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Data. Structures. Ron Penton. TM for Game. Programmers. Team LRN Ron Penton's lifelong dream has always been to be a game programmer. From the.
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To handle this, we will need to compare units not only in the same cell, but in neighboring cells too.

Data Structures and Algorithms for Game Developers 1ED + CD-Rom

Now we have a function that will take a single unit and a list of other units and see if there are any hits. Note that we now also pass in the coordinates of the cell, not just its unit list. Those additional handleUnit calls look for hits between the current unit and units in four of the eight neighboring cells.

Consider what would happen if we did check all eight neighboring cells. When finding hits for A, we would look at its neighbor on the right and find B. Then, when finding hits for B, we would look at its neighbor on the left and find A. Only looking at half of the neighboring cells fixes that. If we have small cells and large attack distances, we may need to scan a bunch of neighboring cells several rows out.

Instead, I tried to organize this by their essential characteristics.

My hope is that once you do learn about quadtrees and binary space partitions BSPs and the like, this will help you understand how and why they work and why you might choose one over the other. Our grid example partitioned space into a single flat set of cells. In contrast, hierarchical spatial partitions divide the space into just a couple of regions. This process continues recursively until every region has fewer than some maximum number of objects in it.

Flat data structures are easier to reason about and simpler to implement. Memory usage is constant. It can be faster to update when objects change their positions. When an object moves, the data structure needs to be updated to find the object in its new location. With a hierarchical spatial partition, this can mean adjusting several layers of the hierarchy. It handles empty space more efficiently. Imagine in our earlier example if one whole side of the battlefield was empty. Instead of lots of little partitions to walk, there is a single big one.

It handles densely populated areas more efficiently. This is the other side of the coin: if you have a bunch of objects all clumped together, a non-hierarchical partition can be ineffective. A hierarchical partition will adaptively subdivide that into smaller partitions and get you back to having only a few objects to consider at a time. In our sample code, the grid spacing was fixed beforehand, and we slotted units into cells.

The goal is have a balanced partitioning where each region has roughly the same number of objects in order to get the best performance. Consider in our grid example if all of the units were clustered in one corner of the battlefield. Objects can be added incrementally. Adding an object means finding the right partition and dropping it in, so you can do this one at a time without any performance issues. Objects can be moved quickly. With fixed partitions, moving a unit means removing it from one and adding it to another.

If the partition boundaries themselves change based on the set of objects, then moving one can cause a boundary to move, which can in turn cause lots of other objects to need to be moved to different partitions. The partitions can be imbalanced. Of course, the downside of this rigidity is that you have less control over your partitions being evenly distributed. If objects clump together, you get worse performance there while wasting memory in the empty areas. Spatial partitions like BSPs and k-d trees split the world recursively so that each half contains about the same number of objects.

To do this, you have to count how many objects are on each side when selecting the planes you partition along. Bounding volume hierarchies are another type of spatial partition that optimizes for the specific set of objects in the world. You can ensure the partitions are balanced. This gives not just good performance, but consistent performance: if each partition has the same number of objects, you ensure that all queries in the world will take about the same amount of time.

When you need to maintain a stable frame rate, this consistency may be more important than raw performance. This is why these kinds of partitions are more frequently used for art and static geometry that stays fixed during the game. If the partitioning is object-independent, but hierarchy is object-dependent:. One spatial partition deserves special mention because it has some of the best characteristics of both fixed partitions and adaptable ones: quadtrees. A quadtree starts with the entire space as a single partition. If the number of objects in the space exceeds some threshold, it is sliced into four smaller squares.

The boundaries of these squares are fixed: they always slice space right in half. Then, for each of the four squares, we do the same process again, recursively, until every square has a small number of objects in it.

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Adding a new object means finding the right square and adding it. If that bumps that square above the maximum count, it gets subdivided. The other objects in that square get pushed down into the new smaller squares. Adding a single object can never trigger more than one subdivision. Removing objects is equally simple. This, of course, follows from the above.

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The partitions are balanced. You can treat your spatial partition as the place where the objects in your game live, or you can consider it just a secondary cache to make look-up faster while also having another collection that directly holds the list of objects. Traversing all objects is faster.

Imagine if, in our earlier example, most of the cells were empty. Having to walk the full grid of cells to find the non-empty ones can be a waste of time. A second collection that just stores the objects gives you a way to walk all them directly. You have two data structures, one optimized for each use case. Courses in this program.

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