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.
NavGround 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
NavGround Pro & Cons
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
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
