From Design to Deployment: Decentralized Coordination of Heterogeneous Robotic Teams.
control framework
decentralized behaviors
heterogeneous robotic teams
over-the-air update
swarm intelligence
swarm programming
swarm systems
Journal
Frontiers in robotics and AI
ISSN: 2296-9144
Titre abrégé: Front Robot AI
Pays: Switzerland
ID NLM: 101749350
Informations de publication
Date de publication:
2020
2020
Historique:
received:
16
07
2019
accepted:
20
03
2020
entrez:
27
1
2021
pubmed:
28
1
2021
medline:
28
1
2021
Statut:
epublish
Résumé
Many applications benefit from the use of multiple robots, but their scalability and applicability are fundamentally limited when relying on a central control station. Getting beyond the centralized approach can increase the complexity of the embedded software, the sensitivity to the network topology, and render the deployment on physical devices tedious and error-prone. This work introduces a software-based solution to cope with these challenges on commercial hardware. We bring together our previous work on Buzz, the swarm-oriented programming language, and the many contributions of the Robotic Operating System (ROS) community into a reliable workflow, from rapid prototyping of decentralized behaviors up to robust field deployment. The Buzz programming language is a hardware independent, domain-specific (swarm-oriented), and composable language. From simulation to the field, a Buzz script can stay unmodified and almost seamlessly applicable to all units of a heterogeneous robotic team. We present the software structure of our solution, and the swarm-oriented paradigms it encompasses. While the design of a new behavior can be achieved on a lightweight simulator, we show how our security mechanisms enhance field deployment robustness. In addition, developers can update their scripts in the field using a safe software release mechanism. Integrating Buzz in ROS, adding safety mechanisms and granting field updates are core contributions essential to swarm robotics deployment: from simulation to the field. We show the applicability of our work with the implementation of two practical decentralized scenarios: a robust generic task allocation strategy and an optimized area coverage algorithm. Both behaviors are explained and tested with simulations, then experimented with heterogeneous ground-and-air robotic teams.
Identifiants
pubmed: 33501219
doi: 10.3389/frobt.2020.00051
pmc: PMC7806003
doi:
Types de publication
Journal Article
Langues
eng
Pagination
51Informations de copyright
Copyright © 2020 St-Onge, Varadharajan, Švogor and Beltrame.