Path planning is a fundamental component in robotics and game artificial intelligence that considerably influences the motion efficiency of robots and unmanned aerial vehicles, as well as the realism and immersion of virtual environments. However, traditional algorithms are often limited to single-objective optimization and lack real-time adaptability to dynamic environments. This study addresses these limitations through a proposed real-time dynamic multiobjective (RDMO) path-planning algorithm based on an enhanced A* framework. The proposed algorithm employs a queue-based structure and composite multiheuristic functions to dynamically manage game tasks and compute optimal paths under changing-map-connectivity conditions in real time. Simulation experiments are conducted using real-world road network data and benchmarked against mainstream hybrid approaches based on genetic algorithms (GAs) and simulated annealing (SA). The results show that the computational speed of the RDMO algorithm is 88 and 73 times faster than that of the GA- and SA-based solutions, respectively, while the total planned path length is reduced by 58% and 33%, respectively. In addition, the RDMO algorithm also shows excellent responsiveness to dynamic changes in map connectivity and can achieve real-time replanning with a minimal computational overhead. The research results prove that the RDMO algorithm provides a robust and efficient solution for multiobjective path planning in games and robotics applications and has a great application potential in improving system performance and user experience in related fields in the future.

The rapid evolution of virtual reality (VR) and augmented reality (AR) technologies has significantly transformed human-computer interaction, with applications spanning entertainment, education, healthcare, industry, and remote collaboration. A central challenge in these immersive systems lies in enabling intuitive, efficient, and natural interactions. Hand gesture recognition offers a compelling solution by leveraging the expressiveness of human hands to facilitate seamless control without relying on traditional input devices such as controllers or keyboards, which can limit immersion. However, achieving robust gesture recognition requires overcoming challenges related to accurate hand tracking, complex environmental conditions, and minimizing system latency. This study proposes an artificial intelligence (AI)-driven framework for recognizing both static and dynamic hand gestures in VR and AR environments using skeleton-based tracking compliant with the OpenXR standard. Our approach employs a lightweight neural network architecture capable of real-time classification within approximately 1.3 ms while maintaining average accuracy of 95%. We also introduce a novel dataset generation method to support training robust models and demonstrate consistent classification of diverse gestures across widespread commercial VR devices. This work represents one of the first studies to implement and validate dynamic hand gesture recognition in real time using standardized VR hardware, laying the groundwork for more immersive, accessible, and user-friendly interaction systems. By advancing AI-driven gesture interfaces, this research has the potential to broaden the adoption of VR and AR across diverse domains and enhance the overall user experience.