Path planning for indoor robots using natural language processing a chunk-based approach with cascaded conditional random fields

This paper presents a novel method for indoor robot navigation through natural language path description. The method focuses on extracting path information directly from natural language inputs to guide robots, without relying on pre-built high-precision maps. A chunk-based analysis technique is employed to enhance the robot’s comprehension of natural language by constructing cascaded conditional random fields (CCRFs). Initially, a detailed analysis of a corpus of path descriptions is conducted to uncover the relationship between syntax and semantics. Using this, noun phrases and semantic chunks are extracted to build path units. To address issues related to noun phrase interpretation, a reasoning method for noun entity relations is introduced, which incrementally constructs complete route information. The method’s performance is evaluated through several experiments, demonstrating its potential to enhance human-robot interaction in navigation tasks. The significance of this research lies in advancing natural language-guided robot navigation, making robots more autonomous and capable of harmoniously interacting in human environments.