2009. december 3., csütörtök
[link] Birds Of A Feather Flock Together
Introduction to flocking: http://www.scienceagogo.com/news/19980828021426data_trunc_sys.shtml
2009. november 26., csütörtök
Flocking with informed agents. (Cucker, Felipe; Huepe, Cristian)
MathS In Action 1, No. 1, 1-25, electronic only (2008).
Abstract: Two similar Laplacian-based models for swarms with informed agents are proposed and analyzed analytically and numerically. In these models, each individual adjusts its velocity to match that of its neighbors and some individuals are given a preferred heading direction towards which they accelerate if there is no local velocity consensus. The convergence to a collective group swarming state with constant velocity is analytically proven for a range of parameters and initial conditions. Using numerical computations, the ability of a small group of informed individuals to accurately guide a swarm of uninformed agents is investigated. The results obtained in one of our two models are analogous to those found for more realistic and complex algorithms for describing biological swarms, namely, that the fraction of informed individuals required to guide the whole group is small, and that it becomes smaller for swarms with more individuals. This observation in our simple system provides insight into the possibly robust dynamics that contribute to biologically effective collective leadership and decision-making processes. In contrast with the more sophisticated models mentioned above, we can describe conditions under which convergence to consensus is ensured.
Abstract: Two similar Laplacian-based models for swarms with informed agents are proposed and analyzed analytically and numerically. In these models, each individual adjusts its velocity to match that of its neighbors and some individuals are given a preferred heading direction towards which they accelerate if there is no local velocity consensus. The convergence to a collective group swarming state with constant velocity is analytically proven for a range of parameters and initial conditions. Using numerical computations, the ability of a small group of informed individuals to accurately guide a swarm of uninformed agents is investigated. The results obtained in one of our two models are analogous to those found for more realistic and complex algorithms for describing biological swarms, namely, that the fraction of informed individuals required to guide the whole group is small, and that it becomes smaller for swarms with more individuals. This observation in our simple system provides insight into the possibly robust dynamics that contribute to biologically effective collective leadership and decision-making processes. In contrast with the more sophisticated models mentioned above, we can describe conditions under which convergence to consensus is ensured.
Flocking of multi-agent systems with a dynamic virtual leader (Shi, Hong; Wang, Long; Chu, Tianguang)
Int. J. Control 82, No. 1, 43-58 (2009). ISSN 0020-7179; ISSN 1366-5820
Abstract: This paper considers the flocking problem of a group of autonomous agents moving in Euclidean space with a virtual leader. We investigate the dynamic properties of the group for the case where the state of the virtual leader may be time-varying and the topology of the neighbouring relations between agents is dynamic. To track such a leader, we introduce a set of switching control laws that enable the entire group to generate the desired stable flocking motion. The control law acting on each agent relies on the state information of its neighbouring agents and the external reference signal (or ‘virtual leader’). Then we prove that, if the acceleration of the virtual leader is known, then each agent can follow the virtual leader, and the convergence rate of the centre of mass (CoM) can be estimated; if the acceleration is unknown, then the velocities of all agents asymptotically approach the velocity of the CoM, thus the flocking motion can be obtained. However, in this case, the final velocity of the group may not be equal to the desired velocity. Numerical simulations are worked out to illustrate our theoretical results.
Abstract: This paper considers the flocking problem of a group of autonomous agents moving in Euclidean space with a virtual leader. We investigate the dynamic properties of the group for the case where the state of the virtual leader may be time-varying and the topology of the neighbouring relations between agents is dynamic. To track such a leader, we introduce a set of switching control laws that enable the entire group to generate the desired stable flocking motion. The control law acting on each agent relies on the state information of its neighbouring agents and the external reference signal (or ‘virtual leader’). Then we prove that, if the acceleration of the virtual leader is known, then each agent can follow the virtual leader, and the convergence rate of the centre of mass (CoM) can be estimated; if the acceleration is unknown, then the velocities of all agents asymptotically approach the velocity of the CoM, thus the flocking motion can be obtained. However, in this case, the final velocity of the group may not be equal to the desired velocity. Numerical simulations are worked out to illustrate our theoretical results.
