TY - JOUR
T1 - Multiagent Persistent Monitoring via Time-Inverted Kuramoto Dynamics
AU - Boldrer, Manuel
AU - Pasqualetti, Fabio
AU - Palopoli, Luigi
AU - Fontanelli, Daniele
PY - 2022
Y1 - 2022
N2 - We present a novel distributed multi-robot coordination strategy to persistently monitor a closed path-like environment. Our monitoring strategy relies on a class of time-inverted Kuramoto dynamics, whose multiple equilibria coincide with different monitoring configurations and allow us to tune the covering time of specific areas based on their priority. We provide a detailed analysis of the equilibria of the considered class of time-inverted Kuramoto dynamics and demonstrate the effectiveness of the proposed monitoring strategy via numerical examples.
AB - We present a novel distributed multi-robot coordination strategy to persistently monitor a closed path-like environment. Our monitoring strategy relies on a class of time-inverted Kuramoto dynamics, whose multiple equilibria coincide with different monitoring configurations and allow us to tune the covering time of specific areas based on their priority. We provide a detailed analysis of the equilibria of the considered class of time-inverted Kuramoto dynamics and demonstrate the effectiveness of the proposed monitoring strategy via numerical examples.
KW - multiagent systems
KW - nonlinear networked systems
KW - persistent monitoring
KW - Time-inverted Kuramoto dynamics
UR - http://www.scopus.com/inward/record.url?scp=85131697122&partnerID=8YFLogxK
U2 - 10.1109/LCSYS.2022.3178294
DO - 10.1109/LCSYS.2022.3178294
M3 - Article
AN - SCOPUS:85131697122
VL - 6
SP - 2798
EP - 2803
JO - IEEE Control Systems Letters
JF - IEEE Control Systems Letters
SN - 2475-1456
ER -