G-flocking: Flocking Model Optimization based on Genetic Framework
This research tackles the challenge of enhancing robotic swarm navigation for applications requiring scalability and adaptability, but it appears incremental as it builds on existing flocking models.
The paper addresses the problem of poor stability and adaptability in robotic swarm autonomous navigation due to complex conflicts from internal pattern maintenance, external environment changes, and target area orientation, and proposes optimizing the flocking model to improve performance.
Flocking model has been widely used to control robotic swarm. However, with the increasing scalability, there exist complex conflicts for robotic swarm in autonomous navigation, brought by internal pattern maintenance, external environment changes, and target area orientation, which results in poor stability and adaptability. Hence, optimizing the flocking model for robotic swarm in autonomous navigation is an important and meaningful research domain.