Automatic cutting and suturing control system based on improved FP16 visual recognition algorithm

The main objective of this study is to develop a shared design of master and slave devices for bilateral teleoperation mechanisms used for robotic excavators in agricultural applications. In robot teleoperation research, many potential applications within controlled and hazardous environments come to light. Robots, with their capacity for remote control by human operators through master devices, often employ the master-slave teleoperation approach. This strategy finds frequent use across manufacturing, construction, and agriculture industries. The master-slave system necessitates two interdependent components that collaboratively steer the robot in real time. However, challenges arise when manipulating the robot arm proves challenging due to structural differences between the master device (such as a joystick) and the slave device (the robot arm). A stable operational framework must be established for the robot to function optimally. This teleoperation system employs a master device to govern the actions of the slave device, a dynamic that heavily influences the operational complexity. Hence, the focal point of this study is to enhance the master-slave algorithm for teleoperation applications that rely on controlling robot arm movements. Despite the differing dimensions of the master and slave devices, they both share a common structure. The kinematic model bridging these components must be intelligible to ensure user-friendliness, facilitating effortless robot control. Calculating the robot arm's end effector movement and positioning involves employing the forward kinematics of the arm, determined through Denavit-Hartenberg parameters and transformation matrices. By mitigating communication delays between the master and slave devices using a technique centered around the robot arm's end effector position, the effectiveness of teleoperation can be significantly improved. Our designed robot arm attains 80% to 100% precision across joints. In summary, streamlining the robot arm's structure and minimizing delays offers a route to bolstering both stability and efficiency in robotic movement.

With the increasing global population, the demand for agriculture is also on the rise. The crucial stages of agricultural production, namely fruit identification and picking, play a vital role in enhancing product quality and minimizing losses. Traditional manual processing methods, although time-tested, are not only inefficient but also challenging to maintain consistency, making them inadequate to meet the large-scale requirements of modern agricultural production. Consequently, the integration of automation technology has become a necessity. For agricultural robot the machine vision system often need to work in two typical environments, the field environment and the orchard environment. Depending on the varying objectives of operation in diverse environments, crop robots require the ability to rapidly identify fruits within images featuring significant color disparities between crops and two-dimensional field backgrounds. Consequently, a visual servo control system is being investigated. A novel camera attitude search method that employs active visual servo technology to minimize occlusions during orchard search is proposed. The recognition function of the end-effector is exceedingly crucial. The precision of the end effector's identification capabilities directly influences the success rate of automated operations. This is particularly evident in fruit picking, sorting, and other tasks, where the diverse shapes, maturity levels, and colors of fruits present significant challenges to the robotic arm's end effector. The rapid advancement of deep learning technology, however, offers a novel solution for the recognition of fruits by the robotic arm's end effector. By emulating the human visual system, deep learning models can extract the feature representation of fruits from vast amounts of data, enabling accurate identification of fruits in various conditions.

Object pose estimation is one of the crucial parts in vision-based object manipulation system using standard industrial robot manipulator, particularly in pose estimation of the end effector of the robot arm to grasp the object targeted. This paper presents the utilization of stereo vision system to estimate the 3D (3 dimensional) object position and orientation to pick up and place the object targeted in an arbitrary location within the workspace. In order to accomplish this task, a calibrated stereo camera in the eye to hand configuration is used to capture the images of the object on the left and right camera. Then, the specific object feature is extracted and the 3D position and orientation of the object are calculated using image processing algorithm. Finally, the end effector of robot arm equipped with gripper will pick up the object targeted according to the object pose estimation output, and then place it to the desired location. The experimental results using 6 DOF robot arm are demonstrated and show the effectiveness of the proposed approach with good performance.

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.

