Collective Intelligence of Autonomous Animals in VR Hunting

Kangqiao Zhao, Feng Lin, Hock Soon Seah

Abstract: In the scenario of a VR hunting game, autonomous behaviour of in-game animals is essential. In this study, new adaptive steering algorithms are designed for autonomous animals to navigate around the environment, with a research focus on collective intelligence in decision making and tactical actions. Advanced strategies for a group of autonomous animals are developed in order to simulate a more realistic forest environment. Computational experiments and comparisons with animation results are presented, accompanied by a demo video, which show significant advantages over previous work. The new models and algorithms can also be used for autonomous motion controls for other XR-based training.

External IDs:dblp:conf/vr/Zhao0S21