Safe and Trustworthy Robot Pathfinding with BIM, MHA*, and NLP

TL;DR

This paper presents a novel approach for construction robot pathfinding that combines BIM-based spatial and semantic data, MHA* algorithm, and NLP techniques to improve obstacle avoidance in complex, dynamic environments.

Contribution

It introduces an integrated method using BIM, MHA*, and LLMs for domain-specific, efficient, and adaptable robot pathfinding in construction sites.

Findings

80% increase in robot object proximity

Maintained similar path lengths

Effective dynamic object avoidance

Abstract

Construction robots have gained significant traction in recent years in research and development. However, the application of industrial robots has unique challenges. Dynamic environments, domain-specific tasks, and complex localization and mapping are significant obstacles in their development. In construction job sites, moving objects and complex machinery can make pathfinding a difficult task due to the possibility of object collisions. Existing methods such as simultaneous localization and mapping are viable solutions to this problem, however, due to the precision and data quality required by the sensors and the processing of the information, they can be very computationally expensive. We propose using spatial and semantic information in building information modeling (BIM) to develop domain-specific pathfinding strategies. In this work, we integrate a multi-heuristic A* (MHA*) algorithm using APFs from the BIM spatial information and process textual information from the BIM using large language models (LLMs) to adjust the algorithm for dynamic object avoidance. We show increased robot object proximity by 80% while maintaining similar path lengths.