Bettering Simulation and Efficiency with an Superior Physics Solver

In mid-2015, Roblox unveiled a significant improve to its physics engine: the Projected Gauss-Seidel (PGS) physics solver. For the primary yr, the brand new solver was optionally available and offered improved constancy and better efficiency in comparison with the beforehand used spring solver.

In 2016, we added help for a various set of recent physics constraints, incentivizing builders emigrate to the brand new solver and lengthening the artistic capabilities of the physics engine. Any new locations used the PGS solver by default, with the choice of reverting again to the basic solver.

We ironed out some stability points related to excessive mass variations and complicated mechanisms by the introduction of the hybrid LDL-PGS solver in mid-2018. This made the outdated solver out of date, and it was fully disabled in 2019, robotically migrating all locations to the PGS.

In 2019, the efficiency was additional improved utilizing multi-threading that splits the simulation into jobs consisting of linked islands of simulating components. We nonetheless had efficiency points associated to the LDL that we lastly resolved in early 2020.

The physics engine remains to be being improved and optimized for efficiency, and we plan on including new options for the foreseeable future.

Implementing the Legal guidelines of Physics

The primary goal of a physics engine is to simulate the movement of our bodies in a digital setting. In our physics engine, we care about our bodies which can be inflexible, that collide and have constraints with one another.

A physics engine is organized into two phases: collision detection and fixing. Collision detection finds intersections between geometries related to the inflexible our bodies, producing acceptable collision data akin to collision factors, normals and penetration depths. Then a solver updates the movement of inflexible our bodies below the affect of the collisions that had been detected and constraints that had been offered by the person.

The movement is the results of the solver deciphering the legal guidelines of physics, akin to conservation of power and momentum. However doing this 100% precisely is prohibitively costly, and the trick to simulating it in real-time is to approximate to extend efficiency, so long as the result’s bodily lifelike. So long as the fundamental legal guidelines of movement are maintained inside an inexpensive tolerance, this tradeoff is totally acceptable for a pc sport simulation.

Taking Small Steps

The primary concept of the physics engine is to discretize the movement utilizing time-stepping. The equations of movement of constrained and unconstrained inflexible our bodies are very troublesome to combine instantly and precisely. The discretization subdivides the movement into small time increments, the place the equations are simplified and linearized making it attainable to resolve them roughly. Which means throughout every time step the movement of the related components of inflexible our bodies which can be concerned in a constraint is linearly approximated.

Though a linearized drawback is simpler to resolve, it produces drift in a simulation containing non-linear behaviors, like rotational movement. Later we’ll see mitigation strategies that assist cut back the drift and make the simulation extra believable.


Having linearized the equations of movement for a time step, we find yourself needing to resolve a linear system or linear complementarity drawback (LCP). These methods could be arbitrarily giant and might nonetheless be fairly costly to resolve precisely. Once more the trick is to search out an approximate answer utilizing a sooner methodology. A contemporary methodology to roughly remedy an LCP with good convergence properties is the Projected Gauss-Seidel (PGS). It’s an iterative methodology, which means that with every iteration the approximate answer is introduced nearer to the true answer, and its last accuracy will depend on the variety of iterations.

This animation reveals how a PGS solver adjustments the positions of the our bodies at every step of the iteration course of, the target being to search out the positions that respect the ball and socket constraints whereas preserving the middle of mass at every step (this can be a kind of positional solver utilized by the IK dragger). Though this instance has a easy analytical answer, it’s a very good demonstration of the concept behind the PGS. At every step, the solver fixes one of many constraints and lets the opposite be violated. After a couple of iterations, the our bodies are very near their right positions. A attribute of this methodology is how some inflexible our bodies appear to vibrate round their last place, particularly when coupling interactions with heavier our bodies. If we don’t do sufficient iterations, the yellow half may be left in a visibly invalid state the place one in every of its two constraints is dramatically violated. That is referred to as the excessive mass ratio drawback, and it has been the bane of physics engines because it causes instabilities and explosions. If we do too many iterations, the solver turns into too sluggish, if we don’t it turns into unstable. Balancing the 2 sides has been a painful and lengthy course of.

Mitigation Methods

A solver has two main sources of inaccuracies: time-stepping and iterative fixing (there’s additionally floating level drift but it surely’s minor in comparison with the primary two). These inaccuracies introduce errors within the simulation inflicting it to float from the right path. A few of this drift is tolerable like barely completely different velocities or power loss, however some should not like instabilities, giant power positive factors or dislocated constraints.

