Bio-inspired grasp control in a robotic hand with massive sensorial input |
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Authors: | Luca Ascari Ulisse Bertocchi Paolo Corradi Cecilia Laschi Paolo Dario |
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Institution: | (1) Centre of Excellence for Information and Communication Engineering (CEIIC), Scuola Superiore Sant’Anna, Pisa, Italy;(2) HENESIS s.r.l., Parma, Italy;(3) Center for Research In Micro- and Nano-engineering (CRIM), Scuola Superiore Sant’Anna, Pisa, Italy;(4) Advanced Robotics Technology and Systems Laboratory (ARTS Lab), Scuola Superiore Sant’Anna, Pisa, Italy |
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Abstract: | The capability of grasping and lifting an object in a suitable, stable and controlled way is an outstanding feature for a
robot, and thus far, one of the major problems to be solved in robotics. No robotic tools able to perform an advanced control
of the grasp as, for instance, the human hand does, have been demonstrated to date. Due to its capital importance in science
and in many applications, namely from biomedics to manufacturing, the issue has been matter of deep scientific investigations
in both the field of neurophysiology and robotics. While the former is contributing with a profound understanding of the dynamics
of real-time control of the slippage and grasp force in the human hand, the latter tries more and more to reproduce, or take
inspiration by, the nature’s approach, by means of hardware and software technology. On this regard, one of the major constraints
robotics has to overcome is the real-time processing of a large amounts of data generated by the tactile sensors while grasping,
which poses serious problems to the available computational power. In this paper a bio-inspired approach to tactile data processing
has been followed in order to design and test a hardware–software robotic architecture that works on the parallel processing
of a large amount of tactile sensing signals. The working principle of the architecture bases on the cellular nonlinear/neural
network (CNN) paradigm, while using both hand shape and spatial–temporal features obtained from an array of microfabricated
force sensors, in order to control the sensory-motor coordination of the robotic system. Prototypical grasping tasks were
selected to measure the system performances applied to a computer-interfaced robotic hand. Successful grasps of several objects,
completely unknown to the robot, e.g. soft and deformable objects like plastic bottles, soft balls, and Japanese tofu, have
been demonstrated. |
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