Graph‐based control framework for motion propagation and pattern preservation in swarm flight simulations

• Published in: Computer animation and virtual worlds Vol. 35; no. 3
• Main Authors: Qi, Feixiang, Wang, Bojian, Wang, Meili
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Subscription Services, Inc 01.05.2024
• Subjects: Algorithms; Animation; biological behavior; Computer graphics; Control algorithms; control framework; Control methods; Flight; flight simulation; Information transfer; Motion control; multi‐agent systems; Robot control; Simulation; topological structure
• ISSN: 1546-4261; 1546-427X
• DOI: 10.1002/cav.2276

Simulation of swarm motion is a crucial research area in computer graphics and animation, and is widely used in a variety of applications such as biological behavior research, robotic swarm control, and the entertainment industry. In this paper, we address the challenges of preserving structural relations between the individuals in swarm flight simulations by proposing an innovative motion control framework that utilizes a graph‐based hierarchy to illustrate patterns within a swarm and allows the swarm to perform flight motions along externally specified paths. In addition, this study designs motion propagation strategies with different focuses for varied application scenarios, analyzes the effects of information transfer latencies on pattern preservation under these strategies, and optimizes the control algorithms at the mathematical level. This study not only establishes a complete set of control methods for group flight simulations, but also has excellent scalability, which can be combined with other techniques in this field to provide new solutions for group behavior simulations. This paper introduces a novel motion control framework for swarm flight simulations that employs a graph‐based hierarchy to maintain structural relationships within the swarm. It designs varied motion propagation strategies for different applications, examines the impact of information transfer delays on pattern preservation, and mathematically optimizes the control algorithms. The study provides a comprehensive set of control methods with excellent scalability that allows for integration integration with other techniques for enhanced group behavior simulations.