Open Motion Planning Library (OMPL)

OMPL's core is its extensive collection of state-of-the-art sampling-based motion planners. These algorithms probabilistically explore the configuration space to find feasible paths without explicitly constructing its geometry. Key included planners are:

• Rapidly-exploring Random Trees (RRT, RRT, RRT-Connect)*
• Probabilistic Roadmaps (PRM, PRM)*
• Expansive Space Trees (EST)
• Single-Query Bi-Directional Lazy (SBL)
• Kinodynamic planners like RRT and PDST. This suite allows developers to benchmark and select the optimal planner for a given problem's dimensionality, constraints, and required solution quality (e.g., optimal vs. feasible).

OMPL uses a highly abstract, templated architecture that cleanly separates the planning algorithm from the geometric representation of the robot. The ompl::base::StateSpace class defines the robot's configuration space (e.g., SE(2) for a mobile robot, SO(3) for rotation, RealVectorStateSpace for joints). Planners operate generically on these state spaces. This design enables:

• Code Reusability: The same RRT implementation plans for a 2D drone or a 7-DOF arm.
• Custom State Spaces: Engineers can define novel spaces (e.g., for soft robots).
• Simplified Integration: New planners can be developed and tested without detailed geometric knowledge.

Beyond finding any feasible path, OMPL provides frameworks for optimizing trajectories. The ompl::base::OptimizationObjective class allows planners like RRT* and PRM* to asymptotically converge towards optimal paths. Common objectives include:

• Path Length Minimization (shortest path).
• Mechanical Work or Energy minimization.
• Maximizing Clearance from obstacles.
• Minimizing Control Effort or Jerk. Users can define custom, multi-objective cost functions. The library also includes tools for path simplification (shortcutting) and smoothing post-processing to refine planner output.

Many real-world robots are subject to kinematic or dynamic constraints. OMPL provides a sophisticated framework for planning under constraints, such as nonholonomic constraints (e.g., car-like vehicles) or closure constraints (for parallel manipulators). Key components:

• Constraint Types: Enforced via ompl::base::Constraint for equality (g(x)=0) or inequality (h(x)≤0) constraints.
• Projection-Based Planning: Uses tangent-space sampling to generate states that inherently satisfy constraints.
• Atlas-Based Planning: For complex constraint manifolds, it builds a local chart-based representation (Atlas). This allows OMPL to solve problems for underactuated systems and robots with closed kinematic chains.

OMPL includes ompl::tools::Benchmark and ompl::tools::Experience classes to facilitate rigorous, reproducible evaluation of planning algorithms.

• Automated Benchmarking: Run multiple planners on a set of problems, collecting metrics like runtime, path length, memory usage, and success rate.
• Data Logging & Visualization: Output results to console or SQLite databases, with scripts to generate comparative plots.
• Experience-Based Planning: Cache previous planning experiences (ompl::tools::Experience) to warm-start and dramatically accelerate future solves in similar environments. This feature is essential for research, algorithm development, and selecting the best planner for a production system.

This is the core algorithmic paradigm of OMPL. Instead of explicitly modeling the entire configuration space, these planners probe the free space with random samples.

• Key Idea: Construct a graph or tree of collision-free samples to find a feasible, though not necessarily optimal, path.
• OMPL Implementations: Includes Rapidly-exploring Random Trees (RRT), Probabilistic Roadmaps (PRM), and their many variants (RRT*, PRM*).
• Advantage: Highly effective for high-dimensional spaces (like multi-joint arms) where geometric modeling is intractable.

The fundamental mathematical representation for motion planning. A robot's C-Space is the set of all its possible configurations, where each point represents a unique pose.

• Transformation: Physical obstacles are mapped into forbidden regions (C-obstacles) within this space.
• Dimensionality: A simple mobile robot's C-space might be 2D (x, y), while a 7-DOF arm's is 7-dimensional.
• OMPL's Role: OMPL's StateSpace classes (e.g., RealVectorStateSpace, SO2StateSpace) provide the abstraction for working within and sampling from these complex spaces.

The search for not just a feasible path, but the best path according to a defined cost function (e.g., path length, time, energy).

• Asymptotic Optimality: Property of planners like RRT* and PRM* that guarantee convergence to an optimal solution as sampling time increases.
• OMPL's Optimizers: OMPL provides optimizing planners and tools like OptimizationObjective to define cost functions (e.g., PathLengthOptimizationObjective).
• Relation to Trajectory Optimization: Often, a sampling-based planner finds a rough, collision-free path, which is then smoothed and optimized by a downstream trajectory optimizer.

The fundamental query at the heart of every OMPL planner: "Is this robot configuration in collision?"

• OMPL Abstraction: Planners delegate this critical task to a user-defined StateValidityChecker function.
• Implementation: This function typically interfaces with a dedicated collision detection library (like FCL or Bullet) that operates on the robot's geometric meshes/primitive shapes and the environment model.
• Performance: This is often the computational bottleneck; efficient collision checking is essential for real-time planning.

Planning that considers the kinematic and dynamic constraints of the robot, not just geometric collisions. This includes limits on velocity, acceleration, and nonholonomic constraints (e.g., a car can't move sideways).

• Beyond Geometry: The planner must find paths that are dynamically feasible.
• OMPL Support: OMPL provides control-based planners (like SST, KPIECE) that use a ControlSpace to define the system's dynamics. The planner reasons over state and control inputs.