The robot arm is a device consisting of a moving chain of links connected by joints. Electrical motors are frequently used to move each robot arm joint. An end-effector that can move freely in space is usually attached to one end of the robot platform, which is fixed. Robot arms can do repetitive operations at rates and precision far exceeding human operators. Nowadays, robot arm systems are widely used worldwide to increase the quality and efficiency of the manufacturing process in the industry. Typical applications of the robot arm system are assembly, painting, welding, pick and place operation, and others. Besides, many industries employ robot arms for various jobs such as selecting and putting, painting, and material handling. However, one of the most challenging issues in completing these jobs is determining the target location of the robot arm's end-effector. There are two different methods for analyzing the robot arm's movement: forward and inverse kinematic analysis. Based on the visual servo algorithm, this study uses inverse kinematics to execute the pick and place operation. First, an object recognition algorithm is implemented to identify the object to be grasped. Then, an algorithm to avoid any obstacles is done. The study's findings show that good system performance has been obtained in all three algorithms: first, object recognition algorithm, second, obstacle avoidance algorithm, and lastly, visual servobased pick and place operation. Thus, it can be concluded that the robot arm's visual servo algorithm is suitable for pick-and-place applications.

This project shows a robotic arm that can pick up objects. It was made with accuracy and speed in mind for use in factories. The robotic arm is very good at picking up metal nuts. It uses cutting edge technologies, like Haar cascade to find objects and inverse kinematics to figure out angles very accurately, to make its moves more exact and dexterous. A powerful computer vision method called Haar cascade is used to find metal nuts in the robotic arm's working environment. To do this, positive and negative pictures are used to train a Haar cascade classifier, which makes a model that can recognize the unique traits of metal nuts. The object recognition process makes sure that the nuts are located correctly, which lets the robotic arm deal with them in a controlled and precise way. The robotic arm uses inverse kinematics along with Haar cascading to figure out the exact joint angles needed for smooth, controlled movement. Inverse kinematics is very important for figuring out the joint setups that are needed to place the robotic arm's end-effector exactly on top of the metal nuts that have been found. Haar cascading for finding objects and inverse kinematics for accurate angle estimates work together to make sure that the robotic arm can pick up metal nuts in a variety of space arrangements. This combined system is a high-tech way to automatethe picking of specific items in manufacturing and assembly lines. It shows how industrial automation can be used to improve accuracy, efficiency, and flexibility.

A dynamic gripper with qualities that resemble the human hand as closely as possible is sought after in the field of robotics. The idea of a robotic arm has been used in various cutting-edge technology fields, including agriculture, to assist people or farmers in carrying out regular tasks, such as gathering fruit, etc. The robot arm's end effector is one of the essential parts of the robot that we can configure based on their tasks, such as a spraying adaptor for fertilization function or a gripper for the picking mechanism. Since fruits have a delicate and fragile surfaces, it is vital to have a gripper with a smooth contact surface that can apply the right amount of force to pick the fruits without causing any bruising that can degrade the crop's quality. Hence, this paper proposes a robotic arm gripper design for the crop-picking mechanism using a force sensor as the main component of the Arduino Uno embedded system. The reliability result for the chili obtained is around 95% showing that this design is promising for designing an adaptive robotic arm gripper.

A custom-made robot arm that is specially designed for a given task is better than a general-purpose robot arm based on the performance index it is designed for. In industries, however, a general-purpose robot arm is prevalently used since it can perform over several and varying tasks. Because of this, a custom-made robot arm becomes impractical due to its high fabrication cost. In this study, we propose the design of tool attachment as a cost-effective and alternative method for improving the performance of a general-purpose robot arm. Wherein, the task completion time is the performance index in designing the tool attachment. A tool attachment, a passive linkage attached between the end-effector of robot arm and tool, is customized for a given task. We showed that the proposed method is effective by employing it in a task with multiple reconfigurable goals, or goals that can be rearranged and can be positioned by a table.

This paper presents a direct way of modeling the Kinematics of an Articulated Robotic Arm. The range for industrial application of this configuration is wide. Kinematics includes Forward Kinematics and inverse Kinematics. Forward kinematics refers to conversion of joint angles to end effectors position whereas Inverse Kinematics refers to conversion of world co-ordinates of end effector to joint angles of the Arm. The proposed method uses spherical co-ordinate system to model it's both Forward and Inverse Kinematics. Using this model, path planning for different scenarios can be done. Implementation of different algorithms to evaluate effective path by either avoiding obstacles or by passing through intermediate path points is possible. End effector of Robotic Arm easily follows complex 3D space curves like Circular Helix and Conical Helix, etc. using this algorithm. The above-mentioned algorithm also has been tested in scenarios where real time inputs from user are used for robotic arm actuation using a custom designed software.

