21 citations · 30 across the 7 of their papers we have counts for
7 papers
Fast and scalable multi-robot deployment planning under connectivity constraints
Yaroslav Marchukov, Luis Montano
In this paper we develop a method to coordinate the deployment of a multi-robot team to reach some locations of interest, so-called primary goals, and to transmit the information f…
Multi-agent coordination for data gathering with periodic requests and deliveries
Yaroslav Marchukov, Luis Montano
In this demo work we develop a method to plan and coordinate a multi-agent team to gather information on demand. The data is periodically requested by a static Operation Center (OC…
Communication-aware planning for robot teams deployment
Yaroslav Marchukov, Luis Montano
In the present work we address the problem of deploying a team of robots in a scenario where some locations of interest must be reached. Thus, a planning for a deployment is requir…
Multi-robot coordination for connectivity recovery after unpredictable environment changes
Yaroslav Marchukov, Luis Montano
In the present paper we develop a distributed method to reconnect a multi-robot team after connectivity failures, caused by unpredictable environment changes, i.e. appearance of ne…
Multi-agent coordination for on-demand data gathering with periodic information upload
Yaroslav Marchukov, Luis Montano
In this paper we develop a method for planning and coordinating a multi-agent team deployment to periodically gather information on demand. A static operation center (OC) periodica…
Improving robot navigation in crowded environments using intrinsic rewards
Diego Martinez-Baselga, Luis Riazuelo, Luis Montano
Autonomous navigation in crowded environments is an open problem with many applications, essential for the coexistence of robots and humans in the smart cities of the future. In re…
Deep reinforcement learning oriented for real world dynamic scenarios
Diego Martinez, Luis Riazuelo, Luis Montano
Autonomous navigation in dynamic environments is a complex but essential task for autonomous robots. Recent deep reinforcement learning approaches show promising results to solve t…