Afzal Ahmad presents PACNav: A Collective Navigation Approach for UAV Swarms:Deprived of Communication and External Local
On 2023-04-04 11:00:00 at E112, Karlovo náměstí 13, Praha 2
This article proposes Persistence Administered Collective Navigation (PACNav)
as an approach for achieving decentralized collective navigation of Unmanned
Aerial Vehicle (UAV) swarms.
The technique is based on the flocking and collective navigation behavior
observed in natural swarms, such as cattle herds, bird flocks, and even large
groups of humans. As global and concurrent information of all swarm members is
not available in natural swarms, these systems use local observations to
achieve the desired behavior. Similarly, PACNav relies only on local
observations of relative positions of UAVs, making it suitable for large swarms
deprived of communication capabilities and external localization systems. We
introduce the novel concepts of path persistence and path similarity that allow
each swarm member to analyze the motion of other members in order to determine
its own future motion. PACNav is based on two main principles: (1) UAVs with
little variation in motion direction have high path persistence, and are
considered by other UAVs to be reliable leaders; (2) groups of UAVs that move
in a similar direction have high path similarity, and such groups are assumed
to contain a reliable leader. The proposed approach also embeds a reactive
collision avoidance mechanism to avoid collisions with swarm members and
environmental obstacles. This collision avoidance ensures safety while reducing
deviations from the assigned path. Along with several simulated experiments, we
present a real-world experiment in a natural forest, showcasing the validity
and effectiveness of the proposed collective navigation approach in challenging
environments.
The source code is released as open-source, making it possible to replicate the
obtained results and facilitate the continuation of research by the community.
as an approach for achieving decentralized collective navigation of Unmanned
Aerial Vehicle (UAV) swarms.
The technique is based on the flocking and collective navigation behavior
observed in natural swarms, such as cattle herds, bird flocks, and even large
groups of humans. As global and concurrent information of all swarm members is
not available in natural swarms, these systems use local observations to
achieve the desired behavior. Similarly, PACNav relies only on local
observations of relative positions of UAVs, making it suitable for large swarms
deprived of communication capabilities and external localization systems. We
introduce the novel concepts of path persistence and path similarity that allow
each swarm member to analyze the motion of other members in order to determine
its own future motion. PACNav is based on two main principles: (1) UAVs with
little variation in motion direction have high path persistence, and are
considered by other UAVs to be reliable leaders; (2) groups of UAVs that move
in a similar direction have high path similarity, and such groups are assumed
to contain a reliable leader. The proposed approach also embeds a reactive
collision avoidance mechanism to avoid collisions with swarm members and
environmental obstacles. This collision avoidance ensures safety while reducing
deviations from the assigned path. Along with several simulated experiments, we
present a real-world experiment in a natural forest, showcasing the validity
and effectiveness of the proposed collective navigation approach in challenging
environments.
The source code is released as open-source, making it possible to replicate the
obtained results and facilitate the continuation of research by the community.