A real-time Hybrid A* path planner for nonholonomic autonomous vehicles, generating smooth, efficient paths in unstructured environments.
Hybrid A* Path Planner is a real-time motion planning algorithm for nonholonomic autonomous vehicles. It generates smooth, kinematically feasible paths in unstructured environments like parking lots by combining A* search in continuous space with discretized headings. The implementation was specifically developed for the KTH Research Concept Vehicle to navigate around obstacles using LIDAR-generated maps.
Autonomous vehicle researchers, robotics engineers, and students working on motion planning for nonholonomic systems, particularly those using ROS and requiring real-time path generation in unstructured environments.
Developers choose this implementation for its proven real-time performance (~10 Hz), deterministic path generation, and seamless ROS integration. It provides a production-ready Hybrid A* variant optimized for autonomous driving scenarios with comprehensive visualization tools.
Hybrid A* Path Planner for the KTH Research Concept Vehicle
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Achieves planning rates of approximately 10 Hz, as stated in the README, making it suitable for dynamic autonomous driving applications where low latency is critical.
Uses continuous sampling with 72 discrete headings per cell and Dubin's shot to generate smooth trajectories that respect nonholonomic constraints, essential for vehicle navigation.
Combines constrained and unconstrained heuristics to balance efficiency and feasibility in path search, improving convergence and optimality based on the algorithm description.
Built with ROS for modularity and includes RViz for visualization, as detailed in the setup, allowing for easy simulation and debugging in autonomous systems.
Requires a full ROS environment and dependencies like OMPL, which can be cumbersome for non-ROS projects or teams unfamiliar with robotic middleware.
Relies on binary obstacle maps from LIDAR without built-in support for real-time updates, limiting effectiveness in scenarios with rapidly changing obstacles.
Based on a 2016 master's thesis, the implementation may lack recent advancements in path planning and community maintenance, potentially requiring manual fixes for modern systems.