2009. november 25., szerda
Flocking control of multi-agent systems with application to nonholonomic multi-robots. (Li, Qin; Jiang, Zhong-Ping)
Kybernetika 45, No. 1, 84-100 (2009). ISSN 0023-5954
Abstract: We revisit the artificial potential based approach in the flocking control for multi-agent systems, where our main concerns are migration and trajectory tracking problems. The static destination or, more generally, the moving reference point is modeled by a virtual leader, whose information is utilized by some agents, called active agents (AA), for the controller design. We study a decentralized flocking controller for
the case where the set of AAs is fixed. Some results on the velocity consensus, collision avoidance, group configuration and robustness are proposed. Further, we apply the proposed controller to the observer based flocking control of a team of nonholonomic mobile robots.
Abstract: We revisit the artificial potential based approach in the flocking control for multi-agent systems, where our main concerns are migration and trajectory tracking problems. The static destination or, more generally, the moving reference point is modeled by a virtual leader, whose information is utilized by some agents, called active agents (AA), for the controller design. We study a decentralized flocking controller for
the case where the set of AAs is fixed. Some results on the velocity consensus, collision avoidance, group configuration and robustness are proposed. Further, we apply the proposed controller to the observer based flocking control of a team of nonholonomic mobile robots.
Collective cognition in animal groups (Iain D. Couzin)
Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
The remarkable collective action of organisms such as swarming ants, schooling fish and flocking birds has long captivated the attention of artists, naturalists, philosophers and scientists. Despite a long history of scientific investigation, only now are we beginning to decipher the relationship between individuals and group-level properties. This interdisciplinary effort is beginning to reveal the underlying principles of collective decision-making in animal groups, demonstrating how social interactions, individual state, environmental modification and processes of informational amplification and decay can all play a part in tuning adaptive response. It is proposed that important commonalities exist with the understanding of neuronal processes and that much could be learned by considering collective animal behavior in the framework of cognitive science.
The remarkable collective action of organisms such as swarming ants, schooling fish and flocking birds has long captivated the attention of artists, naturalists, philosophers and scientists. Despite a long history of scientific investigation, only now are we beginning to decipher the relationship between individuals and group-level properties. This interdisciplinary effort is beginning to reveal the underlying principles of collective decision-making in animal groups, demonstrating how social interactions, individual state, environmental modification and processes of informational amplification and decay can all play a part in tuning adaptive response. It is proposed that important commonalities exist with the understanding of neuronal processes and that much could be learned by considering collective animal behavior in the framework of cognitive science.
2009. november 21., szombat
An adaptive flocking algorithm for performing approximate clustering (Agostino Forestiero, Giandomenico Spezzano, Gianluigi Folino)
Institute of High-Performance Computing and Networking (ICAR), National Research Council (CNR), Italy Via Pietro Bucci 41C, I-87036 Rende (CS), Italy
Abstract: This paper presents an approach based on an adaptive bio-inspired method to make state of the art clustering algorithms scalable and to provide them with an any-time behavior. The method is based on the biology-inspired paradigm of a flock of birds, i.e. a population of simple agents interacting locally with each other and with the environment. The flocking algorithm provides a model of decentralized adaptive organization useful to solve complex optimization, classification and distributed control problems. This approach avoids the sequential search of canonical clustering algorithms and permits a scalable implementation. The method is applied to design two novel clustering algorithms based on the main principles of two popular clustering algorithms: DBSCAN and SNN. This apporach can identify clusters of widely varying shapes and densities and is able to extract an approximate view of the clusters whenever it is required. Both the algorithms have been evaluated on synthetic and real world data sets and the impact of the flocking strategy on performance has been evaluated.
2009 Elsevier Inc. All rights reserved.
Abstract: This paper presents an approach based on an adaptive bio-inspired method to make state of the art clustering algorithms scalable and to provide them with an any-time behavior. The method is based on the biology-inspired paradigm of a flock of birds, i.e. a population of simple agents interacting locally with each other and with the environment. The flocking algorithm provides a model of decentralized adaptive organization useful to solve complex optimization, classification and distributed control problems. This approach avoids the sequential search of canonical clustering algorithms and permits a scalable implementation. The method is applied to design two novel clustering algorithms based on the main principles of two popular clustering algorithms: DBSCAN and SNN. This apporach can identify clusters of widely varying shapes and densities and is able to extract an approximate view of the clusters whenever it is required. Both the algorithms have been evaluated on synthetic and real world data sets and the impact of the flocking strategy on performance has been evaluated.
2009 Elsevier Inc. All rights reserved.
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