Pure pursuit controller and Reeds-Shepp sampling-based planner for car-like vehicle navigation in SE(2) space.
se2_navigation is an open-source robotics package that provides pure pursuit control and Reed-Shepp sampling-based planning for car-like vehicles operating in SE(2) space. It solves the problem of precise navigation and path following for non-holonomic systems, enabling slow maneuvers like parking and precision harvesting. The package separates core algorithms from ROS dependencies and has been tested on real hardware.
Robotics engineers and researchers working on autonomous car-like vehicles, mobile robots, or precision navigation systems, particularly those using ROS and needing non-holonomic motion planning.
Developers choose se2_navigation for its integrated planning and control tailored to non-holonomic vehicles, real-hardware validation, minimal dependencies, and flexibility through modular design with ROS integration.
Pure Pursuit Control and SE(2) Planning
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Demonstrated on actual vehicles like Toyota Prius and Menzi Muck M545, with GIFs showing successful navigation in real scenarios, ensuring reliability for practical applications.
Core algorithms are separated from ROS-dependent code, as highlighted in the README, allowing for easy customization and integration into different systems without heavy dependencies.
Combines Reeds-Shepp path planning with pure pursuit control, specifically handling forward and reverse driving for car-like vehicles, enabling precise maneuvers like parking.
Includes RViz planning interface and dynamic reconfigure for real-time tuning, enhancing development and debugging in ROS environments, as noted in the features list.
Explicitly warned as unsuitable for high-speed driving in the README, restricting use to slow maneuvers like parking or precision harvesting, which limits broader autonomous applications.
Relies on sampling-based geometric planners like Reeds-Shepp, which may not efficiently handle real-time obstacle avoidance or complex, cluttered paths without additional adaptations.
Full functionality requires ROS integration, and documentation is split across multiple READMEs, adding overhead and a learning curve for teams not already using the ROS ecosystem.