Navigation Algorithm

Navigation algorithms aim to enable autonomous agents, from robots to spacecraft, to efficiently and safely reach their destinations, often in complex and dynamic environments. Current research emphasizes robust solutions for mapless navigation, leveraging techniques like diffusion models, conditional variational autoencoders, and reinforcement learning, often incorporating visual data processing and semantic understanding through deep neural networks. This field is crucial for advancing robotics, autonomous vehicles, and space exploration, with recent work focusing on improving algorithm performance in crowded or unstructured settings and developing standardized evaluation metrics for social robot navigation.