SteerSuite underlying data structure analysis

Before porting the SteerSuite to CUDA code, I have to fully understand how SteerSuite works.

From http://www.magix.ucla.edu/steersuite/, you can download the code.

There are seven projects in the SteerSuite solution, pprAI, simpleAI, steerlib, steersim, steerbench, steertool, and glfw. Most of the work will rely on the first three projects which includes the underlying data structure storing agents and obstacles, and how each agent move with the help of the AI.

First of all, all the data are stored in the GridDatabase2D class, in the form of an array of SpatialDatabaseItemPtr. And each item can either be an agent or an obstacle, depending on the initial options. Beside this array, another array of GridCell stores the identical database, but only the pointers to the original data. The whole geometry is divided into this array of GridCell, and if agents and obstacles are close enough, their pointers will be store in a GridCell.

Every time, each agent is updated by using corresponding AIs, either simpleAI or pprAI. First step, each agent will read its neighborhood information from the whole database, then do the corresponding computation, at last, the new position will be written back to the agent, and the content of GridCell will be changed as well as the position changes of the agents.

By examining the simpleAI, pprAI, and steerlib projects, there are six subclasses of SpatialDatabaseItem, i.e. SimpleAgent, PPRAgent, BoxObstacle, CircleObstacle, and OrientedBoxObstacle. Now I will only take care of SimpleAgent.


In SimpleAgent, there are _position, _velocity, _forward, _enabled, _radius, and _goalQueue as the class members. Because some of them uses self-defined data like Util::Point, Util::Vector and standard template library like std::queue<SteerLib::AgentGoalInfo>, I have to use float3 and array instead.

The basic routine will be that first copying data from host to device, then updating each agent in parallel at device side, finally copying the modified data back.

If this method does not work well, it may be worth trying rewrite database part completely in CUDA. Let us see.