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CS 378: Computer Game Technology

CS 378: Computer Game Technology. Physics for Games Spring 2012. Game Physics – Basic Areas. Point Masses Particle simulation Collision response Rigid-Bodies Extensions to non-points Soft Body Dynamic Systems Articulated Systems and Constraints Collision Detection. Physics Engines.

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CS 378: Computer Game Technology

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  1. CS 378: Computer Game Technology Physics for Games Spring 2012

  2. Game Physics – Basic Areas • Point Masses • Particle simulation • Collision response • Rigid-Bodies • Extensions to non-points • Soft Body Dynamic Systems • Articulated Systems and Constraints • Collision Detection

  3. Physics Engines • API for collision detection • API for kinematics (motion but no forces) • API for dynamics • Examples • Box2d • Bullet • ODE (Open Dynamics Engine) • PhysX • Havok • Etc. University of Texas at Austin CS 378 – Game Technology Don Fussell 3

  4. Particle dynamics and particle systems • A particle system is a collection of point masses that obeys some physical laws (e.g, gravity, heat convection, spring behaviors, …). • Particle systems can be used to simulate all sorts of physical phenomena: University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell 4

  5. Particle in a flow field • We begin with a single particle with: • Position, • Velocity, • Suppose the velocity is actually dictated by some driving function g: y x g(x,t) University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell 5

  6. Vector fields • At any moment in time, the function g defines a vector field over x: • How does our particle move through the vector field? University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell 6

  7. Diff eqs and integral curves • The equation is actually a first order differential equation. • We can solve for x through time by starting at an initial point and stepping along the vector field: • This is called an initial value problem and the solution is called an integral curve. Start Here University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell 7

  8. Eulersmethod • One simple approach is to choose a time step, Dt, and take linear steps along the flow: • Writing as a time iteration: • This approach is called Euler’s method and looks like: • Properties: • Simplest numerical method • Bigger steps, bigger errors. Error ~ O(Dt2). • Need to take pretty small steps, so not very efficient. Better (more complicated) methods exist, e.g., “Runge-Kutta” and “implicit integration.”

  9. Particle in a force field • Now consider a particle in a force field f. • In this case, the particle has: • Mass, m • Acceleration, • The particle obeys Newton’s law: • The force field f can in general depend on the position and velocity of the particle as well as time. • Thus, with some rearrangement, we end up with: University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell 9

  10. Second order equations This equation: is a second order differential equation. Our solution method, though, worked on first order differential equations. We can rewrite this as: where we have added a new variable v to get a pair of coupled first order equations. University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell 10

  11. Phase space • Concatenate x and v to make a 6-vector: position in phase space. • Taking the time derivative: another 6-vector. • A vanilla 1st-order differential equation. University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell 11

  12. Differential equation solver Starting with: Applying Euler’s method: And making substitutions: Writing this as an iteration, we have: Again, performs poorly for large Dt. University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell 12

  13. Particle structure How do we represent a particle? Position in phase space position velocity force accumulator mass University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell 13

  14. Single particle solver interface getDim getState setState derivEval University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell 14

  15. n time particles Particle systems In general, we have a particle system consisting of n particles to be managed over time: University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell 15

  16. n time particles Particle system solver interface For n particles, the solver interface now looks like: get/setState getDim derivEval University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell 16

  17. Particle system diff. eq. solver We can solve the evolution of a particle system again using the Euler method: University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell 17

  18. Forces • Each particle can experience a force which sends it on its merry way. • Where do these forces come from? Some examples: • Constant (gravity) • Position/time dependent (force fields) • Velocity-dependent (drag) • Combinations (Damped springs) • How do we compute the net force on a particle? University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell 18

  19. Particle systems with forces • Force objects are black boxes that point to the particles they influence and add in their contributions. • We can now visualize the particle system with force objects: nf forces n time particles Fnf F1 F2 University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell 19

  20. Gravity and viscous drag The force due to gravity is simply: p->f += p->m * F->G Often, we want to slow things down with viscous drag: p->f -= F->k * p->v University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell 20

  21. r= rest length Damped spring Recall the equation for the force due to a spring: We can augment this with damping: The resulting force equations for a spring between two particles become: University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell 21

  22. derivEval Clear forces Loop over particles, zero force accumulators Calculate forces Sum all forces into accumulators Return derivatives Loop over particles, return v and f/m Clear force accumulators 1 Apply forces to particles 2 Fnf F1 F2 F3 3 Return derivativesto solver University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell 22

  23. P N Bouncing off the walls • Add-on for a particle simulator • For now, just simple point-plane collisions A plane is fully specified by any point P on the plane and its normal N. University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell 23

  24. x v N P Collision Detection How do you decide when you’ve crossed a plane? University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell 24

  25. v v N P Normal and tangential velocity To compute the collision response, we need to consider the normal and tangential components of a particle’s velocity. University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell 25

  26. Collision Response before after Without backtracking, the response may not be enough to bring a particle to the other side of a wall. In that case, detection should include a velocity check: University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell 26

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