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About pandaa

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  1. Yes, I wrote a demo to test the two algorithms, refer to the open source: https://github.com/arirahikkala/astar-jps   I found a thread here that people discuss jps performance: http://www.bay12forums.com/smf/index.php?topic=92732.0
  2. I tested the Jump point search algorithm(http://harablog.wordpress.com/2011/09/07/jump-point-search/) and the classic AStar,  and found that JPS is slow than classic AStar. The paper said jps is 2-30 times faster than classic AStar, I don't know why. The biggest comsumption is jump function, because it need visit so many tiles to find jump point, I highlight all the visited tiles in jump function.    The dashed lines are the horizontal and vertical lines that are checked while checking the diagonal.
  3. http://aigamedev.com/open/article/clearance-based-pathfinding/ Is it possible to handle dynamic obstacle in clearance-based pathfinding? how about the performance?
  4. pandaa

    Collision avoidance in RTS game

    I don't want to use steering behavior because it can cause jitter and oscillation. StarCraft2 use steering behavior, but it push the stationary units away in physical simulation.
  5. I tested steering behavior and RVO for collision avoidance in my rts game, but they are not good for stationary unit avoidance. Is there any good solutions?
  6. pandaa

    Navigation Mesh Generation

    I am developing a RTS game, and replaced the grid pathfing with navmesh for the performance reason. But the new problems comes, handling dynamic obstacle is not so effecient in navmesh.
  7. pandaa

    Papers on RVO/HRVO?

    RVO work well in collision avoidance between moved agents, but when moved agents avoid stationary agents, it will get stuck sometimes.
  8. Thank you! I'm trying to understand RVO solution.
  9. Two agents, A(position:Vector3, velocity:Vector3), B(position:Vector3, velocity:Vector3), Move to Goal: Vector3, How to calculate the Velocity Obstacle? thanks!
  10. Not really. Velocity obstacle algorithms use velocities as an input to compute regions in the plane; control of your entity in order to avoid those regions is a conceptually separate step and it is achieved by applying forces, exactly like in the case of traditional steering behaviours and any other type of physically decent dynamic simulation. You might be confused by the fact that many steering behaviours compute forces rather directly by adding up components instead of explicitly computing the appropriate acceleration for a desired velocity and location like in typical VO setups, but they have an equally advisory value; for example, these forces should be realistically clipped to a certain maximum force, further reduced to avoid exceeding velocity limits, and so on. You can combine RVOs and steering behaviours in a number of ways, most likely by giving priority to RVO-based collision avoidance: for example, you might compute the force your steering behaviours would like to apply and correct it by braking and by swerving towards the "best" side of the RVO if it would cause a collision. [/quote] thanks, I'll have a try.
  11. steering behavior is force-based, RVO is velocity-based
  12. Replace the obstacle avoidance of steering behavior with RVO, is it possible?
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