TY - GEN
T1 - Self-Organized UAV Flocking Based on Proximal Control
AU - Amorim, Thulio
AU - Nascimento, Tiago
AU - Petracek, Pavel
AU - De Masi, Giulia
AU - Ferrante, Eliseo
AU - Saska, Martin
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/6/15
Y1 - 2021/6/15
N2 - In this work, we address the problem of achieving cohesive and aligned flocking (collective motion) with a swarm of unmanned aerial vehicles (UAVs). We propose a method that requires only onboard sensing of the relative range and bearing of neighboring UAVs, and therefore requires only proximal control for achieving formation. Our method efficiently achieves flocking in the absence of any explicit orientation information exchange (alignment control), and achieves flocking in a random direction without externally provided directional information. To implement proximal control, the Lennard-Jones potential function is used to maintain cohesiveness and avoid collisions. Our approach may be used independently from any external positioning system such as GNSS or Motion Capture, and can therefore be used in GNSS-denied environments. The performance of the approach was tested in real-world conditions by experiments with UAVs that rely only on a relative visual localization system called UVDAR, proposed by our group. To evaluate the degree of alignment and cohesiveness, we used the order metric and the steady-state value.
AB - In this work, we address the problem of achieving cohesive and aligned flocking (collective motion) with a swarm of unmanned aerial vehicles (UAVs). We propose a method that requires only onboard sensing of the relative range and bearing of neighboring UAVs, and therefore requires only proximal control for achieving formation. Our method efficiently achieves flocking in the absence of any explicit orientation information exchange (alignment control), and achieves flocking in a random direction without externally provided directional information. To implement proximal control, the Lennard-Jones potential function is used to maintain cohesiveness and avoid collisions. Our approach may be used independently from any external positioning system such as GNSS or Motion Capture, and can therefore be used in GNSS-denied environments. The performance of the approach was tested in real-world conditions by experiments with UAVs that rely only on a relative visual localization system called UVDAR, proposed by our group. To evaluate the degree of alignment and cohesiveness, we used the order metric and the steady-state value.
KW - flocking
KW - Micro aerial vehicles
KW - self-organization
KW - swarm robotics
KW - unmanned aerial vehicles
UR - http://www.scopus.com/inward/record.url?scp=85111446993&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85111446993&partnerID=8YFLogxK
U2 - 10.1109/ICUAS51884.2021.9476847
DO - 10.1109/ICUAS51884.2021.9476847
M3 - Conference contribution
AN - SCOPUS:85111446993
T3 - 2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021
SP - 1374
EP - 1382
BT - 2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021
Y2 - 15 June 2021 through 18 June 2021
ER -