Autonomous Robotic Exploration Using Semantic Environment Mapping

Abstract

Autonomous exploration frameworks play a vital role in robotic deployments across search-and-rescue domains, hazardous industrial monitoring, and next-generation planetary exploration. Classic robotic exploration methodologies rely almost exclusively on geometric spatial layouts, prioritizing the extraction of unmapped frontiers based entirely on distance calculations and basic volumetric vacancy metrics. Unfortunately, ignoring high-level contextual information often causes robots to make inefficient backtracking choices and struggle to interpret complex, cluttered human settings. This paper introduces an intelligent, context-aware robotic exploration framework that integrates real-time deep semantic segmentation directly into a dynamic, multi-layer occupancy grid map. By assigning contextual cost weights to distinct environmental elements (such as structural pathways, debris fields, and static obstacles), the proposed routing agent intelligently guides exploration trajectories based on logical spatial predictions. Computational trials carried out across complex indoor digital twins reveal that the semantic exploration framework reduces overall exploration cycle timelines by 22.6% and drops structural localization errors by 18.4% compared to standard geometric frontiers. These results emphasize the immense value of using context-aware spatial understanding to drive true operational efficiency in advanced autonomous logistics and robotic engineering.