Efficient Social Navigation: Leveraging Discrete Morse Theory for Dynamic Agent Interaction

Abstract

Robotic navigation in dynamic environments presents significant challenges, particularly in managing interactions with moving agents while ensuring efficient path planning. We introduce a novel integration of social navigation strategies with topological path planning, leveraging Discrete Morse Theory, Vietoris-Rips complex, and a homotopical framework to enhance adaptability. Our method dynamically assesses path feasibility and optimizes trajectory selection through three key strategies: waiting, deflection, and diverse path selection. By incorporating Morse values into a sampling-based roadmap, our approach prioritizes critical configurations for efficient motion planning. Unlike existing methods that rely on static heuristics or extensive learning-based predictions, our framework offers a real-time, adaptive mechanism for congestion-aware navigation. Experimental evaluations demonstrate an efficient solution(< 100s in computation) with improved path adaptability, resulting in 30-60% increase in traversal time in environments containing 3-9 degrees of freedom robot and 15-60 dynamic agents.

Department(s)

Computer Science

Document Type

Conference Proceeding

DOI

10.1109/CASE58245.2025.11163972

Publication Date

1-1-2025

Journal Title

IEEE International Conference on Automation Science and Engineering

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