Abstract
Desktop manipulators have found extensive applications in the factory, home, and classroom. However, existing customized desktop manipulators have hardly equipped sensors to percept the environment. In this paper, we develop a low-cost solution to this issue, by equipping an ordinary camera and tactile sensors to an off-the-shelf desktop manipulator. This integrated system uses the image to coarsely localize the objects on the desk and drives the end-effector to approach the object. During the grasping procedure, a flex sensor is used for precisely localizing the objects and a set of force sensors fixed on the gripper can be used to classify the objects according to their materials or deformability. All of the hardware units can be easily purchased and the total cost is under $500. We also made extensive experimental validation on the fruits classification task to show the advantages of the developed manipulator.
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Fan, L., Xia, Z., Yang, Y. (2019). Visual-Tactile Fusion for a Low-Cost Desktop Robotic Manipulator Grasping. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2018. Communications in Computer and Information Science, vol 1005. Springer, Singapore. https://doi.org/10.1007/978-981-13-7983-3_50
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DOI: https://doi.org/10.1007/978-981-13-7983-3_50
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