-------------------------------------------------------------------------------------------------------------------------------------- Today I will More

-------------------------------------------------------------------------------------------------------------------------------------- Today I will show you how to set up an particle system which will works with the new MoGraph2 dynamics to create falling cubes. We are using the new version of Cinema 4D 11.5 for that. If you have the particle/dynamics set up in the way I did it in the tutorial you are able to use almost every object as an particle. I tried to achieve that effects with the previous version of Cinema 4D but failed because of the old dynamics system. I did some test with Thinking Particles and that's the way to do it if you're not calling 11.5 your own or if you need more control over your particles. If you know any other way or if you are an expert in Thinking Particles let us know in the comments below. Take a closer look at Matthias Muellers videos here: vimeo.com/user624589 -------------------------------------------------------------------------------------------------------------------------------------- As this is my first tutorial I would love to hear your comments about it. Suggestions, feedback or improvements are welcome. Write a comment or send me an Email. I am not sure if I'll record video tutorials frequently so I used the trial version of iShowU. Sorry about the watermark. Everybody who is not an owner of the new version but wants to follow me and play with the new modules go to Maxon maxon.net/ and download your trial version. Follow me on Twitter twitter.com/F3tch and take a look at my blog blog.grischatheissen.com .
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"A Nervous System Model for Direct Dynamics Animation" Direct dynamics animation consists of synthesizing the movements of a model from the More

"A Nervous System Model for Direct Dynamics Animation" Direct dynamics animation consists of synthesizing the movements of a model from the specification of its physical properties (mass and moment of inertia), the conditions of bond between its contracting parties, the conditions of contact with other bodies and the forces that acts on it. This approaching has the advantage to generate animations with physical realism. The problem, that continues relevant as inquiry object, is the control of the model: "What forces must be applied to the model to generate the desired movement?". The solution of the problem, presented in this work, assumes that the studied model consists of a structure of rigid link bodies whose movements are generated by internal actuators, with its forces defined by a nervous system. With use of artificial neural networks and evolutionary computation, the proposed controller is capable of adapting itself to control different articulated models, and to generate varied types of movements while it keeps the stability even with small variations of the terrain. The presented model possesses, in its core, a Central Pattern Generator (CPG) based on neural oscillators, that has their activities regulated by the sensorial module, to allow the balance of the structure and stability of the movement, responding to environment variations. For the adaptation to the articulated structure and learning of movements, the controller has a cognitive module, responsible for the search of neural parameters, through genetic algorithms, and the feedback networks (sensorial answers to environment variations), with genetic programming. Results are presented related to the control of models humanoid, cheetah, frog, luxo and luxo-2, having these last two ones equal topologies, but with variations in the sizes of the bodies and freedom of the joints. All the models are tested in plain land and with slope. ODE (http://www.ode.org/) was used to physical simulation.
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