@Article{cmc.2024.055538,
AUTHOR = {Baowei Wang, Wen You},
TITLE = {Virtual Assembly Collision Detection Algorithm Using Backpropagation Neural Network},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {81},
YEAR = {2024},
NUMBER = {1},
PAGES = {1085--1100},
URL = {http://www.techscience.com/cmc/v81n1/58342},
ISSN = {1546-2226},
ABSTRACT = {As computer graphics technology continues to advance, Collision Detection (CD) has emerged as a critical element in fields such as virtual reality, computer graphics, and interactive simulations. CD is indispensable for ensuring the fidelity of physical interactions and the realism of virtual environments, particularly within complex scenarios like virtual assembly, where both high precision and real-time responsiveness are imperative. Despite ongoing developments, current CD techniques often fall short in meeting these stringent requirements, resulting in inefficiencies and inaccuracies that impede the overall performance of virtual assembly systems. To address these limitations, this study introduces a novel algorithm that leverages the capabilities of a Backpropagation Neural Network (BPNN) to optimize the structural composition of the Hybrid Bounding Volume Tree (HBVT). Through this optimization, the research proposes a refined Hybrid Hierarchical Bounding Box (HHBB) framework, which is specifically designed to enhance the computational efficiency and precision of CD processes. The HHBB framework strategically reduces the complexity of collision detection computations, thereby enabling more rapid and accurate responses to collision events. Extensive experimental validation within virtual assembly environments reveals that the proposed algorithm markedly improves the performance of CD, particularly in handling complex models. The optimized HBVT architecture not only accelerates the speed of collision detection but also significantly diminishes error rates, presenting a robust and scalable solution for real-time applications in intricate virtual systems. These findings suggest that the proposed approach offers a substantial advancement in CD technology, with broad implications for its application in virtual reality, computer graphics, and related fields.},
DOI = {10.32604/cmc.2024.055538}
}