Open Access

ARTICLE

Virtual Assembly Collision Detection Algorithm Using Backpropagation Neural Network

Baowei Wang^1,2,*, Wen You²

1 School of Computer, Nanjing University of Information Science and Technology, Collaborative Innovation Center of Jiangsu Atmospheric Environment and Equipment Technology, Digital Forensics Engineering Research Center of Digital Forensics Ministry of Education, Nanjing, 210044, China
2 School of Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

* Corresponding Author: Baowei Wang. Email:

Computers, Materials & Continua 2024, 81(1), 1085-1100. https://doi.org/10.32604/cmc.2024.055538

Received 30 June 2024; Accepted 27 August 2024; Issue published 15 October 2024

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.

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Keywords

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