Yuri Lenon B. Nogueira
ABSTRACT
In this paper, we approach the relevant problem of controlling locomotion of articulated figures taking Physics into account. The model proposed in this work determines the forces that actuate the articulated figure in order to obtain a desired locomotion goal. The controller developed for that purpose is based on some of the works on control of neuro-musculoskeletal representations of articulated figures and on neural oscillators encountered in the literature. Our model, however, takes a more generic approach using evolutionary computation and is capable of automatically generating motion gaits while maintaining stability independently of the environment and of the controlled articulated figure. The limitations of the proposed controller are also discussed.
- M. V. de Panne, R. Kim, and E. Fiume. Virtual wind-up toys for animation. In Proceedings of Graphics Interface '94, pages 208--215, 1994.Google Scholar
- V. DeSapio, J. Warren, O. Khatib, and S. Delp. Simulating the task-level control of human motion: a methodology and framework for implementation. The Visual Computer, 21(5):289--302, 2005.Google ScholarDigital Library
- S. Grillner. Control of locomotion in bipeds, tetrapods and fish. Brooks VB (ed) Handbook of Phisiology, Sect I: The nervous system, vol. II: Motor Control, pages 1179--1236, 1981.Google Scholar
- K. Hase, K. Miyashita, S. Ok, and Y. Arakawa. Human gait simulation with a neuromusculoskeletal model and evolutionary computation. Journal of Visualization and Computer Animation, 14(2):73--92, 2003.Google ScholarCross Ref
- K. Hase and N. Yamazaki. Computational evolution of human bipedal walking by a neuro-musculo-skeletal model. In Proceedings of the Third International Symposium on Artificial Life and Robotics, pages 174--177, 1998.Google Scholar
- Joseph Laszlo. Controlling bipedal locomotion for computer animation. MSc Thesis., University of Toronto, Department of Computer Science, 1996.Google Scholar
- Karl Sims. Evolving Virtual Creatures. In Computer Graphics (Siggraph '94 Proceedings), pages 15--22, 1994. Google ScholarDigital Library
- J. Koza. Genetic programming: A paradigm for genetically breeding populations of computer programs to solve problems. Technical Report STAN-CS-90-1314, Dept. of Computer Science, Stanford University, June 1990. Google ScholarDigital Library
- K. Matsuoka. Sustained oscillations generated by mutually inhibiting neurons with adaptation. Biological Cybernetics, 52(6):367--376, 1985.Google ScholarDigital Library
- K. Miyashita, S. Ok, and K. Hase. Evolutionary generation of human-like bipedal locomotion. Mechatronics, 13(8--9):791--807, 2003.Google Scholar
- A. Nagano, B. R. Umberger, M. W. Marzke, and K. G. M. Gerritsen. Neuromusculoskeletal computer modeling and simulation of upright, straight-legged, bipedal locomotion of australopithecus afarensis (a.1.288--1). American Journal of Physical Anthropology, 126(1):2--13, 2005.Google ScholarCross Ref
- J. Ni and A. Kato. A model of neuro-musculo-skeletal system for human locomotion under position constraint condition. ASME Journal of Biomechanical Engineering, 125:499--506, 2003.Google ScholarCross Ref
- S. Ok and D. Kim. Evolution of the CPG with sensory feedback for bipedal locomotion. In ICNC (2), pages 714--726, 2005. Google ScholarDigital Library
- A. Prochazka. The man-machine analogy in robotics and neurophysiology. Journal of Automatic Control, 12:4--8, 2002.Google ScholarCross Ref
- T. Reil and P. Husbands. Evolution of central pattern generators for bipedal walking in a real-time physics environment. IEEE Transactions on Evolutionary Computation, 6(2):159--168, April 2002. Google ScholarDigital Library
- W. Sellers, G. Cain, W. Wang, and R. Crompton. Stride lengths, speed and energy costs in walking of Australopithecus afarensis: Using evolutionary robotics to predict locomotion of early human ancestors. J R Soc Interface, 2(5):431--441, 2005.Google ScholarCross Ref
- R. Smith. Open Dynamics Engine (ODE). http://www.ode.org/, Visited on February 15, 2006.Google Scholar
- G. Taga. A model of the neuro-musculo-skeletal system for anticipatory adjustment of human locomotion during obstacle avoidance. Biological Cybernetics, 78(1):9--17, 1998.Google ScholarCross Ref
- G. Taga, Y. Yamaguchi, and H. Shimizu. Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment. Biological Cybernetics, 65(3):147--159, 1991.Google ScholarDigital Library
- D. Whitley. A genetic algorithm tutorial. Statistics and Computing, 4:65--85, 1994.Google ScholarCross Ref
- M. Williamson. Designing Rhythmic Motions using Neural Oscillators. In IROS, 1999.Google ScholarCross Ref
- K. Wolff and P. Nordin. Learning biped locomotion from first principles on a simulated humanoid robot using linear genetic programming. In Genetic and Evolutionary Computation - GECCO-2003, volume 2723 of LNCS, pages 495--506. Springer-Verlag, 2003. Google ScholarDigital Library
Index Terms
- A nervous system model for direct dynamics animation control based on evolutionary computation
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