Collision detection: review of methods and recent advances in crowd simulation

Crowd simulation is a large complex system that visualizes the behavior of crowd entities' movement and their interactions with the virtual environment. Crowd model is usually integrated into a virtual environment to make the environment alive. In the context of agent-based simulation (as in crowd simulation), it encompasses collision checking between moving agents that are present in the same environment. Hence, it is important to design an efficient and yet effective collision detection in crowd simulation. This is to ensure that it is cost effective toward computational processing usage and still produce a believable behavior. This paper presents a study of collision detection techniques in crowd models, and recent advancement to accelerate the process so that in turn, these efforts could also improve the performance and outcome of crowd model in virtual environment applications

Parallelizing Broad Phase Collision Detection Algorithms for Sampling Based Path Planners

Collision checking takes most of the time in sampling based path planning algorithms. When the scene gets crowded, more samples are needed and the probability decreases to find a collision free sample. Broad phase algorithms are designed to eliminate obviously collision free samples, so narrow phase algorithms can concentrate on fewer samples suspected to be in collision. In this study, we compare the performance of two broad phase algorithms implemented on both CPU and GPU. A novel technique is proposed to provide load balancing and efficient cache utilization on Bounding Sphere Collision Detection algorithm. Furthermore, Thrust library is extensively utilized on Sweep and Prune (SAP) algorithm. Our experimental results indicate speedups up to 103 times faster for GPU-based SAP algorithm and 134 times faster for GPU-based Bounding Sphere algorithm, compared to CPU implementations. This may allow using sampling based path planning algorithms for scenes with many robots