activity
20162021
most citedOnboard Ranging-based Relative Localization and Stability for Lightweight Aerial Swarms

11 citations · 20 across the 6 of their papers we have counts for

collaborators

6 papers

cs.RO2021★ 1 cited

A model-based framework for learning transparent swarm behaviors

Mario Coppola, Jian Guo, Eberhard Gill +1

This paper proposes a model-based framework to automatically and efficiently design understandable and verifiable behaviors for swarms of robots. The framework is based on the auto…

cs.RO2021

MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world models

Daniël Willemsen, Mario Coppola, Guido C. H. E. de Croon

Multi-robot systems can benefit from reinforcement learning (RL) algorithms that learn behaviours in a small number of trials, a property known as sample efficiency. This research…

cs.RO2020★ 11 cited

Onboard Ranging-based Relative Localization and Stability for Lightweight Aerial Swarms

Shushuai Li, Feng Shan, Jiangpeng Liu +3

Lightweight aerial swarms have potential applications in scenarios where larger drones fail to operate efficiently. The primary foundation for lightweight aerial swarms is efficien…

cs.RO2018★ 6 cited

On-board Range-based Relative Localization for Micro Aerial Vehicles in indoor Leader-Follower Flight

Steven van der Helm, Kimberly N. McGuire, Mario Coppola +1

We present a range-based solution for indoor relative localization by Micro Air Vehicles (MAVs), achieving sufficient accuracy for leader-follower flight. Moving forward from previ…

cs.RO2018★ 1 cited

Provable Emergent Pattern Formation by a Swarm of Anonymous, Homogeneous, Non-Communicating, Reactive Robots with Limited Relative Sensing and no Global Knowledge or Positioning

Mario Coppola, Jian Guo, Eberhard K. A. Gill +1

In this work, we explore emergent behaviors by swarms of anonymous, homogeneous, non-communicating, reactive robots that do not know their global position and have limited relative…

cs.RO2016★ 1 cited

On-board Communication-based Relative Localization for Collision Avoidance in Micro Air Vehicle teams

Mario Coppola, Kimberly McGuire, Kirk Y. W. Scheper +1

Micro Air Vehicles (MAVs) will unlock their true potential once they can operate in groups. To this end, it is essential for them to estimate on-board the relative location of thei…