4 accepted papers
Distributed multi-robot teams are increasingly used for optimal coverage of domains with unknown density distributions, often modeled with Gaussian Processes (GPs). However, current methods rely on data sharing, raising privacy concerns and computational issues. We propose a Federated Learning (FL)
The present study addresses the challenge of effectively deploying a multi-robot team to optimally cover a domain with unknown density distribution. Specifically, we propose a distribute coverage-based control algorithm that enables a group of autonomous robots to simultaneously learn and estimate a
This paper presents a coverage based control algorithm to coordinate a group of autonomous robots. Most of the solutions presented in the literature rely on an exact Voronoi partitioning, whose computation requires complete knowledge of the environment to be covered. This can be achieved only by rob…
This paper deals with the traffic management of multiple Automated Guided Vehicles (AGVs) in an automatic factory or warehouse. We propose innovative methods, evolved from the studies previously conducted in [1], to coordinate a fleet of AGVs in an industrial environment, and we describe the methodo…