参考文献:[1]Y. Liu, K. M. Passino. Stable social foraging swarms in a noisy environment[J]. IEEE Trans. on Automatic Control, 2004, 49(1): 30-4.CrossRef MathSciNet [2]V. Gazi, K. M. Passino. Stability analysis of swarms[J]. IEEE Trans. on Automatic Control, 2003, 48(4): 692-97.CrossRef MathSciNet [3]C. M. Breder. Equations descriptive of fish schools and other animal aggregations[J]. Ecology, 1954, 35(3): 361-70.CrossRef [4]K. Warburton, J. Lazarus. Tendency-distance models of social cohesion in animal groups[J]. J. of Theoretical Biology, 1991, 150(4): 473-88.CrossRef [5]A. Okubo. Dynamical aspects of animal grouping: swarms, schools, flocks, and herds[J]. Advances in Biophysics, 1986, 22(1): 1-4.CrossRef [6]A. Jadbabaie, J. Lin, A. S. Morse. Coordination of groups of mobile autonomous agents using nearest neighbor rules[J]. IEEE Trans. on Automatic Control, 2003, 48(6): 988-001.CrossRef MathSciNet [7]V. Gazi, K. M. Passino. Stability analysis of social foraging swarms[J]. IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, 2004, 34(1): 539-56.CrossRef [8]S. Chen, H. Fang. Modeling and stability analysis of large-scale swarm[J]. Control and Decision, 2005, 20(5): 490-94 [9]S. Liu, F. Yang. Evaluation of minimum circumscribed circle form error by computational geometry approach[J]. J. of Engineering Graphics, 2000, 21(3): 83-9.MathSciNet [10]S. Chen, H. Fang. Optimal path planning for dynamic environment[J]. J. of Huazhong University of Science & Technology (Nature Science Edition), 2003, 31(12): 29-1.MathSciNet
作者单位:Shiming Chen (1) Huajing Fang (2)
1. School of Electrical & Electronic Engineering, East China JiaoTong University, Nanchang, Jiangxi, 330013, China 2. Department of Control Science & Engineering, Huazhong University of Science & Technology, Wuhan, Hubei, 430074, China
刊物类别:Control; Systems Theory, Control; Optimization; Computational Intelligence; Complexity; Control, Rob
出版者:South China University of Technology and Academy of Mathematics and Systems Science, CAS
ISSN:2198-0942
文摘
In this article we specify an individual-based foraging swarm (i.e., group of agents) model with individuals that move in an n-dimensional multi-obstacle environment. The motion of each individual (i) is determined by three factors: i) attraction to the local object position (\(\bar x_{io + } \)) which is decided by the local information about the individuals-position that individual i can find; ii) repulsion from the other individuals on short distances; and iii) attraction to the global object position (x goal) or repulsion from the obstacles in the environment. The emergent behavior of the swarm motion is the result of a balance between inter-individual interaction and the simultaneous interactions of the swarm members with their environment. We study the stability properties of the collective behavior of the swarm based on Lyapunov stability theory. The simulations show that the swarm can converge to goal regions and diverge from obstacle regions of the environment while maintaining cohesive. Keywords Foraging swarm Modeling Stability analysis Multi-obstacle environment