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Tesi etd-06012023-200257


Tipo di tesi
Tesi di laurea magistrale
Autore
SOMMA, DANIELE
URN
etd-06012023-200257
Titolo
Learning-based active compliance control of a tendon-driven soft arm
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
BIONICS ENGINEERING
Relatori
relatore Prof. Falotico, Egidio
Parole chiave
  • active compliance
  • control
  • ESN
  • LSTM
  • machine learning
  • soft robotics
  • tendon-driven
Data inizio appello
20/06/2023
Consultabilità
Non consultabile
Data di rilascio
20/06/2063
Riassunto
Soft robots are extremely challenging in modeling and control due to their high dimensionality. The development of learning based controllers offers a wide range of possibilities. In this work a learning based controller for the active compliance of a tendon-driven soft arm has been developed using pyElastica as simulation environment. Recurrent neural networks, have been employed for the generation of the inverse kinematics (IK) of the soft manipulator and a comparison between Long-Short-Term-Memory (LSTM) and Echo-State-Network (ESN) has been performed. The control system, given the application of an external force at the tip of the robot, generates a trajectory to be followed to achieve active compliance. Comparing the movement of the robot induced by the same force with or without the controller, the results demonstrated a reduction of the force sensed at the tip of the robot, thus the soft manipulator has been able to follow actively the trajectory imposed by the external force.
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