5 accepted papers
Deep clustering (DC) leverages the representation power of deep architectures to learn embedding spaces that are optimal for cluster analysis. This approach filters out low-level information irrelevant for clustering and has proven remarkably successful for high dimensional data spaces. Some DC meth…
This paper presents a novel methodology that allows a swarm of robots to perform a cooperative transportation task. Our approach consists of modeling the swarm as a Gibbs Random Field (GRF), taking advantage of this framework’s locality properties. By setting appropriate potential functions, robots…
This paper presents a novel approach that allows a swarm of heterogeneous robots to produce simultaneously segregative and flocking behaviors using only local sensing. These behaviors have been widely studied in swarm robotics and their combination allows the execution of several complex tasks. Our…
In this paper, we propose a motion planning method to escort a set of agents from one place to a goal in an environment with obstacles. The agents are distributed in a finite area, with a time-varying perimeter, in which we put multiple robots to patrol around it with a desired velocity. Our proposa
Predicting the behavior of dangerous environmental boundaries, like spreading fire or oil spill, provides relevant information to mitigate the problem or even to support evacuation actions in order to save human or animal lives. In this letter, we present a model that uses a single robot moving arou