Vision-aided online A\(^*\) path planning for efficient and safe navigation of service robots

The deployment of autonomous service robots in human-centric environments is hindered by a critical gap in perception and planning. Traditional navigation systems rely on expensive LiDARs that, while geometrically precise, are semantically unaware, they cannot distinguish an important document on an office floor from a harmless piece of litter, treating both as physically traversable. While advanced semantic segmentation methods exist, their integration into real-time path planning systems that remain efficient on low-cost embedded hardware is still largely unexplored. This paper presents a framework to bridge this gap, delivering context-aware navigation on an affordable robotic platform. Our approach centers on a tight integration of an efficient, accurate, and real-time RGB-D perception model with an online A* planner. The perception system employs a semantic segmentation model to identify user-defined visual constraints, enabling the robot to navigate based on contextual importance rather than physical size alone. This adaptability allows an operator to define what is critical for a given task, be it sensitive papers in an office or safety lines in a factory, thus resolving the ambiguity of what to avoid. This semantic perception is seamlessly fused with geometric data. The identified visual constraints are projected as non-geometric obstacles onto a global map that is continuously updated from sensor data, enabling robust navigation through both partially known and unknown environments. This process creates a unified map of both physical and semantic objects, upon which the online A* search algorithm computes optimal, collision-free paths that intelligently respect this comprehensive world representation. We validate our framework through extensive experiments in high-fidelity simulations and on a real-world robotic platform. The results demonstrate robust, real-time performance, proving that a cost-effective robot can safely navigate complex environments while respecting critical visual cues invisible to traditional planners. Our work provides a practical and scalable solution for deploying context-aware service robots in real-world environments. Our source code and dataset are publicly accessible at https://github.com/PraveenSingh24/VisionAided.