Jorge Cortés
Professor
Cymer Corporation Endowed Chair
Data-driven distributionally robust coverage control by mobile robots
D. Boskos, J. Cortés, S. Martínez
Proceedings of the IEEE Conference on
Decision and Control, Singapore, 2023,
pp. 2030-2035
Abstract
This paper provides a data-driven solution to the
problem of coverage control by which a team of
robots aims to optimally deploy in a spatial region
where certain event of interest may occur. This
event is random and described by a probability
density function, which is unknown and can only be
learned by collecting data. In this work, we hedge
against this uncertainty by designing a
distributionally robust algorithm that optimizes the
locations of the robots against the worst-case
probability density from an ambiguity set. This
ambiguity set is constructed from data initially
collected by the agents, and contains the true
density function with prescribed
confidence. However, the objective function that the
robots seek to minimize is non-smooth. To address
this issue, we employ the so-called gradient
sampling algorithm, which approximates the Clarke
generalized gradient by sampling the derivative of
the objective function at nearby locations and
stabilizes the choice of descent directions around
points where the function may fail to be
differentiable. This enables us to prove that the
algorithm converges to a stationary point from any
initial location of the robots, in analogy to the
well-known Lloyd algorithm for differentiable costs
when the spatial density is known.
pdf
Mechanical and Aerospace Engineering,
University of California, San Diego
9500 Gilman Dr,
La Jolla, California, 92093-0411
Ph: 1-858-822-7930
Fax: 1-858-822-3107
cortes at ucsd.edu
Skype id:
jorgilliyo