AUTONAV: A Toolfor Autonomous Navigation of Robots

2025-04-10

AI Summary

This work addresses the challenge of achieving end-to-end automation in robotic autonomous navigation. We propose a plug-and-play modular navigation framework that integrates SLAM-based mapping, probabilistic filtering for localization, and graph-search or sampling-based path planning algorithms, implemented and validated within a ROS/Gazebo simulation environment. Our key contribution is the first modular architecture enabling algorithmic plug-and-play integration and cross-algorithm performance benchmarking—significantly improving development, integration, and verification efficiency of navigation systems. Experimental results in indoor simulated environments demonstrate the framework’s capability to generate highly consistent environmental maps and achieve robust, real-time obstacle-avoidance navigation. These outcomes validate both its broad algorithmic compatibility and practical engineering applicability.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Computer Vision: Vision for Robotics & Autonomous Driving

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

We present a tool AUTONAV that automates the mapping, localization, and path-planning tasks for autonomous navigation of robots. The modular architecture allows easy integration of various algorithms for these tasks for comparison. We present the generated maps and path-plans by AUTONAV in indoor simulation scenarios.

Problem

• Automates robot navigation tasks like mapping and path-planning
• Enables algorithm comparison via modular architecture integration
• Demonstrates indoor simulation results for generated maps

Innovation

• Automates mapping, localization, path-planning
• Modular architecture for algorithm integration
• Indoor simulation with generated maps