Semantic-Enhanced Path Planning for Safety-Centric Indoor Robots Navigation

This paper presents an innovative approach for indoor mobile robot navigation, focusing on environments like Ambient Assisted Living spaces (AAL), smart homes, and factories, where humans and robots coexist. The need for effective space and traffic management in these areas is critical for ensuring...

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Bibliographic Details
Published in2024 10th International Conference on Automation, Robotics and Applications (ICARA) pp. 185 - 190
Main Authors Omer, Karameldeen, Torta, Elena, Monteriu, Andrea
Format Conference Proceeding
LanguageEnglish
Published IEEE 22.02.2024
Subjects
Autonomous Robotics
Context-Aware Navigation
Digital twins
Human-Centered Design
Location awareness
Navigation
Path planning
Robot sensing systems
Safety
Safety-Centric Navigation
Semantic Mobile Robot Navigation
Semantic Path Planning
Semantics
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Summary:This paper presents an innovative approach for indoor mobile robot navigation, focusing on environments like Ambient Assisted Living spaces (AAL), smart homes, and factories, where humans and robots coexist. The need for effective space and traffic management in these areas is critical for ensuring safety and mobility. Our novel Semantic Path Planning algorithm improves upon traditional grid mapping by using a multi-dimensional array in the map file, where varying intensities indicate occupancy levels. The planner imposes penalties on free cells near specific static objects based on user preferences, facilitating autonomous robot movement in designated areas without creating rigid barriers. A standout feature of our algorithm is its ability to prioritize specific zones frequented by vulnerable groups like the elderly, or areas designated for rest, pets, or children's play. By enhancing the A* path planner with semantic and geometric data, our approach enables the management of these zones, leading to a comprehensive grid map for optimal path-finding. Moreover, this methodology is promising for multi-robot systems with differing navigation abilities and access rights. It also improves robot localization by focusing on unique environmental landmarks, thereby enhancing tracking accuracy and aiding in swift localization recovery. This approach is ideal for safe, efficient, and context-aware navigation in dynamic shared human-robot environments.
ISSN:2767-7745
DOI:10.1109/ICARA60736.2024.10553077