Real‐time path planning in heterogeneous environments

Tibboel²,

Proceedings of the Seventh International Conference on Motion in Games

2014

Self Cite

The Weighted Region Problem is defined as the problem of finding a cost-optimal path in a weighted planar polygonal subdivision. Searching for paths on a grid representation of the scene is fast and easy to implement. However, grid representations do not capture the exact geometry of the scene. Hence, grid paths can be inaccurate or might not even exist at all. Methods that work on an exact representation of the scene can approximate an optimal path up to an arbitrarily small -error. However, these methods are computationally inefficient and thus not well-suited for real-time applications. In this paper, we analyze the quality of optimal paths on a 8-neighbor-grid. We prove that the costs of such a path in a scene with weighted regions can be arbitrarily high in the general case. If all regions are aligned with the grid, we prove that the costs are at most 4 + 4 − 2 √ 2 times the costs of an optimal path. In addition, we present a new hybrid method called Vertex-based Pruning (VBP). VBP computes paths that are -optimal inside a pruned subset of the scene. Experiments show that VBP paths can be computed at interactive rates, and are thus well-suited as an input for advanced path-following strategies in robotics, crowd simulation or gaming applications.

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Computing high-quality paths in weighted regions

Tibboel²,

Proceedings of the Seventh International Conference on Motion in Games

2014

Self Cite

“…The Modified Indicative Routes and Navigation (MIRAN) method adopts from the IRM the concept of using an indicative route and computing an attraction point in each step of the simulation (Jaklin, Cook IV, & Geraerts, 2013). It computes several candidate attraction points in each step of the simulation.…”

Section: The Modified Indicative Routes and Navigation Methodsmentioning

confidence: 99%

Navigating Through Virtual Worlds

Advances in Game-Based Learning

2016

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With the rise and success of digital games over the past few decades, path planning algorithms have become an important aspect in modern game development for all types of genres. Indirectly-controlled playable characters as well as non-player characters have to find their way through the game's environment to reach their goal destinations. Modern gaming hardware and new algorithms enable the simulation of large crowds with thousands of individual characters. Still, the task of generating feasible and believable paths in a time- and storage-efficient way is a big challenge in this emerging and exciting research field. In this chapter, the authors describe classical algorithms and data structures, as well as recent approaches that enable the simulation of new and immersive features related to path planning and crowd simulation in modern games. The authors discuss the pros and cons of such algorithms, give an overview of current research questions and show why graph-based methods will soon be replaced by novel approaches that work on a surface-based representation of the environment.

“…One option is to define a reference point on the global path for each agent, e.g. the point on the global path that is closest to an agent's position [Jaklin et al 2013]. …”

Section: Leader and Last Membermentioning

confidence: 99%

Adding sociality to virtual pedestrian groups

Kremyzas

Proceedings of the 21st ACM Symposium on Virtual Reality Software and Technology

2015

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We present a new method, Social Groups and Navigation (SGN), to simulate the walking behavior of small pedestrian groups in virtual environments. SGN is the first method to simulate group behavior on both global and local levels. We define quantitative metrics to measure the coherence and the sociality of a group based on existing empirical data of real crowds. SGN is designed to let groups stay in coherent and social formations without explicitly modeling such formations. For groups of four, SGN generates between 13% and 53% more socially-friendly behavior than existing methods, measured over the lifetime of a group in the simulation. For groups of three, the gain is between 15% and 31%, and for groups of two, the gain is between 1% and 4%. SGN can be used with any existing global path planner and any existing path following method. Experiments show that SGN enables the simulation of thousands of agents in real time. Due to its flexible design, it can be easily integrated into a larger crowd simulation framework.