TY - GEN
T1 - Constant Bearing Flocking
AU - de Souza Junior, Cristino
AU - Manoni, Tiziano
AU - Ferrante, Eliseo
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - In this paper, we present “bearing-and-range-only” approach for a self-organized flocking, which allows the flocking alignment without the assumption of measuring agent’s velocity or orientation. This last assumption challenges the implementation with real robot, since common off-the-shelf sensors do not provide such information unless inter-agent communication is used. To overcome the above issue, we propose a flocking behavior based on the “constant bearing rule”, which is known geometrical concept commonly used for missile guidance. The proposed behavior is described by a steering law and a velocity law. In the first one, the agent tries to keep constant bearing towards the target (if informed), or towards the center of mass of the perceived neighbors (if not informed). The second law allows the agent to regulate its linear velocity to keep a minimal safety distance towards the closest agent. Together, the two laws combined realize alignment. We perform simulation experiments to evaluate the new method and we compare the results with a “range-and-bearing” state of the art method.
AB - In this paper, we present “bearing-and-range-only” approach for a self-organized flocking, which allows the flocking alignment without the assumption of measuring agent’s velocity or orientation. This last assumption challenges the implementation with real robot, since common off-the-shelf sensors do not provide such information unless inter-agent communication is used. To overcome the above issue, we propose a flocking behavior based on the “constant bearing rule”, which is known geometrical concept commonly used for missile guidance. The proposed behavior is described by a steering law and a velocity law. In the first one, the agent tries to keep constant bearing towards the target (if informed), or towards the center of mass of the perceived neighbors (if not informed). The second law allows the agent to regulate its linear velocity to keep a minimal safety distance towards the closest agent. Together, the two laws combined realize alignment. We perform simulation experiments to evaluate the new method and we compare the results with a “range-and-bearing” state of the art method.
UR - http://www.scopus.com/inward/record.url?scp=85142731050&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85142731050&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-20176-9_26
DO - 10.1007/978-3-031-20176-9_26
M3 - Conference contribution
AN - SCOPUS:85142731050
SN - 9783031201752
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 300
EP - 307
BT - Swarm Intelligence - 13th International Conference, ANTS 2022, Proceedings
A2 - Dorigo, Marco
A2 - Strobel, Volker
A2 - Camacho-Villalón, Christian
A2 - Hamann, Heiko
A2 - Hamann, Heiko
A2 - López-Ibáñez, Manuel
A2 - García-Nieto, José
A2 - Engelbrecht, Andries
A2 - Pinciroli, Carlo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th International Conference on Swarm Intelligence, ANTS 2022
Y2 - 2 November 2022 through 4 November 2022
ER -