Analyzing and Comparing Shot Planning Strategies and Their Effects on the Performance of an Augment Reality Based Billiard Training System

Abstract

The shot planning of a cue after it collides with an object ball determines a player's success in a billiard game. This paper proposes three novel gaming strategies to investigate the effect of cue shots planning on gaming performance. The first algorithm considers the nearest pocket for every selected target object ball, seeking optimal post collision positions. The second algorithm considers all pocket and target object ball combinations during both the pre- and post-collision optimal shot selection processes. The third algorithm considers a multi-objective optimization process for optimal shot planning control. The simulations are conducted based on a collision model considering the restitution effects. An augmented reality training facility is devised to guide users in both aiming and cue repositioning control in a real-world billiard game. Experimental results not only prove the reliability of our training device in selecting a proper shot sequence using the all-pocket optimal shot planning algorithm, but it also proves the consistency with the restitution theory.