgical Robotics, Haptic Feedback, Robotic Arm, Robotic Surgery Benefits, Robotic Surgery Risks, Robot-Assisted Surgery, Surgeon Console, Real-Time Feedback, Surgical Training, Robotic System Development, System Testing, Clinical Trials, Surgical Robot Performance, Robotics in Urology, Telepresence Surgery, Robotic Surgery Cost, Robotic Surgery Future Directions.

In this paper, an interactive robotic anthropomorphic hand system is presented, which was developed as an important tool for the educational outreach activities. The robotic anthropomorphic hand incorporates 15 degrees of freedom, providing sufficient mobility in the demonstration of various postures. To increase the attractiveness of the robotic hand, pneumatic muscle actuators are used to drive the robotic hand motion through artificial tendons. The interaction of the robotic hand with a human is enabled with a control device, which allows the human operator to control the hand motion in a natural way. The robotic hand system has been successfully demonstrated in a recent engineering education outreach event, in which over 100 children at all ages operated the robotic hand through the control device.

The rapid advancements in robotics and automation have highlighted the need for robotic systems capable of adapting to the diverse physical properties of objects. Traditional grippers often lack the versatility to handle both hard and soft objects without extensive reprogramming or hardware adjustments. Previous studies have explored various approaches to this challenge, including tactile sensors and force feedback mechanisms to distinguish object properties. For instance, research by Calandra et al. (2018) utilized deep learning with tactile data to enable robotic hands to identify objects and adjust grip accordingly. Similarly, the paper by Li et al. (2020) “Design and performance characterization of a soft robot hand with fingertip haptic feedback for teleoperation”, focuses on designing and characterizing a soft robotic hand with fingertip haptic feedback for teleoperation emphasizing real-time tactile sensing and feedback mechanisms. However, these studies primarily focus on tactile feedback or specialized hardware, limiting their applicability in scenarios where such systems are not available or practical.This study introduces a novel machine learning-based approach, focusing on the use of visual data alone to classify and adapt to the hardness or softness of objects. By leveraging the CIFAR-100 dataset, we trained a deep learning model based on the ResNet50 architecture, achieving significant results in binary classification of hard and soft objects. The CIFAR-100 dataset, consisting of 100 diverse object categories/classes, was reorganized into two classes: hard (39 categories) and soft (61 categories). The ResNet50 model, pre-trained on ImageNet, was fine-tuned specifically for this task, with modifications to the last 210 layers to enhance its adaptability.Data augmentation techniques, including rotations, translations, shearing, zooming, and horizontal flipping, were applied to simulate real-world variations, ensuring robust learning. The model was further refined with additional fully connected layers, dropout, and batch normalization to prevent overfitting. Optimized using the AdamW optimizer, the model achieved a training accuracy of 83.31% and a validation accuracy of 80.25%, with a test accuracy of 80%. The precision, recall, and F1-scores were 0.82, 0.86, and 0.84 for the soft object class, and 0.76, 0.71, and 0.73 for the hard object class, demonstrating the model’s effectiveness in distinguishing between hard and soft objects without the need for specialized sensors. It is also observed that the accuracy of the model in relation to hard objects is significantly lesser as compared to soft objects. It is understood that the CIFAR-100 dataset (comprising of 100 classes) is inadequate for model training, so we are exploring the ILSVRC (ImageNet subset) dataset for model training in future. The research is useful in a variety of fields where the focus lies on object handling. In everyday life, we encounter a wide array of objects with varying degrees of hardness, requiring different levels of care and precision during handling. By relying on visual data and machine learning algorithms, as demonstrated in this research, robotic systems can become more autonomous and versatile, reducing the burden on human operators and improving overall efficiency. This approach can lead to cost savings by reducing the need for specialized hardware, such as tactile sensors, which are often expensive and difficult to integrate.

