Safety is often the most important requirement in robotics applications. Nonetheless, control techniques that can provide safety guarantees are still extremely rare for nonlinear systems, such as robot manipulators. A well-known tool to ensure safety is the viability kernel, which is the largest set of states from which safety can be ensured. Unfortunately, computing such a set for a nonlinear system is extremely challenging in general. Several numerical algorithms for approximating it have been proposed in the literature, but they suffer from the curse of dimensionality. This letter presents a new approach for numerically approximating the viability kernel of robot manipulators. Our approach solves optimal control problems to compute states that are guaranteed to be on the boundary of the set. This allows us to learn directly the set boundary, therefore learning in a smaller dimensional space. Compared to the state of the art on systems up to dimension 6, our algorithm resulted to be more than 2 times as accurate for the same computation time, or 6 times as fast to reach the same accuracy.

VBOC: Learning the Viability Boundary of a Robot Manipulator Using Optimal Control / La Rocca, Asia; Saveriano, Matteo; Del Prete, Andrea. - In: IEEE ROBOTICS AND AUTOMATION LETTERS. - ISSN 2377-3766. - 8:11(2023), pp. 6971-6978. [10.1109/lra.2023.3313921]

VBOC: Learning the Viability Boundary of a Robot Manipulator Using Optimal Control

La Rocca, Asia
Primo
;
Saveriano, Matteo
Secondo
;
Del Prete, Andrea
Ultimo
2023-01-01

Abstract

Safety is often the most important requirement in robotics applications. Nonetheless, control techniques that can provide safety guarantees are still extremely rare for nonlinear systems, such as robot manipulators. A well-known tool to ensure safety is the viability kernel, which is the largest set of states from which safety can be ensured. Unfortunately, computing such a set for a nonlinear system is extremely challenging in general. Several numerical algorithms for approximating it have been proposed in the literature, but they suffer from the curse of dimensionality. This letter presents a new approach for numerically approximating the viability kernel of robot manipulators. Our approach solves optimal control problems to compute states that are guaranteed to be on the boundary of the set. This allows us to learn directly the set boundary, therefore learning in a smaller dimensional space. Compared to the state of the art on systems up to dimension 6, our algorithm resulted to be more than 2 times as accurate for the same computation time, or 6 times as fast to reach the same accuracy.
2023
11
La Rocca, Asia; Saveriano, Matteo; Del Prete, Andrea
VBOC: Learning the Viability Boundary of a Robot Manipulator Using Optimal Control / La Rocca, Asia; Saveriano, Matteo; Del Prete, Andrea. - In: IEEE ROBOTICS AND AUTOMATION LETTERS. - ISSN 2377-3766. - 8:11(2023), pp. 6971-6978. [10.1109/lra.2023.3313921]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/402855
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