Open Motion Planning Library (OMPL)

• OMPL is a modular C++ framework for sampling-based motion planning in robotics, supporting diverse state spaces and flexible problem definitions.
• The library features an extensible benchmarking infrastructure that logs performance metrics, supports concurrent planner evaluations, and enables reproducible comparisons through visualization tools.
• OMPL’s design promotes interoperability with third-party libraries, ensuring standardized evaluation and rapid integration of new planning methods in both research and industry.

The Open Motion Planning Library (OMPL) is a software framework designed for the development, integration, and benchmarking of sampling-based motion planning algorithms. Written in C++, OMPL serves as a foundational library for academic research and industrial applications in robotic motion planning, providing a modular architecture, a broad selection of planners, and an extensible benchmarking infrastructure. It is used as a core planning backend for numerous robotics platforms and has played a central role in the development and rigorous evaluation of both classical and modern motion planning methods.

1. Architecture and Core Design Principles

OMPL provides a modular, object-oriented framework targeting the implementation and testing of sampling-based motion planners for high-dimensional configuration spaces. The core abstractions include:

• State space abstraction: OMPL supports arbitrary state spaces, enabling planners to operate on spaces such as $\mathbb{R}^n$, SE(2), SE(3), compound spaces, or user-defined manifolds.
• Problem definition: Planning problems are specified in C++ by composing the state space, specifying the state validity function (typically a collision checker), and setting the start and goal states. Optional optimization objectives, such as minimizing path length or maximizing clearance, can also be included.
• Planner abstraction: OMPL employs a generic planner interface that supports plug-and-play integration of different algorithmic families. Multiple planners can be instantiated and benchmarked in parallel within a single experiment.

This separation of concerns allows researchers to experiment with algorithm variants and configurations while maintaining a reproducible computational environment.

2. Benchmarking Infrastructure

A distinguishing feature of OMPL is its extensible benchmarking infrastructure, which enables quantitative and qualitative assessment of motion planning algorithms under standardized conditions (Moll et al., 2014). The architecture consists of:

• Benchmarking software framework: Integrated directly into OMPL, users define planning problems and planners via the C++ API, configuration files, or using the OMPL.app GUI. Multiple planners (even multiple instances of the same planner, e.g., for parameter sweeps) can be launched concurrently. Each run's performance (including time, number of sampled states, solution quality metrics) is logged independently. Failures, timeouts, and crashes are recorded per run, preventing systemic failures from invalidating entire experiments.
• Extensible logging/database formats: Benchmark results are exported to plain-text, human-readable files. A conversion utility imports logs into an SQLite3 database with an extensible schema. Key tables include experiments (storing high-level run metadata), plannerConfigs (individual planner parameters), runs (per-run results, including solution status), and progress (for optimizers reporting trajectory improvement over time). The schema grows automatically as new metrics or planners are introduced, requiring no codebase changes for extensibility.
• Planner Arena visualization tool: The Planner Arena web-based visualization system interfaces directly with the benchmark database [http://plannerarena.org]. It auto-generates UI widgets and plotting facilities based on the available data, supporting interactive exploration of performance attributes such as solution time, quality, cumulative probability of success, and regression across OMPL versions. Export formats (PDF, RData) permit both professional reporting and further statistical analysis in environments such as R.

This tripartite system allows researchers to perform large-scale, statistically robust comparisons among planners, and facilitates in-depth diagnostics of algorithmic behavior.

3. Workflow and Implementation

The typical workflow in OMPL involves the following sequence:

1. Define configuration space and planning problem in C++ with constructs such as:

   ompl::base::StateSpacePtr space(new ompl::base::RealVectorStateSpace(2)); ompl::geometric::SimpleSetup ss(space); ss.setStateValidityChecker(obstacleChecker); // Set start/goal, etc.

2. Instantiate one or more planners and tune parameters, either programmatically or with configuration file entries such as planner.parameter=value.
3. Run the benchmark to generate, for each planner, multiple runs with timeouts, random seeds, and logging of performance metrics. Each planner executes in a separate thread, ensuring that a single planner’s hang or crash does not interrupt the experiment.

   ompl::geometric::RRTConnect rrtConnect(ss.getSpaceInformation()); benchmark.addPlanner(&rrtConnect); benchmark.runBenchmark(); benchmark.saveResults("benchmark_log.txt");

4. Analyze results by importing logs into the SQL database, then exploring them using Planner Arena or custom SQL/statistical queries.

This modular pipeline promotes repeatable, unbiased performance comparisons and efficient workflow management when evaluating algorithms across complex robotic scenarios.

4. Extensibility and Interoperability

OMPL's architecture is explicitly designed for extensibility and adoption by other planning platforms:

• Extensible log/database schema: The dynamic expansion of log and progress tables allows OMPL to accommodate new planner types, metrics, or problem properties without modification of parsing code. This design enables rapid integration of new research algorithms with additional outputs.
• Interoperation with third-party libraries: The log format is sufficiently general that other libraries such as MoveIt! can produce compatible outputs for cross-tool benchmarking. This facilitates cross-system comparisons and the adoption of OMPL’s benchmarking methodology beyond its native ecosystem.
• Open-source and community-driven development: By providing a modular C++ base, robust APIs, and well-documented outputs, OMPL has become a standard research tool, and enhancements in the library quickly propagate through the motion planning community.

5. Statistical and Visualization Techniques

The built-in statistical infrastructure in OMPL is designed for rigorous, scientifically sound performance analysis:

• Overall performance plots: Box plots and cumulative distribution functions are automatically generated, presenting medians, quartiles, confidence intervals, outliers, and probability of success over timeouts.
• Progress plots: For asymptotically optimal planners, intermediate solution cost (e.g., path length) is plotted over runtime with smoothed means and 95% confidence intervals, enabling temporal diagnostics of convergence.
• Regression plots: By archiving results across versions of OMPL, users can detect regression or improvement in algorithms resulting from code or parameter changes.
• Export options: PDF and RData outputs provide camera-ready figures for reporting and flexibility for customized, post-hoc statistical analysis.

These facilities are essential for coping with the stochasticity in sampling-based motion planners and for constructing statistically significant, reproducible comparisons.

6. Impact and Reproducibility

The deep coupling of benchmarking with OMPL’s planning and problem definition facilities results in a platform that advances both the state of the art and reproducibility standards in motion planning:

• Standardization: The unified environment and common interfaces ensure that planners are compared under identical conditions, controlling for differences in problem encoding, hardware, or system loads.
• Rapid adoption: The straightforward extensibility and ease of integration have resulted in widespread usage not only in academic research but also in deployment pipelines of robotics frameworks.
• Research methodology: By centralizing planner benchmarking and facilitating rigorous, open comparison, OMPL and its infrastructure have clarified the empirical evaluation standard in motion planning algorithm research.

7. Concluding Remarks

OMPL provides an integrated, extensible suite for motion planner implementation, benchmarking, analysis, and visualization. Its design philosophy—centered on modularity, extensibility, and rigorous comparison—has influenced both research practice and software infrastructure for robotic motion planning. The benchmarking infrastructure specifically enables the isolation and evaluation of algorithmic advances, fosters reproducibility, and supports the large-scale adoption and fair comparison of new planning methodologies in both academic and industrial settings (Moll et al., 2014).