A single-center randomized controlled study. The objective of this study was to introduce a novel robotic system with an independent arm ("Orthbot Intelligent Orthopedic Minimally Invasive System"; Xin Junte Surgical Technologies) that has been developed and tested as a surgical assistant for autoplacement of the Kirschner wire (K-wire) in lumbar fusion, and to evaluate its accuracy by comparing it with the conventional free-hand instrumentation. Robotic technology has performed excellently in spine surgeries and has demonstrated high clinical value and potential. Robot-assisted spinal surgery is now being promoted as a paradigm for technology-led advancement. A total of 24 patients were recruited and assigned randomly to the robotic arm group (RG) or the free-hand group (FG). Deviation distance and deviation angle (DA) of K-wire placement were measured and compared between the RG and the FG. The average deviation distance was 0.88±0.08 mm in the RG and 5.13±1.68 mm in the FG (P<0.001). In both coronal and sagittal radiographs, the average DA of K-wire placement was smaller in the RG (P<0.05), and in both axial and sagittal computed tomography scans, the average DA of pedicle screw placement was also lower in the RG (P<0.05), which indicated higher accuracy of the robotic system. The novel robotic system in this study has shown certain advantages over the conventional free-hand approach in K-wire placement for lumbar fusion, including being more accurate in K-wire placement, fully automatic, and more adaptive to preoperative plans. Although the robotic arm proves to be promising in our results, the small sample size in this clinical study necessitates further multicenter, large sample follow-up studies to verify its advantages.

This paper presents the development of Robot Technology Components (RTCs) using RT-Middleware (RTM) for designing assistive robotic arms and human-machine interfaces (HMIs) that can be used by the disabled. RTM is an open software platform for a component-oriented robot system. An RTC is the basic functional unit for constructing a robot system using RTM. The interface definition for the basic functionality of RTCs is standardized by the Object Management Group (OMG). Therefore, software modules written as RTCs can be shared and reused in other robot systems. One feature of an assistive robotic arm is that the arm is controlled by manual operation. Various HMIs for manually operating the robotic arms have been developed according to the abilities of each user and the requirements regarding the actions that the robot can perform. These HMIs can be interchangeably connected to the assistive robotic arms by implementing the software modules for the robotic arm and the HMI as RTCs with a common software interface.

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.

This study explores the incorporation of time-varying sign gain into a parallel iterative learning control (ILC) architecture, augmented by an expert system, to enhance the performance and stability of a robotic arm system. The methodology involves iteratively tuning the learning control gains using time-varying sign gain guided by an expert system. Stability analysis, encompassing asymptotic and monotonic convergence, demonstrates promising results across multiple joints, affirming the effectiveness of the proposed control architecture. In comparison with traditional PID control, fixed gain ILC, and ILC with adaptive learning in the expert system, the analysis focuses on stability, precision, and adaptability, using root mean square error (RMSE) as a key metric. The results show that ILC with adaptive learning from the expert system consistently reduces RMSE, even in the presence of learning transients. This adaptability effectively controls the learning transients, ensuring improved performance in subsequent iterations. In conclusion, the integration of time-varying sign gain with expert system assistance in a parallel ILC architecture holds promise for advancing adaptive control in robotic systems. Positive outcomes in stability, precision, and adaptability suggest practical applications in real-world scenarios. This research provides valuable insights into the implementation of dynamic learning mechanisms for enhanced robotic system performance, laying the groundwork for future refinement in robotic manipulator control systems.

With the continuous advancement of industrial automation, robotic arms have been widely adopted across various fields, making performance optimization a key research focus. As a critical component for enhancing robotic arm performance, kinematic analysis plays a vital role in achieving precise control. This study utilizes the MatlabRobotics toolbox to conduct kinematic analysis on the SNR3-C30 robotic arm. By establishing a mathematical model of the robotic arm and solving forward/reverse kinematic equations, we implemented a fifth-order polynomial interpolation algorithm for trajectory planning in the joint space. The research demonstrates that the established model accurately describes the robotic arm's motion characteristics, while the forward/reverse kinematic solution method proves effective and reliable. These findings provide crucial theoretical foundations for optimizing design and developing control strategies for the SNR3-C30 robotic arm, significantly improving operational precision and efficiency in real-world applications. This work holds great significance for advancing robotic arm technology development.

The NeuRobot is a telecontrolled microscopic micromanipulator system designed for neurosurgical procedures. The unit houses a three-dimensional endoscope and three robot arms that the surgeon operates without direct contact with the patient. The authors have successfully performed robotics-assisted neurosurgical procedures by using the NeuRobot in a 54-year-old man who had a recurrent atypical meningioma. Following the usual preparation of craniotomy and opening of the dura mater, a portion of the tumor was removed using the NeuRobot with the aid of microscopic observation. No complication related to the use of the NeuRobot was encountered and the patient's postoperative course was uneventful. Although various kinds of robots have been developed for use in neurosurgery in recent years, a robotic telemanipulation system capable of performing several surgical tasks has not previously been introduced to clinical neurosurgery. This is the first case report in which neurosurgical manipulation by a robotics system is described.

