Under the Guidance of

Transcription

1 Presented by Linga Venkatesh ( ) Deepak Jayanth ( ) Under the Guidance of Prof. Parag Chaudhuri

2 Flocking/Swarming/Schooling Nature Phenomenon Collective Behaviour by animals of same size Reasons - Avoiding predators - Search for food - Aero Dynamic Efficiency

3 Simulation of large number of characters are very common nowadays in movies, games and screensavers. Applications Computer animation Games Robotics Crowd Simulation Interactive Graphics, etc,.

4 1. Modeling each and every characters paths. Tedious task Keeping the birds from not colliding each frame Hard to edit Final result will be unrealistic 2. Allowing the characters to interact among themselves No need of animators work to some extent. Final Result will be realistic

5 This Approach resembles real world scenario This paper uses the particle system approach, which are used to model the dynamic "fuzzy objects like fire, smoke,etc..

6 Objects are made of clouds of particles rather than primitive types like polygon with definite boundary Collection of large no of particles with its own individual behaviour. Each particles have position, velocity, lifetime, color, size, etc.

7 Typical computer animation models only shape and physical properties of characters. The goal of behavior model simulate characters to handle details on their own. Behaviours ranging from simple path planning to complex emotions. The behaviors are represented as rules. Each boids(bird objects) as an actor.

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9 mass - scalar position - vector velocity - vector max_force - scalar max_speed - scalar orientation - N basis vectors

10 Based on forward Euler s Integration steering_force = truncate (steering_direction, max_force) acceleration = steering_force / mass velocity = truncate (velocity + acceleration, max_speed) position = position + velocity New Basis Vectors new_forward = normalize (velocity) approximate_up = normalize (approximate_up) new_side = cross (new_forward, approximate_up) new_up = cross (new_forward, new_side)

11 Seek and Flee desired_velocity = normalize (position - target) * max_speed steering = desired_velocity - velocity

12 Pursuit and Evasion Assume the quarry won t move for next T time units. Scale velocity by T and add that to current position

13 Offset Pursuit Refers to steering a path which passes near but not directly into a moving target. Compute an offset by a given radius R from the predicted future position of the target Use seek behaviour

14 Arrival target_offset = target - position distance = length (target_offset) ramped_speed = max_speed * (distance / slowing_distance) clipped_speed = minimum (ramped_speed, max_speed) desired_velocity = (clipped_speed / distance) * target_offset steering = desired_velocity - velocity

15 Wander and Explore The steering force takes a random walk from one direction to another. Next frame s steering force = previous value + Random displacement Cover region of space

16 Path following Circular tube specified by a curve and radius. For projection distance less than the path radius no corrective steering is required.

17 Axis considerations X axis pitch Y axis Yaw Z axis boids forward translation / roll Simple model of viscous speed damping Truncation of over anxious requests Gravity only to define banking Lift aligned with Yaxis

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19 For straight motion, no radial force Banking aligns the Y axis with gravity. When turning radial component grows larger Acceleration swings outward Centrifugal force tries to push the object away from the turning direction With corrective banking boids Y axis is aligned with Resultant acceleration. Produces realistic representation of how flying object move and orient themselves.

20 Birds seem to have a desire to stay close to the flock and a desire to avoid collisions within the flock. To build simulate flocks start with a boid model which supports geometry Add behaviours correspond to collision avoidance and urge to join the flock 1. Collision Avoidance 2. Velocity Matching 3. Flock Centering

21 Collision Avoidance

22 Collision Avoidance Flock Centering

23 Collision Avoidance Flock Centering Velocity Matching

24 Each individual behavioural urge will result in their own acceleration. Flight Model will AVERAGE them. This works pretty well BUT. AI techniques are needed to solve this problem.

25 Algorithm :: 1. Priorities will be assigned for each acceleration resulted from behavioural urges. 2. These acceleration requests are sorted in the descending order. 3. These are added to accumulator one by one. 4. The magnitude of each request is measured and added to another accumulator. 5. The above steps are performed until magnitude becomes greater than sum of priorities. 6. This magnitude is the final acceleration.

26 The boid model doesn t have senses like real animals. Simulated boids have direct access to geometrical database describing objects. It may not be possible to give each simulated boid the complete information about the world. Interestingly the aggregate motion of the flock depends on a limited localized view of the world.

27 The neighbour root is a spherical zone of sensitivity centered at boids local origin. Magnitude of sensitivity is inverse of exponent of distance. The field of sensitivity should be oriented in the direction of the boid.

28 To combine flock simulation with other animated action. It is to describe the timing of various flock actions by passing parameters to simulation softwares. We can animate dynamic parameter (global position or direction vector). Scripts control the direction of flock using this parameter. Path of each boid will be smooth though the goal changes abruptly.

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30 The motion of the simulated should also be affected by objects in the environment. There are two solutions for environmental collision avoidance Force Field Concept Steer-to-avoid

31 Force Field Concept Assumes a repulsion force in the space around the obstacles. Increases as the boid gets close to the obstacle. This produces good results but has drawbacks.

32 Steer-to-Avoid Aiming at point offset from silhouette edge computes radial vector

33 O(n 2 ) where n is the flock population. If a separate processor is assigned for each boid then, it can be reduced to O(n).

34 Models a polarized, non colliding flock motion Its difficult to measure how valid they are But still this model behaves similar to natural birds flock like motion The parameter can be adjusted to obtain many variations.

35 Reynolds, C. W.(1987), Flocks, Herds, and Schools: A Distributed Behavioral model, in Computer Graphics, 21(4) (SIGGRAPH '87 Conference Proceedings), pages Reynolds, C. W, Steering Behaviors For Autonomous Characters, in the proceedings of Game Developers Conference 1999 held in San Jose, California. Miller Freeman Game Group, San Francisco, California. Pages William T. Reeves, Particle Systems: A Technique for Modelling a Class of Fuzzy Objects, ACM Transactions on Graphics, April Potts, W. K., The Chorus-Line Hypothesis of Manoeuvre Coordination in Avian Flocks, letter in Nature, Vol. 309, May 24, 1984, pp