Programming Paradigms in Graphics pp 137-153 | Cite as
Reactivity, Concurrency, Data-flow and Hierarchical Preemption for Behavioural Animation
Abstract
Behavioural models offer the ability to simulate autonomous entities. Such entities perceive their environment, and communicate and decide actions to execute, on themselves or on their environment. These reactive systems treat flows of data to and from the environment in a complex way. They need modularity, concurrency and hierarchy, and involve task control and preemption. We examine the adequacy for decision making of the behavioural model in the following programming paradigms: reactivity, concurrency, data-flow and hierarchical preemption.
Reactive languages provide complete design environments. The specification of concurrent behaviours is naturally supported in the synchronous languages, and they address control intensive applications (sequencing and preempting tasks) as well as computation intensive applications (data-flow). Signal GTi is an extension of the language Signal where data-flow processes can be composed into nested preemptive tasks.
An application in the simulation of a transportation system shows how these programming paradigms can be of use, and how Signal GTi can support their implementation.
Keywords
State Machine Behavioural Model Simulation Platform Finite State Automaton Dynamic EntityPreview
Unable to display preview. Download preview PDF.
References
- [1]O. Ahmad, J. Cremer, S. Hansen, J. Kearney, and P. Willemsen. Hierarchical, concurrent state machines for behavior modeling and scenario control. In Conference on Al, Planning, and Simulation in High Autonomy Systems, Gainesville, Florida, USA, 1994.Google Scholar
- [2]F. Arbab and E. Rutten. MANIFOLD: a programming model for massive parallelism. In Proceedings of the Working Conference on Massively Parallel Programming Models, pages 151–159, IEEE, Berlin, German, September 1993.CrossRefGoogle Scholar
- [3]B. Arnaldi, R. Cozot, and S. Donikian. Virtual urban environment for the simulation of an automated electrical cars platoon in the praxitele project. In Second Eurographics Workshop on Virtual Environments, Monte Carlo, January 1995.Google Scholar
- [4]B. Arnaldi and G. Dumont. Vehicle simulation versus vehicle animation. In Third Eurographics Workshop on Animation and Simulation, Cambridge, September 1992.Google Scholar
- [5]N. I. Badler, C. B. Phillips, and B. L. Webber. Simulating Humans: Computer Graphics Animation and Control. Oxford University Press, 1993.Google Scholar
- [6]Norman I. Badler, Bonnie L. Webber, Jugal Kalita, and Jeffrey Esakov, editors. Making them move: mechanics, control, and animation of articulated figures. Morgan Kaufmann, 1991Google Scholar
- [7]A. Benveniste and G. Berry. The synchronous approach to reactive and real-time systems. Proceedings of the IEEE, 79 (9): 1270–1282, September 1991.Google Scholar
- [8]M. Booth, J. Cremer, and J. Kearney. Scenario control for real-time driving simulation. In Fourth Eurographics Workshop on Animation and Simulation, pages 103–119, Politechnical University of Catalonia, September 1993.Google Scholar
- [9]S. Donikian. Les modèles comportementaux pour la génération du mouvement d’objets dans une scène. Revue Internationale de CFAO et d’Infographie,9(6):847–871,1994. Numéro Spécial lre journées AFIG Groplan.Google Scholar
- [10]S. Donikian and B. Amaldi. Complexity and concurrency for behavioral animation and simulation. In G. Hégron and O. Fahlander, editors, Fifth Eurographics Workshop on Animation and Simulation, Oslo, Norvège, September 1994.Google Scholar
- [11]S. Donikian and R. Cozot. General animation and simulation platform. In D. Terzopoulos and D. Thalmann, editors, Computer Animation and Simulation’95, pages 197–209, Springer-Verlag, 1995.Google Scholar
- [12]S. Donikian and G. Hégron. A declarative design method for 3d scene sketch modeling. In EUROGRAPHICS’93 Conference Proceedings, Barcelona, Spain, September 1993.Google Scholar
- [13]N. Halbwachs. Synchronous programming of reactive systems. Kluwer, 1993.Google Scholar
- [14]G. Hégron and B. Arnaldi. Computer Animation: Motion and Deformation Control. Eurographics’ 92 Tutorial Notes, Eurographics Technical Report Series, Cambridge (Grande-Bretagne ), September 1992.Google Scholar
- [15]J. Kearney, J. Cremer, and S. Hansen. Motion control through communicating, hierarchical state machines. In G. Hegron and O. Fahlander, editors, Fifth Eurographics Workshop on Animation and Simulation, Oslo, Norway, September 1994.Google Scholar
- [16]P. Le Guernic, M. Le Borgne, T. Gautier, and C. Le Maire. Programming real time application with SIGNAL. Proceedings of the IEEE, 79 (9): 1321–1336, September 1991.CrossRefGoogle Scholar
- [17]H. Marchand, E. Rutten, and M. Samaan. Specifying and verifying a transformer station in SIGNAL and SIGNALGTi. Research Report 2521, INRIA, March 1995. (ftp ftp.inria.fr, file /INRIA/publication/RR/RR-2521.ps.gz).Google Scholar
- [18]M. Parent and P. Daviet. Automatic driving for small public urban vehicles. In Intelligent Vehicle Symposium, Tokyo, Japon, July 1993.Google Scholar
- [19]Gary Ridsdale and Tom Calvert. Animating microworlds from scripts and relational constraints. In N. Magnenat-Thalmann and D. Thalmann, editors, Computer Animation ‘80 (Second workshop on Computer Animation), pages 107–118, Springer-Verlag, April 1990.Google Scholar
- [20]E. Rutten and P. Le Guemic. The sequencing of data flow tasks in SIGNAL. In Proceedings of the ACM SIGPLAN Workshop on Language, Compiler and Tool Support for Real-Time Systems, Orlando, Florida, June 1994.Google Scholar
- [21]Xiaoyuan Tu and Demetri Terzopoulos. Artificial fishes: physics, locomotion, perception, behavior. In Computer Graphics (SIGGRAPH’94 Proceedings), pages 43–50, Orlando, Florida, July 1994.Google Scholar
- [22]Michiel van de Panne and Eugene Fiume. Sensor-actuator networks. In James T. Kajiya, editor, Computer Graphics (SIGGRAPH ‘83 Proceedings), pages 335–342, August 1993.Google Scholar