Simulating collective transport of virtual ants
Authors: Guo, S., Wang, M., Notman, G., Chang, J., Zhang, J. and Liao, M.
Journal: Computer Animation and Virtual Worlds
Volume: 28
Issue: 3-4
eISSN: 1546-427X
ISSN: 1546-4261
DOI: 10.1002/cav.1779
Abstract:This paper simulates the behaviour of collective transport where a group of ants transports an object in a cooperative fashion. Different from humans, the task coordination of collective transport, with ants, is not achieved by direct communication between group individuals, but through indirect information transmission via mechanical movements of the object. This paper proposes a stochastic probability model to model the decision-making procedure of group individuals and trains a neural network via reinforcement learning to represent the force policy. Our method is scalable to different numbers of individuals and is adaptable to users' input, including transport trajectory, object shape, external intervention, etc. Our method can reproduce the characteristic strategies of ants, such as realign and reposition. The simulations show that with the strategy of reposition, the ants can avoid deadlock scenarios during the task of collective transport.
https://eprints.bournemouth.ac.uk/29461/
Source: Scopus
Simulating collective transport of virtual ants
Authors: Guo, S., Wang, M., Notman, G., Chang, J., Zhang, J. and Liao, M.
Journal: COMPUTER ANIMATION AND VIRTUAL WORLDS
Volume: 28
Issue: 3-4
eISSN: 1546-427X
ISSN: 1546-4261
DOI: 10.1002/cav.1779
https://eprints.bournemouth.ac.uk/29461/
Source: Web of Science (Lite)
Simulating collective transport of virtual ants
Authors: Zhang, J.
Journal: Computer Animation & Virtual Worlds
Volume: 28
Issue: 3-4
Publisher: John Wiley & Sons Inc.
ISSN: 1546-4261
DOI: 10.1002/cav.1779
Abstract:Guo, SH; Wang, ML; Notman, G; Chang, J; Zhang, JJ; Liao, MH
https://eprints.bournemouth.ac.uk/29461/
Source: Manual
Simulating collective transport of virtual ants
Authors: Guo, S.H., Wang, M.L., Notman, G., Chang, J., Zhang, J.J. and Liao, M.H.
Journal: Computer Animation & Virtual Worlds
Volume: 28
Issue: 3-4
ISSN: 1546-4261
Abstract:This paper simulates the behaviour of collective transport where a group of ants transports an object in a cooperative fashion. Different from humans, the task coordination of collective transport, with ants, is not achieved by direct communication between group individuals, but through indirect information transmission via mechanical movements of the object. This paper proposes a stochastic probability model to model the decision-making procedure of group individuals and trains a neural network via reinforcement learning to represent the force policy. Our method is scalable to different numbers of individuals and is adaptable to users' input, including transport trajectory, object shape, external intervention, etc. Our method can reproduce the characteristic strategies of ants, such as realign and reposition. The simulations show that with the strategy of reposition, the ants can avoid deadlock scenarios during the task of collective transport.
https://eprints.bournemouth.ac.uk/29461/
Source: BURO EPrints