How do I integrate steering_functions with OMPL for sampling-based planning?

The library provides distance and interpolate functions required by OMPL; you'll need to create a custom state space as outlined in the documentation, linking it to your OMPL planner for nonholonomic constraints.

Steering_functions vs OMPL's built-in Dubins or Reeds-Shepp: which is better?

Steering_functions offers more variants like continuous curvature paths and belief propagation, but OMPL's implementations might be more optimized for basic use. Choose this library if you need smoother paths or uncertainty handling beyond OMPL's defaults.

What's the practical difference between Dubins and Reeds-Shepp paths?

Dubins paths are for forward-only or backward-only driving, while Reeds-Shepp allows mixing forward and backward segments, making it better for tight parking maneuvers where reversing is needed.

How to build steering_functions without ROS dependencies?

Use CMake with -DBUILD_WITH_ROS=OFF and install Eigen separately. This creates a standalone library, but you'll miss ROS-specific features like RViz visualization.

Is steering_functions fast enough for real-time robot navigation?

It depends: Dubins paths are very fast (~1.3 µs), but continuous curvature variants like HC-Reeds-Shepp are slower. For real-time, benchmark your chosen algorithm on your hardware and consider path length trade-offs.

How do I handle arbitrary start and goal curvatures in motion planning?

Use the CC-Dubins or HC-Reeds-Shepp variants that accept user-specified curvatures, enabling curvature continuity when connecting paths in sampling-based planners, as noted in the README's state input section.

steering_functions — Car-Like Robot Steering Library

What is steering_functions?

Steering Functions is a C++ library that implements path planning algorithms for car-like robots with limited turning radius. It provides both classic steering functions like Dubins and Reeds-Shepp paths, along with continuous curvature variants that produce smoother trajectories. The library solves the problem of generating feasible paths for vehicles that cannot turn instantaneously, which is essential for autonomous navigation in constrained environments.

Target Audience

Robotics researchers and engineers working on motion planning for car-like robots, autonomous vehicles, or mobile robots with nonholonomic constraints. It's particularly useful for those integrating path planning with ROS or OMPL-based systems.

Value Proposition

Developers choose this library because it offers a comprehensive collection of steering functions with different continuity properties in a single, well-documented package. Unlike implementing these algorithms from scratch, it provides tested implementations with ROS integration and support for uncertainty propagation, saving significant development time for robotic motion planning projects.

Overview

This C++ library provides steering functions for car-like robots with limited turning radius, enabling motion planning for vehicles that cannot turn on the spot. It implements both classic algorithms like Dubins and Reeds-Shepp paths, along with newer continuous curvature (CC) and hybrid curvature (HC) variants that produce smoother, more realistic trajectories.

Key Features

Dubins Paths — G¹ continuous, length-optimal paths for forward or backward driving.
Reeds-Shepp Paths — G¹ continuous, length-optimal paths allowing both forward and backward driving.
Continuous Curvature Variants — G² continuous paths (CC-Dubins, CC-Reeds-Shepp) for smoother motion.
Hybrid Curvature Variants — G² continuous between cusps (HC-Reeds-Shepp) balancing smoothness and length.
Flexible State Input — Supports arbitrary start and goal curvatures for integration with sampling-based planners.
Belief Propagation — Computes Gaussian belief along paths for uncertainty-aware planning.
ROS Integration — Includes RViz visualization and works with ROS 1/2 and OMPL.

Philosophy

The library focuses on providing both optimal and suboptimal steering functions with varying continuity constraints, enabling researchers and developers to choose the right trade-off between path length, smoothness, and computational cost for their specific robotic applications.

steering_functions

What is steering_functions?

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