MotionBench: Motion Planning Benchmarking

• MotionBench is a benchmarking framework that enables systematic, reproducible evaluation of motion planning algorithms using configurable experiments and detailed data logging.
• It supports a wide range of planners and custom scenarios via automated execution, comprehensive logging, and dynamic database integration.
• Visualization tools like Planner Arena provide publication-ready statistical plots for in-depth performance analysis and fair cross-algorithm comparisons.

MotionBench is a designation that has been used for several prominent benchmarking frameworks and datasets in robotics and motion planning research, most notably as the infrastructure established with the Open Motion Planning Library (OMPL). In the context of OMPL, MotionBench refers to a robust, extensible infrastructure for the empirical evaluation and comparison of motion planning algorithms under systematic, reproducible, and statistically rigorous conditions. It provides a comprehensive solution to the longstanding challenge of fair and meaningful assessment in the rapidly diversifying field of motion planning algorithms.

1. Software Framework: Architecture and Workflow

The core of MotionBench is a high-level, extensible benchmarking software framework integrated with OMPL, a widely adopted open-source C++ library for sampling-based motion planning. The infrastructure enables users to:

• Formulate Arbitrary Planning Problems: Users can define custom state spaces, collision-checking functions, start/goal configurations, and optimization objectives via either C++ APIs or a graphical user interface (OMPL.app).
• Select and Parameterize Planners: Over 29 planners are supported, including both geometric (kinematic) and kinodynamic variants. Benchmarks can be configured to run any combination of planners with user-specified parameters, batch sweeps, and replicated runs.
• Automated Execution and Logging: Benchmarks automatically conduct repeated trials for statistical validity, robustly handling crashes and timeouts. A detailed log of each run is maintained.
• Database Integration: Results are imported to a SQLite3 relational database, supporting complex queries, aggregation, and flexible post-processing.

The typical workflow involves:

1. Defining planning problems and associated metrics.
2. Specifying planners and their configurations.
3. Automated execution of experiment batches with multi-run statistical sampling.
4. Parsing results into extensible logs, then into a queryable, self-augmenting SQL database.
5. Visualization and in-depth comparative analysis via dedicated tools.

By employing the same underlying computational modules (e.g., collision checking) for all algorithms, MotionBench ensures that any observed performance differences are attributable solely to the theoretical properties and practical implementations of the planners themselves.

2. Extensible Benchmark Result Formats

MotionBench introduces a flexible and self-extending format for benchmark results:

• Plain Text Log Files: Benchmark runs generate straightforward logs with explicitly documented fields for experiment metadata, planner configuration (arbitrary parameter key-value pairs), individual run statistics (e.g., solution time, path cost, number of states), and periodic optimization progress (path cost over time).
• Database Schema: The database schema features dynamic augmentation, automatically adding new columns for unstructured log fields, thereby enabling future-proof extensibility.
• Interoperability: The design enables logs generated by different planners or libraries (e.g., MoveIt!) to be directly compatible with the OMPL framework, fostering community-wide standardization and the possibility of “apples-to-apples” performance comparison across research groups and software stacks.

This infrastructure greatly facilitates collaborative, reproducible benchmarking, and the sharing or archival of detailed experimental results.

3. Visualization and Analysis Tools

Visualization and analysis are integral to MotionBench’s design, embodied in the Planner Arena (http://plannerarena.org) web tool:

• Dynamic Plot Generation: The Planner Arena interface automatically presents selection menus for any combination of problems, planners, and metrics available in a loaded database. It generates publication-ready boxplots, empirical cumulative distribution functions (ECDFs), and convergence/progress plots for optimization-based planners.
• Statistical Transparency: Visualizations highlight not only average or median performance, but also full distributions (variance, outliers), and the ratio of successful to failed runs.
• Export and Extension: All plots are downloadable as vector graphics (PDF) and as R data for custom plotting or statistical analysis. The codebase itself is open source, facilitating custom extensions or local deployment.
• Regression Tracking: Changes in planner or library versions can be visualized, enabling regression studies across software releases.

These analytical tools support rigorous empirical research and fair cross-algorithmic comparison.

4. Empirical Insights and Standardization of Evaluation

Large-scale experiments conducted with MotionBench have yielded several significant empirical findings about algorithmic performance:

• The Importance of Distributional Analysis: Sampling-based planners are inherently stochastic; therefore, full performance distributions (not just mean/median) are critical for robust assessment.
• Specialization, No Universal Winner: No planner consistently dominates all problem types. For example, LBKPIECE often achieves the fastest times but produces longer paths; bidirectional planners like RRT-Connect are frequently, but not universally, faster than their unidirectional counterparts.
• Trade-offs and Application Sensitivity: Solutions must often be chosen according to the application’s priorities between computation time, solution quality (e.g., path length, clearance), and reliability (e.g., probability of finding a feasible solution within time constraints).
• Formal Metric Definitions: Key performance distribution metrics are formalized, such as the ECDF for solution time:

$$F_i(t) = \frac{1}{n} \sum_{j=1}^n \mathbf{1}[T_{i,j} \leq t]$$

where $T_{i,j}$ is the solution time of the $j$-th run for planner $i$.

MotionBench codifies standard, reproducible experimental protocols for the evaluation of motion planners, encouraging statistically valid comparisons and best practices in experimental robotics research.

5. Future Directions and Community Integration

MotionBench is envisioned as a catalyst for the broader adoption of benchmarking standards within the research community:

• Standardized Benchmark Suites: The developers aim to curate and promote a comprehensive collection of both “toy” and realistic benchmarks, enabling nuanced evaluation of planners and assisting in the prediction of real-world performance.
• Open Formats and Platforms: The extensible, easily compatible log/database formats are explicitly intended to facilitate integration with other libraries and to promote the development of universal benchmarking tools.
• Expansion of Supported Problems: The infrastructure is designed for extensibility in supported state spaces (rigid body, kinodynamic, multi-robot, etc.) and in the range of evaluation metrics.
• Cross-Community Collaboration: By lowering barriers to head-to-head comparison and data sharing, MotionBench intends to drive open, collaborative advances across robotics and algorithmic motion planning.

A plausible implication is that, as adoption grows, MotionBench's architecture and protocols may become a standard for fair, rigorous benchmarking throughout robotic motion planning research.

6. Significance and Role in the Field

MotionBench establishes a principled infrastructure to address the field’s need for rigorous, reproducible, and extensible evaluation of motion planning algorithms. Its integration of high-abstraction software, extensible results formatting, and powerful visualization tools together enables:

• Repeatable, statistically robust experimentation,
• Rapid algorithm comparison and regression analysis,
• The sharing and portrayal of large-scale empirical results,
• A groundwork for standardized, community-wide benchmarking and reproducibility.

By addressing both methodological and practical challenges, and by offering open-source tools and formats, MotionBench underpins the development and objective evaluation of the next generation of motion planning algorithms in research and application domains.