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Executing different grasp types with pose uncertainty: Blue (Assumed), Green (Corrected). Objects are repeatedly grasped and lifted 10 × by a mobile manipulator. Base poses to execute a collision-free grasp are autonomously discovered by the mobile manipulator (Color figure online)
Source publication
We present an algorithm that discovers grasp pose solutions for multiple grasp types for a multi-fingered mechanical gripper using partially-sensed point clouds of unknown objects. The algorithm introduces two key ideas: (1) a histogram of finger contact normals is used to represent a grasp “shape” to guide a gripper orientation search in a histogr...
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Visual object grasping can be complex when dealing with different shapes, points of view, and environments since the robotic manipulator must estimate the most feasible place to grasp. This work proposes a new selective grasping system using only point clouds of objects. For the selection of the object of interest, a deep learning network for objec...
Citations
... Vision sensors provide more useful and insightful information than other types of sensors (proximity, position, etc.). The applications of robots are also expanded, some common applications of the robotic vision system are dynamic grasping, automatic sorting, visual servoing, vision guide robot, and visionbased inspection, ... [15][16][17][18][19][20] Because of their wide application in industrial fields, companies have developed robotic systems with full functions, serving many different requirements. Vision systems and control programs are also developed almost completely and packaged into software for easy use by users. ...
In this paper, we develop a low-cost delta robot arm for grasping objects of unspecified size thanks to a vision system. Stepper motors are used instead of ac servo motors to build a low-cost delta robot arm. Furthermore, we use available materials and machining methods such as laser cutting and 3d printing instead of CNC milling and turning to reduce fabrication costs. The controller is based on a low-cost embedded controller-Arduino Uno for controlling the robot's motion. The vision system is constructed to determine the 3D coordinate of objects in the workspace as well as the sizes of objects. The gripper is opened with a distance of two fingers equal to the size of the objects, and the robot is controlled to the objects' coordinates to grasp them. An application to pick up objects on a conveyor belt is developed to validate the design. The experimental results show that the robot system works correctly, the robot arm moves smoothly, and the information determined by the vision system has a small error, ensuring that the robot can accurately pick up products.
Purpose The purpose of this paper is to design a multifingered dexterous hand grasping planning method that can efficiently perform grasping tasks on multiple dexterous hand platforms.
Design/methodology/approach The grasping process is divided into two stages: offline and online. In the offline stage, the grasping solution form is improved based on the forward kinematic model of the dexterous hand. A comprehensive evaluation method of grasping quality is designed to obtain the optimal grasping solution offline data set. In the online stage, a safe and efficient selection strategy of the optimal grasping solution is proposed, which can quickly obtain the optimal grasping solution without collision.
Findings The experiments verified that the method can be applied to different multifingered dexterous hands, and the average grasping success rate for objects with different structures is 91.7%, indicating a good grasping effect.
Originality/value Using a forward kinematic model to generate initial grasping points can improve the generality of grasping planning methods and the quality of initial grasping solutions. The offline data set of optimized grasping solutions can be generated faster by the comprehensive evaluation method of grasping quality. Through the simple and fast obstacle avoidance strategy, the safe optimal grasping solution can be quickly obtained when performing a grasping task. The proposed method can be applied to automatic assembly scenarios where the end effector is a multifingered dexterous hand, which provides a technical solution for the promotion of multifingered dexterous hands in industrial scenarios.
This paper develops a multi-robotic arm system and a stereo vision system to sort objects in the right position according to size and shape attributes. The robotic arm system consists of one master and three slave robots associated with three conveyor belts. Each robotic arm is controlled by a robot controller based on a microcontroller. A master controller is used for the vision system and communicating with slave robotic arms using the Modbus RTU protocol through an RS485 serial interface. The stereo vision system is built to determine the 3D coordinates of the object. Instead of rebuilding the entire disparity map, which is computationally expensive, the centroids of the objects in the two images are calculated to determine the depth value. After that, we can calculate the 3D coordinates of the object by using the formula of the pinhole camera model. Objects are picked up and placed on a conveyor branch according to their shape. The conveyor transports the object to the location of the slave robot. Based on the size attribute that the slave robot receives from the master, the object is picked and placed in the right position. Experiment results reveal the effectiveness of the system. The system can be used in industrial processes to reduce the required time and improve the performance of the production line.
