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NavGround is an open-source 2D navigation framework designed to empower differential drive robots with real-time AI-based motion planning and collision avoidance. It leverages velocity obstacle algorithms, dynamic models, and sensor data to generate safe trajectories in dynamic environments. With lightweight C++ implementation and ROS integration, developers can quickly deploy reactive navigation capabilities on mobile platforms, ensuring smooth, efficient, and safe robotic operations.
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May 05 2025
Virtual & Augmented Reality
Image Generation
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AI Platforms & Frameworks
#AI navigation
#robot motion planning
#collision avoidance
#sensor fusion
#real-time navigation
#modular robotics
#dynamic mapping
#path planning
#autonomous robots
# ROS compatible
#trajectory generation
#robot dynamics
#data-driven navigation
#service robotics
#lightweight APIs
#open source robotics
#customizable navigation
#adaptive robots
#velocity obstacles
#research prototypes
...
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NavGround
NavGround is an open-source 2D navigation framework designed to empower differential drive robots with real-time AI-based motion planning and collision avoidance. It leverages velocity obstacle algorithms, dynamic models, and sensor data to generate safe trajectories in dynamic environments. With lightweight C++ implementation and ROS integration, developers can quickly deploy reactive navigation capabilities on mobile platforms, ensuring smooth, efficient, and safe robotic operations.
Added on:
Social & Email:
Platform:
Pricing:
May 05 2025
Virtual & Augmented Reality
Image Generation
Avatars & Personas
AI Platforms & Frameworks
#AI navigation
#robot motion planning
#collision avoidance
#sensor fusion
#real-time navigation
#modular robotics
#dynamic mapping
#path planning
#autonomous robots
# ROS compatible
#trajectory generation
#robot dynamics
#data-driven navigation
#service robotics
#lightweight APIs
#open source robotics
#customizable navigation
#adaptive robots
#velocity obstacles
#research prototypes
...
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What is NavGround?

NavGround is a comprehensive AI-driven navigation framework that delivers reactive motion planning, obstacle avoidance, and trajectory generation for differential drive and holonomic robots in 2D environments. It integrates dynamic map representations and sensor fusion to detect static and moving obstacles, applying velocity obstacle methods to compute collision-free velocities adhering to robot kinematics and dynamics. The lightweight C++ library offers a modular API with ROS support, enabling seamless integration with SLAM systems, path planners, and control loops. NavGround’s real-time performance and on-the-fly adaptability make it suitable for service robots, autonomous vehicles, and research prototypes operating in cluttered or dynamic scenarios. The framework’s customizable cost functions and extensible architecture facilitate rapid experimentation and optimization of navigation behaviors.

Who will use NavGround?

    
  • Robotics developers
  • 
  • Research institutions
  • 
  • Academic researchers
  • 
  • Robotics startups
  • 
  • Autonomous mobile platform integrators

How to use the NavGround?

    
  • Step1: Clone the NavGround repository from GitHub.
  • 
  • Step2: Build the C++ library using CMake.
  • 
  • Step3: Integrate NavGround into your ROS workspace and launch the navigation node.
  • 
  • Step4: Configure robot and environment parameters in the provided YAML files.
  • 
  • Step5: Feed sensor data (e.g., LiDAR, odometry) to the navigation node.
  • 
  • Step6: Monitor real-time trajectory outputs and adjust cost functions as needed.
  • 
  • Step7: Deploy on physical or simulated robots and validate navigation performance.

Platform

    
  • mac
  • 
  • windows
  • 
  • linux

NavGround's Core Features & Benefits

The Core Features

    
  • Reactive motion planning
  • 
  • Velocity obstacle-based collision avoidance
  • 
  • Dynamic obstacle handling
  • 
  • Sensor fusion integration
  • 
  • Modular C++ API
  • 
  • ROS integration
  • 
  • Customizable cost functions
  • 
  • Real-time trajectory generation

The Benefits

    
  • Fast real-time performance
  • 
  • Lightweight footprint
  • 
  • Adaptive to dynamic environments
  • 
  • Seamless ROS compatibility
  • 
  • Highly customizable and extensible
  • 
  • Open-source community support

NavGround's Main Use Cases & Applications

    
  • Warehouse service robot navigation
  • 
  • Autonomous delivery robot path planning
  • 
  • Robotic motion planning research
  • 
  • Dynamic obstacle avoidance in crowds
  • 
  • Automated guided vehicles in manufacturing

NavGround's Pros & Cons

The Pros

Open-source with active development and community support
Specialized for real-time multi-agent navigation and motion planning
Suitable for complex, dynamic environments, enhancing robot autonomy
Supports simulation and control which aids in research and practical deployments

The Cons

Primarily focused on robotics domain, may not suit non-robotic AI applications
Documentation might require prior knowledge of robotics and AI
No direct pricing or commercial support information available

NavGround's Pricing

Has free planNo
Free trial details
Pricing model
Is credit card requiredNo
Paid from
Has lifetime planNo
Billing frequency
For the latest prices, please visit: https://idsia-robotics.github.io/navground/

FAQs of NavGround

What is NavGround?
Which robots are supported by NavGround?
How do I install NavGround?
Is NavGround compatible with ROS?
Can NavGround avoid dynamic obstacles?
How customizable is the framework?
What sensor inputs does NavGround accept?
Is NavGround open-source?
Can I use NavGround in simulation?
Where can I get support or report issues?

NavGround Company Information

  • IDSIA Robotics
  • idsia-robotics
  • https://idsia-robotics.github.io

Analytic of NavGround

Visit Over Time

Monthly Visits
536
Avg Visit Duration
00:00:00
Page Per Visit
1.02
Bounce Rate
41.34%
Dec 2025 - Feb 2026 All Traffic

Geography

Top 2 Regions
Switzerland
90.63%
United States
9.37%
Dec 2025 - Feb 2026 Worldwide Desktop Only

Traffic Sources

Search
44.97%
Direct
30.93%
Referrals
19.68%
Social
3.38%
Paid Referrals
0.97%
Mail
0.07%
Dec 2025 - Feb 2026 Desktop Only

Top Keywords

KeywordTrafficCost Per Click
ricardo chavez garcia github io70 $ --
human-centered robot navigation—towards a harmoniously human–robot coexisting environment40 $ --
nanoflownet ai deck750 $ --
idsia430 $ --
dalle molle institute for artificial intelligence340 $ --

NavGround Reviews


5/5
Do You Recommend NavGround? Leave a Comment Below!


NavGround's Main Competitors and alternatives?

    
  • ROS Navigation Stack (move_base)
  • 
  • Navigation2 (Nav2)
  • 
  • TEB Local Planner
  • 
  • Open Motion Planning Library (OMPL)
  • 
  • Dynamic Window Approach (DWA)

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