Source-linked AI summary
An Extensible Benchmarking Infrastructure for Motion Planning Algorithms
Mark Moll, Ioan A. Sucan, Lydia E. Kavraki
TL;DR
Sampling-based planners are difficult to characterize across problem classes and performance measures. The paper presents an extensible benchmarking framework, result formats, and interactive visualization, enabling comparison across problems and planners while its default benchmark set remains nonstandard.
Problem
Sampling-based planners are difficult to assess across problem classes, and applications prioritize different metrics with no universal measure for all benchmarks.
Source: §I · p. 1Method
The paper combines OMPL benchmarking capabilities with extensible metrics and logging formats and an interactive tool for exploring collected results.
Source: §I · p. 1, §III · p. 3Results
Planner Arena supports interactive exploration and plots that compare planners across selected benchmark problems and performance attributes.
Source: §III · pp. 3–4Takeaways & Limitations
The general log format and database schema can support direct comparisons of different planning implementations across libraries.
Source: §IV · p. 5The default Planner Arena problems are not claimed to form a standard benchmark set, though they represent problem types used in prior work.
Source: §IV · p. 5
Abstract
from arXiv · showhide
Sampling-based planning algorithms are the most common probabilistically complete algorithms and are widely used on many robot platforms. Within this class of algorithms, many variants have been proposed over the last 20 years, yet there is still no characterization of which algorithms are well-suited for which classes of problems. This has motivated us to develop a benchmarking infrastructure for motion planning algorithms. It consists of three main components. First, we have created an extensive benchmarking software framework that is included with the Open Motion Planning Library (OMPL), a C++ library that contains implementations of many sampling-based algorithms. Second, we have defined extensible formats for storing benchmark results. The formats are fairly straightforward so that other planning libraries could easily produce compatible output. Finally, we have created an interactive, versatile visualization tool for compact presentation of collected benchmark data. The tool and underlying database facilitate the analysis of performance across benchmark problems and planners.
I. INTRODUCTION
Motion planning is difficult even in its basic form, while sampling-based planners remain hard to compare across problem classes and performance measures. The paper introduces an extensible benchmarking infrastructure to support those comparisons.
I. INTRODUCTION
- Even basic motion planning has no efficient general solution, and differential constraints or broader goals make the problem harder.Algorithms therefore often use weaker guarantees, including resolution completeness, probabilistic completeness, or asymptotic optimality.Source: §I · p. 1
- Planner benchmarking requires repeated runs, careful distribution comparisons, and measures suited to different applications and problem classes.Sampling strategies tend to suit particular problem classes, which are difficult to define, and no universal metric captures all applications.Source: §I · p. 1
- The infrastructure extends prior benchmarking capabilities with extensible metrics, accessible abstractions, compatible logging formats, and interactive visualization.The authors state that, to their knowledge, prior work did not offer interactive exploration and visualization of benchmark results.Source: §I · p. 1
II. BENCHMARKING INFRASTRUCTURE
OMPL’s benchmarking workflow packages a motion-planning problem and selected planners into repeated, resource-limited runs, then saves and exposes the collected results.
II. BENCHMARKING INFRASTRUCTURE
- A user defines the robot’s state space, state-validity function, start, and goal in a C++ problem object used to construct a benchmark.State validity can, for example, test collision freedom.Source: §II · pp. 1–2
- The user selects planners, sets per-run time and memory limits, and specifies how many runs to execute for each planner.Source: §II · p. 2
- After benchmarking, results are saved to a log, imported into an SQL database, and available for direct queries or interactive visualization.Source: §II · p. 2
A. Defining motion planning problems
The infrastructure supports problem setup through both a plain-text command-line format and a more flexible programming interface, with GUI assistance and MoveIt! compatibility.