Due to this fact plenty of the complexity within the solver comes from the implementation of strategies to attenuate the affect of computational inaccuracies. Our last implementation makes use of some conventional and a few novel mitigation methods:

  1. Heat beginning: beginning with the answer from a earlier time-step to extend the convergence charge of the iterative solver
  2. Submit-stabilization: reprojecting the system again to the constraint manifold to forestall constraint drift
  3. Regularization: including compliance to the constraints making certain an answer exists and is exclusive
  4. Pre-conditioning: utilizing a precise answer to a linear subsystem, enhancing the steadiness of complicated mechanisms

Methods 1, 2 and three are fairly conventional, however three has been improved and perfected by us. Additionally, though four shouldn’t be unprecedented, we haven’t seen any sensible implementation of it. We use an unique factorization methodology for giant sparse constraint matrices and a brand new environment friendly method of mixing it with the PGS. The ensuing implementation is barely barely slower in comparison with pure PGS however ensures that the linear system coming from equality constraints is solved precisely. Consequently, the equality constraints undergo solely from drift coming from the time discretization. Particulars on our strategies are contained in my GDC 2020 presentation. At present, we’re investigating direct strategies utilized to inequality constraints and collisions.

Getting Extra Particulars

Historically there are two mathematical fashions for articulated mechanisms: there are lowered coordinate strategies spearheaded by Featherstone, that parametrize the levels of freedom at every joint, and there are full coordinate strategies that use a Lagrangian formulation.

We use the second formulation as it’s much less restrictive and requires a lot less complicated arithmetic and implementation.

The Roblox engine makes use of analytical strategies to compute the dynamic response of constraints, versus penalty strategies that had been used earlier than. Analytics strategies had been initially launched in Baraff 1989, the place they’re used to deal with each equality and non-equality constraints in a constant method. Baraff noticed that the contact mannequin could be formulated utilizing quadratic programming, and he offered a heuristic answer methodology (which isn’t the tactic we use in our solver).

As an alternative of utilizing force-based formulation, we use an impulse-based formulation in velocity area, initially launched by Mirtich-Canny 1995 and additional improved by Stewart-Trinkle 1996, which unifies the therapy of various contact varieties and ensures the existence of an answer for contacts with friction. At every timestep, the constraints and collisions are maintained by making use of instantaneous adjustments in velocities as a result of constraint impulses. A wonderful rationalization of why impulse-based simulation is superior is contained within the GDC presentation of Catto 2014.

The frictionless contacts are modeled utilizing a linear complementarity drawback (LCP) as described in Baraff 1994. Friction is added as a non-linear projection onto the friction cone, interleaved with the iterations of the Projected Gauss-Seidel.

The numerical drift that introduces positional errors within the constraints is resolved utilizing a post-stabilization method utilizing pseudo-velocities launched by Cline-Pai 2003. It includes fixing a second LCP within the place area, which tasks the system again to the constraint manifold.

The LCPs are solved utilizing a PGS / Impulse Solver popularized by Catto 2005 (additionally see Catto 2009). This methodology is iterative and considers every particular person constraints in sequence and resolves it independently. Over many iterations, and in ideally suited circumstances, the system converges to a worldwide answer.

Moreover, excessive mass ratio points in equality constraints are ironed out by preconditioning the PGS utilizing the sparse LDL decomposition of the constraint matrix of equality constraints. Dense submatrices of the constraint matrix are sparsified utilizing a way we name Physique Splitting. That is much like the LDL decomposition utilized in Baraff 1996, however permits extra common mechanical methods, and solves the system in constraint area. For extra data, you may see my GDC 2020 presentation.

The structure of our solver follows the concept of Guendelman-Bridson-Fedkiw, the place the speed and place stepping are separated by the constraint decision. Our time sequencing is:

  1. Advance velocities
  2. Constraint decision in velocity area and place area
  3. Advance positions

This scheme has the benefit of integrating solely legitimate velocities, and limiting latency in exterior drive software however permitting a small quantity of perceived constraint violation as a result of numerical drift.

A wonderful reference for inflexible physique simulation is the guide Erleben 2005 that was just lately made freely out there. Yow will discover on-line lectures about physics-based animation, a weblog by Nilson Souto on constructing a physics engine, an excellent GDC presentation by Erin Catto on fashionable solver strategies, and boards just like the Bullet Physics Discussion board and GameDev that are wonderful locations to ask questions.

In Conclusion

The sector of sport physics simulation presents many attention-grabbing issues which can be each thrilling and difficult. There are alternatives to be taught a considerable quantity of cool arithmetic and physics and to make use of fashionable optimizations methods. It’s an space of sport growth that tightly marries arithmetic, physics and software program engineering.

Even when Roblox has a very good inflexible physique physics engine, there are areas the place it may be improved and optimized. Additionally, we’re engaged on thrilling new tasks like fracturing, deformation, softbody, material, aerodynamics and water simulation.

Neither Roblox Company nor this weblog endorses or helps any firm or service. Additionally, no ensures or guarantees are made relating to the accuracy, reliability or completeness of the knowledge contained on this weblog.

This weblog put up was initially revealed on the Roblox Tech Weblog.

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