&lt;div class="htmlview paragraph"&gt;In this investigation a robotic system's dynamic performance is optimized for high reliability under uncertainty. The dynamic capability equations allow designers to predict the dynamic performance of a robotic system for a particular configuration (i.e.,point design). While the dynamic capability equations are a powerful tool, they can not account for performance variations due to aleatory uncertainties inherent in the system. To account for the inherent aleatory uncertainties, a reliability-based design optimization (RBDO) strategy is employed to design robotic systems with robust dynamic performance. RBDO has traditionally been implemented as a nested multilevel optimization process in which reliability constraints require solution to an optimization problem (i.e., reliability analysis). In this work a robust unilevel performance measure approach(PMA) is developed for performing reliability-based design optimization which eliminates the lower level problem in RBDO. A robotic test problem is used to illustrate the efficacy of the approach.&lt;/div&gt;

Problem statement: Sensitization of robot hand is still remaining as crucial issue since most of robot hand systems nowadays are only capable to grasp a predefined specific object. It is still difficult for robot hand system to realize human-like tactile sensation. Some common problems in robot hand system are low accuracy sensing device, sensors are not robust enough for long time work and heavy duties, inconsistence tactile sensing detection and difficulties in control of sensing fusion with robot trajectory. These problems are apparently drawback the progress to commercializing robot hands as real consumer products. Approach: This study presented the application of optical three-axis tactile sensor to robot hand to improve sensitization quality in robotic hand system. The proposed tactile sensor system was designed with compliance modules to communicate with robot hand control system. The sensing principle used in this tactile sensor comparatively provides better sensing accuracy to detect contact phenomena from acquisition of three axial directions of forces. Methodology of force and slippage detection in the tactile sensor system was presented. Accordingly, the optimization of robot hand control algorithm to comply with the tactile sensor system was presented and verified in experiment of grasping and twisting. Results: The tactile sensor presented in this study is capable of detecting normal and shear force simultaneously. The proposed methodology was verified in experiment with paper cup and water, in which the result shows the robot control system managed to respond to the proposed object stiffness distinction parameters and effectively respond to sudden change of object weight during grasping. An experiment of grasping and twisting motions was conducted using a bottle cap. In order to perform simultaneous grasping and twisting tasks, optimization of the control algorithm was conducted with additional parameters to satisfy the desired tasks. Conclusion: Experimental result shows that the robot hand managed to perform grasping and twisting of bottle cap smoothly. The overall results revealed good performance of the proposed optical three-axis tactile sensor system and robot hand control algorithm for future application in a real artificial robot hand. In addition, slippage sensation measured in a robot control system could contribute a better maneuvering of the robot arm-finger system.

Under rapid development of science and industry the industrial robots and robotic systems are more widely used in the field of industry. The robots and robotic systems help to manage and master new technological processes efficiently and effectively. In this article there was carried out the analysis of the two degree of freedom (DOF) robot arm, which is widely used in practice in order to reveal its functions and to identify its key dynamic parameters using fuzzy logic system. The mathematical model of robot arm dynamics is composed, which includes the mathematical models of the robot arm links twirling motors and the direct positions task model generating the end point of the robot. After the analysis of the robot model the expanded research was performed – after calculating the missing parameters of experimental 2DOF robot arm motors there was created the robot arm model with „V-REP“ and “MATLAB Simulink” software packages that allowed to set the control voltages and to observe the changing in time processes of link velocity trajectories. There is calculated designed planar robot end point trajectory deviation from the dynamic trajectory.

In this paper, we present the design and implementation of a robotic arm system equipped with both manual and automated control capabilities, facilitated by an Arduino Uno microcontroller and various peripheral modules. The robotic arm is constructed using three SG90 and three MG996R servo motors, providing multi-degree-of-freedom movement for precise manipulation tasks. Integration of an HC- 05 Bluetooth module enables wireless communication, allowing remote control and programming flexibility. A key feature of our system is its vision-based pick and place functionality, enabled by the inclusion of an OV7670 camera module. Utilizing image processing algorithms, the camera captures real- time footage of the arm's surroundings, facilitating object recognition and localization. This enables autonomous operation, where the arm can identify and manipulate objects based on predefined criteria. The versatility of our robotic arm system makes it suitable for a wide range of applications, including industrial automation, educational purposes, and research endeavours. By combining mechanical design, electronics, and software development, we demonstrate a comprehensive approach to the creation of intelligent robotic systems. Key Words: Robotic Arm, 6-DOF, Image Recognition