A Fast and Reliable Pick-and-Place Application with a Spherical Soft Robotic Arm
Jasan Zughaibi, Matthias Hofer, Raffaello D'Andrea

TL;DR
This paper introduces a learning control approach for a spherical soft robotic arm, enabling fast, reliable pick-and-place operations with compliant behavior and precise trajectory tracking, demonstrated through experimental validation.
Contribution
It presents a novel control architecture combining pressure-based control, gain-scheduling, and iterative learning for a soft robotic arm in pick-and-place tasks.
Findings
Achieved accurate trajectory tracking with set point transitions within 0.3 to 0.6 seconds.
Demonstrated reliable pick-and-place performance with a soft robotic arm.
Validated the control approach through experimental experiments.
Abstract
This paper presents the application of a learning control approach for the realization of a fast and reliable pick-and-place application with a spherical soft robotic arm. The arm is characterized by a lightweight design and exhibits compliant behavior due to the soft materials deployed. A soft, continuum joint is employed, which allows for simultaneous control of one translational and two rotational degrees of freedom in a single joint. This allows us to axially approach and pick an object with the attached suction cup during the pick-and-place application. A control allocation based on pressure differences and the antagonistic actuator configuration is introduced, allowing decoupling of the system dynamics and simplifying the modeling and control. A linear parameter-varying model is identified, which is parametrized by the attached load mass and a parameter related to the joint…
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