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Jumping AI for Unreal Engine

Authors: Silva, Gabriel Quaresma Moreira da;

Jumping AI for Unreal Engine

Abstract

Pathfinding plays a vital role in video games, whether in terms of gameplay mechanics or player immersion. Commonly used methods only allow the simplest types of movements like walking and running. Although seldom, other types of movement, like swimming and flying, are also considered. Even rarer are mechanisms that natively contemplate jumps without the need of extra intervention of game developers. Most games overlook these movements on Non Player Characters, decreasing the realism of the experience. This dissertation discusses the limitations of Navigation Meshes when it comes to take jumps into consideration, while offering solutions to some of its problems. However, found solutions lack in automaticity, requiring high implementation times. In the interest of improving upon this problem, a new solution using grid-based any-angle pathfinding is proposed. In this approach, each cell of this navigation grid constitutes a voxel that delimits a small 3D space and is expressed in a shape of a cube. Voxels discretize the game world and are explored by a search algorithm to achieve pathfinding with jumps. In this context, performance is critical and the paths should be optimal and efficient. Results show that the voxel based solution can be successfully applied in game development and that it has relevant characteristics that could justify choosing this method over the navigation meshes alternatives for jumping.

Country
Portugal
Related Organizations
Keywords

Path planning on grids, Jumping AI, Voxel based worlds, Pathfinding

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
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510
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