Open Motion Planning Library (OMPL)

OMPL implements a comprehensive suite of sampling-based algorithms, decoupled from the geometric and kinematic specifics of any particular robot. This allows engineers to benchmark and swap planners without rewriting application logic.

• Single-Query Planners: Rapidly-exploring Random Trees (RRT, RRT-Connect) and Expansive Space Trees (EST) for one-off path requests.
• Multi-Query Planners: Probabilistic Roadmaps (PRM, PRM*) that precompute a roadmap for repeated queries in static environments.
• Asymptotically Optimal Planners: RRT*, PRM*, Informed RRT*, and BIT* guarantee convergence to the shortest path given infinite time.

OMPL operates on an abstract notion of a state space, requiring only that users implement a minimal interface: state sampling, distance computation, and interpolation. This abstraction makes OMPL agnostic to robot morphology.

• A state can represent any configuration: joint angles for a 7-DOF manipulator, SE(3) poses for a drone, or compound spaces for mobile manipulators.
• The library provides built-in state spaces for Rn, SO(2), SO(3), SE(2), and SE(3), along with their Cartesian products.
• Custom state validity checkers define collision-free regions, enabling integration with any collision detection engine.

OMPL natively supports planning on lower-dimensional manifolds defined by equality constraints, a critical capability for manipulation tasks where the end-effector must maintain orientation or stay on a surface.

• Constraint Types: Position, orientation, and full pose constraints are defined as mathematical functions projecting states onto the constraint manifold.
• Atlas and Tangent Bundle: OMPL uses these methods to construct local charts on the constraint manifold, enabling sampling-based planners to operate effectively in the constrained subspace.
• This enables tasks like keeping a grasped object level or sliding a tool along a curved workpiece surface.

Beyond finding any feasible path, OMPL supports multi-criteria optimization through a flexible OptimizationObjective framework. Planners like RRT* and PRM* minimize user-defined cost functions.

• Path Length: Euclidean or weighted distance in configuration space.
• Minimum Clearance: Maximizing distance from obstacles for safer execution.
• Mechanical Work: Minimizing joint effort or energy consumption.
• Compound Objectives: Weighted combinations of multiple criteria, enabling trade-off analysis between path length and obstacle clearance.

OMPL includes a built-in benchmarking framework (ompl_benchmark) for rigorous, repeatable comparison of planner performance. This is essential for selecting the right algorithm for a specific industrial application.

• Metrics Collected: Planning time, path length, success rate, number of states sampled, and solution smoothness.
• Statistical Rigor: Runs multiple trials with configurable time limits and generates aggregate statistics.
• Output Formats: Results are exportable to SQLite databases and visualized with companion scripts, enabling data-driven planner selection for production systems.

OMPL is the default planning backend for MoveIt, the dominant open-source manipulation framework in ROS. This integration abstracts OMPL's complexity behind a ROS service interface.

• MoveIt's Planning Pipeline translates motion requests into OMPL-compatible state spaces, validity checks, and objectives.
• The OMPL Interface plugin handles adapter logic, allowing MoveIt users to configure and invoke any OMPL planner via YAML parameters.
• This architecture enables rapid prototyping: configure a PRM* planner for bin-picking in simulation, then switch to RRT-Connect for faster replanning on hardware without code changes.

The mathematical foundation upon which OMPL operates. C-Space is an N-dimensional vector space where each axis represents a robot joint. OMPL's samplers explore this space, where the robot is abstracted to a single point. The critical challenge is distinguishing C-Free (collision-free configurations) from C-Obstacle (configurations causing collisions).

The foundational single-query algorithm implemented in OMPL. RRT grows a tree from the start state by randomly sampling C-Space and extending toward samples. Key variants in OMPL include:

• RRT*: Asymptotically optimal, rewires the tree for path shortening
• RRT-Connect: Grows two trees from start and goal for faster connection
• Informed RRT*: Restricts sampling to an ellipsoid after initial solution

A multi-query planner in OMPL designed for static environments. PRM operates in two phases:

• Construction Phase: Builds a roadmap graph by sampling collision-free configurations and connecting them with local planners
• Query Phase: Answers multiple start-goal pairs by searching the precomputed graph using A* or Dijkstra's algorithm Ideal for applications where the environment remains static but goals change frequently.

OMPL is collision-checker agnostic and relies on external libraries for geometric validation. Common backends include:

• FCL (Flexible Collision Library): Default in MoveIt, provides broad-phase and narrow-phase checks
• PQP (Proximity Query Package): Legacy option for triangular mesh collision
• Bullet: Physics engine with integrated collision detection OMPL queries these backends millions of times per planning session, making their speed critical.

Extends OMPL's geometric planners to respect differential constraints like velocity and acceleration limits. Standard RRT assumes holonomic motion; RRT with controls propagates random control inputs through a state space representation. This bridges the gap between geometric paths and dynamically feasible trajectories required for real robots with momentum.