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Dead Reckoning

Dead Reckoning. References: Gamasutra ( 1 ), Gamedev ( 1 ) Forum articles ( 1 ). Introduction. Fast-paced interaction in twitch games Players expect the level of performance of distributed games to approximate that of single computer/player game

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Dead Reckoning

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  1. Dead Reckoning References: Gamasutra (1), Gamedev (1) Forum articles (1)

  2. Introduction • Fast-paced interaction in twitch games • Players expect the level of performance of distributed games to approximate that of single computer/player game • Solution: dedicated network (end-to-end latency 150-200 ms)

  3. Dead Reckoning • From DIS (distributed interactive simulation) protocol of SIMNET project, DoD, USA • Was for networking tank simulators • Objectives: latency hiding, bandwidth reduction • Basic Ideas: • Agree in advance on a set of algorithms that can be used by all players to extrapolate the behavior of entities • Update threshold: how far reality should be allowed to get from these extrapolations before a correction is issued

  4. If the motion is still within the DR threshold (on Owner), no updates required. Dead Reckoning [point-to-point] [linear] [quadratic]

  5. No path Noticeably jerky unless one packet is received per frame New packet [p] New packet [p] DR Algorithm (point-to-point)

  6. New packet [p,v] DR Algorithm (linear) moves along this v, until … Path does not consider change of velocity

  7. New packet [p,v,a] DR Algorithm (quadratic) moves along this v and a, until … NewPosition = OldPosition + Velocity*time + 0.5*Acceleration*(time)2 Path does not consider change of acceleration – jerk

  8. PDU (protocol data unit) • Data packet representing each entity • Kinematic state: position, velocity, acceleration, orientation • Other info: damage level, turret … • Which dead reckoning algorithm to use • Threshold: jerkiness vs. more PDUs to be sent

  9. Smoothing • Jerkiness: due to sudden update of position • Use smoothing algorithm to lessen the apparent jerkiness [more later]

  10. Extension • Predictive Contract • State-based, rather than kinematic-based • State: “drive along road to waypoint” • If the definition of roads, the specific waypoint, the way of driving (right-side), … are known to all players, the vehicle could be computed w/o any network traffic • Others: “turn on/off the sensors”, “send out radio report”

  11. Summary • Dead reckoning is not free: every computer runs an algorithm to extrapolate each entity • Trade processor cycles to reduced network use and apparent latency • If all entities behave unpredictably all the time, DR offers little gain

  12. DR Smoothing with Cubic Splines (ref) • Jerkiness is due to the sudden update of position • The following method ensures smooth positional transition while ensuring accuracy by packet (server) updates • Only cubic (parametric) curve can represent R3 curve [quadratic curves are planar] • Hermite curves are most common. Two points and their corresponding tangents need to be specified (by quadratic kinematics laws)

  13. A Draftsman’s Spline

  14. h1(s) = 2s33s2 + 1 h2(s) = 2s3 + 3s2 h3(s) = s3  2s2 + s h4(s) = s3  s2 P(s) = P1h1(s) + P2h2(s) + T1h3(s) + T2h4(s) P’(s)=P1h1’(s) + P2h2’(s) + T1h3’(s) + T2h4’(s) h1’ = 6s2  6s h2’ = 6s2+6s h3’= 3s2  4s+1 h4’= 3s2– 2s Note: end tangent vectors are dP/ds Math of Hermite Curves

  15. s=1 t=T s=0 t=0 Reparameterization

  16. Algorithm • When a packet [p,v,a] arrives, begin creating a cubic spline for next position • Approximate (extrapolate) the [p,v] after time T • A piece of Hermite curve is defined by two end points and two end tangents • P1: current position • T1: (scaled) current velocity • P2: extrapolated position after T • T2: (scaled) velocity after T T: estimated interval between packets

  17. New packet [p,v,a] Details Move with current spline T2 The position gradually changes from P1 (s=0) to P2 (s=1, t=T) P2 T1 P1 Start a new spline by 1. Current [p,v] 2. New [p,v] computed by [p,v,a] after time T

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