Year: 2025
Type: article
Abstract: Domestic robots frequently fail to navigate and adapt in complex home environments, leading to reduced reliability and increased user intervention. We introduce an integrated spatial computing architecture that combines multi-modal sensor fusion with semantic mapping to achieve a 94% success rate in dynamic obstacle avoidance and 95% coverage accuracy in household navigation. Our system demonstrates breakthrough capabilities in real-time environmental adaptation through a novel three-tier processing pipeline: enhanced sensor fusion that maintains position accuracy within 1.8 centimeters, semantic-aware mapping that reduces computational overhead by 76%, and context-driven navigation that decreases user intervention requirements by 78%. The implementation results show sustained performance across varying lighting conditions, with response times averaging 85 milliseconds and operational reliability reaching 99.2% under normal household conditions. These advances in spatial computing capabilities enable autonomous robots to operate more effectively in dynamic domestic settings while maintaining robust performance across diverse environmental challenges.
Author: Prashant Anand Srivastava
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411093718