REDUCING LATENCY IN A VIRTUAL REALITY-BASED TRAINING APPLICATION - P ISKANDAR, YULITA HANUM (2006)
Overall latency is the elapsed time from input of human motion to the immediate response of the input in the display. Apparently, latency is one of the most frequently cited shortcomings of current Virtual Reality (VR) applications. To compensate latency, previous prediction mechanisms insert a complex mathematical algorithm, which may not be appropriate for complex virtual training applications. More complex VR simulations most likely will impose greater computation burdens and resulted in the increase of latencies. In order to overcome latency problem, this research is an attempt to suggest a new prediction algorithm based on heuristic that could be used to develop a more effective and general system for virtual training applications. The heuristic-based predictor provides a platform to utilize the heuristic power of human along with the algorithmic power, geometry accuracy of motion-planning programs and biomechanical laws of human. Heuristic algorithm is an important module widely used for humanoid robots and avatars in VR systems. However, to the best of the researcher's knowledge, the heuristic approach has not been used as a single prediction algorithm for compensating latency in virtual training systems. In order to find out whether the new prediction algorithm is acceptable and possibly could reduce latency, a fast synchronization squash-game simulation was selected as a study source. This research analyzed the latencies of all subcomponents of this system and designed prediction algorithm that allows high-speed interaction. In measuring the performance on various prediction methods, this research also makes a comparison in real tasks among 1) the heuristic-based prediction, 2) the Grey system prediction and 3) the one without prediction using different sample rates. Findings indicated that heuristic-based algorithm is an accurate prediction method to compensate latency in virtual training. Apparently, heuristic-based prediction and Grey system prediction are significantly better than the one without prediction. When heuristic-based prediction and Grey system prediction were compared, heuristic-based prediction was in fact a better predictor. Overall findings indicated that heuristicbased prediction is efficient, robust and easier to implement.