A survey on collaborative hunting with robotic swarm: Key technologies and application scenarios

Distributed self-tuning with a 0.05 tuning step supports multi-agent coordination, minimizing latency in fleets of up to 50 robots.

“Distributed self-tuning with a 0.05 tuning step further supports multi-agent coordination, minimizing latency in fleets of up to 50 robots [4].”

Distributed control manages fleets of 5-50 robots with efficiency and achieves rapid coordination in UAV swarms.

“Distributed control manages fleets of 5-50 robots with efficiency [4], achieving rapid coordination in UAV swarms [27]”

Centralized architectures experience significant efficiency drops in fleets exceeding 100 robots.

“The literature reports performance disruptions in centralized architectures, with efficiency dropping significantly in fleets exceeding 100 robots [4]”

Scalability is a critical challenge for multi-robot systems as they grow beyond small fleets, with efficiency often declining due to coordination overhead and fault propagation.

“Scalability poses a critical challenge as multi-robot systems grow beyond small fleets, with efficiency often declining due to coordination overhead and fault propagation [4].”

Centralized multi-robot architectures show performance disruptions, with efficiency dropping significantly in fleets exceeding 100 robots.

“The literature reports performance disruptions in centralized architectures, with efficiency dropping significantly in fleets exceeding 100 robots [4]”

Scalability beyond small fleets remains constrained by performance declines.

“though scalability beyond small fleets remains constrained by performance declines [4].”

Scalability is limited by coordination overhead in large fleets, with distributed optimization reducing latency in swarms, yet efficiency drops remain a concern.

“Scalability is limited by coordination overhead in large fleets, with distributed optimization reducing latency in swarms [51], yet efficiency drops remain a concern [4].”