Abstract
The paper proposes a novel data-driven approximation kinematic (DAK) model to estimate the shape and opening level of a PneuNets soft gripper in relation to the applied pressure signal. The model offers suitable capabilities for implementing in real-time applications involving soft grasping planning and size recognition of fragile objects with different sizes and shapes. The proposed DAK model estimates the free bending behavior of a PneuNets actuator (soft gripper finger) based on a set of approximation functions derived from experimental data and an equivalent serial mechanism that mimics the shape of the actuator. The model was tested for a commercial PneuNets actuator with decreasing chamber height, produced by SoftGripping Co. (Hamburg, Germany). The model validation is accomplished through a set of experiments, where the shape and elementary displacements were measured using a digital image processing technique. The experimental data and the estimated data from the DAK model were compared and analyzed, respectively. The proposed approach has applicability in sensorless/self-sensing bending control algorithms of PneuNets actuators and in soft grasping applications where the robotic system must estimate the opening level of the gripper in order to be able to accomplish its task.
Highlights
Grasping is an important function when it comes to human beings and robots
We will address aspects related to soft grasping tasks using soft robotic grippers based on PneuNets bending actuators
The paper proposed a new data-driven kinematic (DAK) model, which approximates the shape of PneuNets bending actuators in relation to input pressure and the opening level of PneuNets-based soft grippers
Summary
Grasping is an important function when it comes to human beings and robots In robotics, this function is performed by a grasping device called a gripper. We will address aspects related to soft grasping tasks (fragile object manipulation) using soft robotic grippers based on PneuNets bending actuators. Simple parallel finger grippers or more complex anthropomorphic robotic hands with many degrees of freedom (DoF) are representative examples of this category [1,2]. Their development and requirements (DoF, number of fingers, actuation method, mechanisms used, gripping range and stroke, etc.)
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