What is robotic grasping?5 answersRobotic grasping refers to the task of making a robotic system pick up objects by applying forces and torques on their surfaces. It is a fundamental and challenging task in robotics research, as it requires the ability to autonomously grasp objects in cluttered environments. Robotic grippers play a crucial role in this task, as they are designed to execute specific manual tasks or meet specific objectives in handling operations. The development of robotic grasping methods has focused on generating grasp poses for known objects, but there is a need for more robust methods that can handle unknown objects and challenging scenes. Recent advancements in data-driven approaches, such as deep reinforcement learning and evolutionary algorithms, have shown promise in addressing the exploration problem and generating diverse repertoire of grasping trajectories.
What are the gripper technologies in robotics?5 answersRobotic gripper technologies vary in design and function. Grippers can be small, accurate, and highly controllable, such as the surgical tool effectors of the Da Vinci robot, or larger and more controllable like the Shadow Dexterous Hand. Some grippers leverage natural motions inspired by observed biomechanical systems, such as the iRobot-Harvard-Yale (iHY) Hand or iRobot-Harvard-Yale (IIT)-Pisa SoftHand. Soft grippers have also gained interest due to their flexibility and ability to manipulate objects without damage. These grippers can be pneumatic or electromechanical, with examples including the gripper with chambered fingers, jamming gripper, tendon driver gripper, and gripper with passive structure. The selection of a gripper is crucial for a robot's productivity and performance. As robotic systems become more autonomous and widely used, the design, form, and function of robotic grippers are increasingly important to consider.
What work has been done with a underwater robot gripper arm?5 answersUnderwater manipulators are critical in the development of marine resources. Traditional rigid manipulators cannot be applied for underwater operations requiring non-destructive grasping, such as biological sampling and artifact salvage. Soft robotics underwater manipulators provide a dexterous and lightweight arm and gripper, but still require high power drive elements and high-frequency valve control components. Ling Bai et al. propose a hydraulic drive control system based on a rigid-flexible coupling accumulator for a submerged soft gripper, enabling depth self-adaptation and fast and stable drive for different volumes of the gripper. Martin Skaldebø et al. introduce a distance estimator using monocular vision for autonomous underwater grasping, validated through autonomous grasping experiments with the SeaArm-2 manipulator. A controllably compliant soft robotic underwater gripper is presented by an article, using neutrally buoyant particle jamming pads and antagonistic low-friction rolling diaphragm hydraulic cylinders for delicate grasping of diverse objects. Mario Baggetta et al. report on the virtual and physical prototyping of a three-fingered gripper designed for underwater manipulation and sampling specimens, with a modular design allowing for the addition of further fingers.
What is main purpose of robot arm?5 answersThe main purpose of a robotic arm is to perform tasks that a human arm can do, either autonomously or manually, with high precision. It can be used in various settings, including industrial and domestic environments. The robotic arm is designed to mimic the movements of a human arm by using accelerometers as sensors to collect data on natural arm motions. It can be used for remotely detecting and intervening suspicious objects, mines, improvised explosive devices (IED), traps, and similar elements that may pose danger. In terms of industrial automation, the robotic arm can be cost-efficient and programmable, with the ability to perform tasks such as welding, gripping, spinning, and detective investigations. The robotic arm also provides multiple degrees of freedom for positioning the end-effector platform, allowing for precise end-effector motion. Additionally, the robotic arm can be used in hardware component processing, including preheating, stamping, and punching, to improve production efficiency and reduce material waste.
Why SCARA manipulator precise?5 answersSCARA manipulators are precise due to their high speed, exceptional precision, and the use of inverse kinematics. The trajectory planning algorithm used in SCARA manipulators effectively improves their control accuracy and work efficiency. The mapping relation between the image coordinate system and the manipulator local coordinate system is accurately established, solving the problem of low accuracy in prior art. The mechanical design process of SCARA manipulators focuses on reducing vibration and backlash, ensuring high precision. The use of readily available components and careful selection of electrical and mechanical components contribute to the precision of SCARA manipulators. The low-cost local manufactured SCARA manipulator designed in one study demonstrates precision and control in pick and place tasks. Overall, the combination of design considerations, trajectory planning, and accurate mapping relations contribute to the precision of SCARA manipulators.
How to model kinematic equation of manipulator with gaussian mixture regression?5 answersGaussian Mixture Regression (GMR) and K-Nearest-Neighbors Regression (KNNR) are two approaches from the machine learning domain that can be used to model the kinematic equation of a manipulator. These methods have been shown to outperform traditional analytical inverse kinematic models in terms of performance and accuracy. GMR uses a mixture of local sparse Gaussian processes to learn the inverse kinematic model of the manipulator. By segmenting the data domain into smaller regions and using independent local Gaussian process models for each region, GMR achieves linear computational cost with comparable accuracy. KNNR, on the other hand, uses a k-nearest-neighbors approach to learn the inverse kinematic model. Both GMR and KNNR have been validated through experiments with real-world robot data and have shown superior performance compared to traditional analytical models.