Placing dental implants in humans is a task that requires a large amount of dexterity, effort and precautionary measures on behalf of the dentist. One of the most important sub-task of this procedure is drilling a hole in the jaw of patient. Increasing advances in the field of robotics have made it possible to build robotic arms that can attain the same degree of precision as that of dentist for this sub-task, thus opening a possibility of automating the entire process which would save time and effort of dentists. In addition, vision equips a robot with a large amount of information like the objects in surroundings, their shape, color, etc. This information on being processed can be used to perform actions by a robot to interact with the surroundings and accomplish a certain goal. In this paper, one such application of computer vision has been discussed to accomplish the goal of automation of end effector guidance for dental implantation in human mandible by means of a robotic arm. Computer vision has been used to establish a closed loop feedback control system for the robotic arm, so as to make the end effector of robotic arm reach the desired location for drilling the hole in jaw. Template matching is used to detect the desired point in subsequent frames captured by a camera mounted at the end effector also consisting of a drilling machine. The system proposed in this paper reliably establishes complete automation of the process, thus demanding only a mouse click effort from the dentist.

Hazardous environments gave a big challenge for the human to accomplish the desired tasks, especially in sampling and collecting materials either for securing the locality or research purposes. Hence, teleoperated robots with robotic arms have been proposed to execute any tasks that appointed. However, most robotic arms on the teleoperated robots only serve specific tasks and have no ability to retract arm with precise and smooth movement, which will compromise its functionality. In this paper, five designs of robotic arms were proposed with different mechanisms for its retractions. Evaluation and selection for the final design was decided based on the decision-making process for all designs. Considering important criterion, evaluation was performed using concept scoring. All concept designs were modelled and analysed in SolidWorks. Load was applied on the end effector of robotic arm to observe Von Mises criteria of designed arms. Both methods indicate that design 5 with linear motor mechanism has the best endurance and thus, chosen as the most suitable design to be mounted on the track vehicle.

Recently, the robot industry has developed and is doing human work instead. Among them, Visual Servoing research is actively underway to control robots using vision sensors. In this study, using OpenCV, a machine vision open-source library, to detect the contours of the object and the central point of the object. Machine Vision algorithms are performed using features such as Grayscale, Gaussian filter, Canny Edge, Contouring. The proposed algorithm recognizes only objects on the workstation without detecting other things. Also, transform the coordinate system of the vision sensor into the robot arm's coordinate system so that the robot arm can move correctly around the center of the object. For this purpose, camera calibration was performed, and a transformation matrix about vision to robot was obtained. Furthermore, we aim to control the 6-DOF robot arm through experiments to move the robot arm's end-effector to the center point of the object detected by the proposed algorithm.

Inspired by human's tactile sensing in daily lives, we present an approach using the tactile sensing plus force-torque information as the feedback to control the robotic arm interacting with our soft human skin in this paper. Three main types of contact between the end-effector of robotic arm and human skin are introduced and the contact model is built up. Hybrid impedance control method is applied to control both the position and force trajectories of the manipulator at the same time. With the feedback of tactile sensing data such as contact state, contact area and so on, several strategies of tactile sensing feedback are included in the control algorithm. Two groups of real experiments are made using a two-link robotic arm equipped with force/torque and tactile sensors to contact with human skin. The results have confirmed the effectiveness of the proposed strategies.

Human-robot interaction plays a major role in designing assistive robots. A robotic arm as an assistive aid to feed the food to the physically challenged people was developed. In that, to get a target point for the end effector containing food needs to be estimated based on the detected face by the camera available on the robotic manipulator. The user can be situated anywhere between 25 cm to 100 cm away from the robotic arm's end-effector as constrained by the design. To dynamically estimate the depth to a point where a spoonful of food delivery is required, an intuitive technique based on the detected face of the user was evaluated using two 2D cameras: Open MV7, NOIR 8MP, and a 3D camera: Intel 435i. The results were calibrated with the distance obtained by the ultrasonic sensor. The 3D camera was utilized as benchmarking. For the current application, it was found that the 3D camera produced much lesser error compared to 2D cameras. It is also found that NOIR camera performs better than the Open MV7 and could be a good alternative to a 3D camera.

Workspace Analysis of 7-DOF Humanoid Robotic Arm