A. Defining motion planning problems
- Rigid-body problems use mesh files and a plain-text key-value format; users can specify path length, minimum clearance, or mechanical work as objectives.The command-line format also supports simple kinodynamic systems, whose planners use random controls when controllers or steering functions are unavailable, making planning extremely challenging.Source: A. Defining motion planning problems · p. 2
- OMPL.app’s GUI can load meshes, define start and goal states, save configurations, visualize planner trees or graphs, and play back saved paths.Source: A. Defining motion planning problems · p. 2
- In code, users can define arbitrary state spaces, validity rules, kinodynamic systems, objectives, and planning algorithms; OMPL 1.0 includes 29 algorithms.The API has minimal requirements and is not limited to sampling-based planners; shared low-level functions such as collision checking help isolate algorithm differences in benchmarks.Source: A. Defining motion planning problems · p. 2
- MoveIt!’s benchmarking facilities are based on and compatible with OMPL’s, using URDF files for robot geometry and kinematics and a database for benchmark problems.Source: A. Defining motion planning problems · p. 2
B. Specifying planning algorithms
OMPL groups planners by motion model and solution objective, while allowing users to tune, replicate, and run planner instances under benchmark supervision.
B. Specifying planning algorithms
- OMPL separates geometric or kinematic planners from kinodynamic planners, and distinguishes first-solution planners from optimizing planners.An optimizing planner’s threshold controls whether it runs until time expires or terminates after finding any solution.Source: B. Specifying planning algorithms · p. 2
- Users can select multiple planners, accept OMPL’s default parameters, or set parameters and create named instances for parameter sweeps.Source: B. Specifying planning algorithms · p. 2
- Each planner run uses a separate thread, allowing the benchmark to terminate a hanging planner, record a crash, and continue.Source: B. Specifying planning algorithms · p. 2
C. A database of benchmark runs
Benchmark logs are consolidated into a queryable SQLite database that organizes experiment, planner, run, and progress data and supports extensible properties and compatible input from other planning software.
C. A database of benchmark runs
- Multiple log files can be added to one SQLite database, which users can transfer as a single file and query programmatically.A script imports the benchmark logs into the database.Source: C. A database of benchmark runs · pp. 2–3
- The schema separates experiment and hardware metadata, planner settings, per-run results, and periodically collected solution-cost progress.Planner settings are stored as key-value lists because parameters vary substantially among planners.Source: C. A database of benchmark runs · p. 3, Figure 2 · p. 3
- Each run records generated states, duration, solution length and clearance, and termination status; solution paths are simplified by shortcutting and smoothing.Runs may end with a solution, a timeout, or a crash.Source: C. A database of benchmark runs · p. 3
- Structured log files let new run and progress properties become database columns automatically, without new parsers or additional code.Planners that do not report a property store “N/A” in its column.Source: C. A database of benchmark runs · p. 3
- Plain-text logs are easy for other motion-planning software to generate and parse, allowing compatible results from tools such as MoveIt! to enter the database.This supports comparisons between abstract OMPL benchmarks and more elaborate MoveIt! settings, possibly derived from experimental data.Source: C. A database of benchmark runs · p. 3
III. INTERACTIVE ANALYSIS OF RESULTS
Planner Arena provides interactive plots for comparing planners across selected problems, metrics, and OMPL versions. Its views expose run distributions, progress, and regressions, while highlighting the importance of missing data and application-specific metrics.
III. INTERACTIVE ANALYSIS OF RESULTS
- The site dynamically builds selection controls from database contents, and its code can run locally or be modified for custom visualizations.Plots can be exported as PDF or as RData containing both the plot and its data for further analysis or customization.Source: §III · pp. 3, 5
- Overall-performance plots compare planners on selected measures, using box plots for integer- and real-valued metrics or cumulative distributions, solution gaps, and best solutions within time limits.A cumulative distribution can show the probability of solving within a specified time; solution gaps may help on very hard problems.Source: §III · pp. 3–4
- Missing values are ignored in most measures, so when a planner fails 99 of 100 runs, average solution length reflects its single successful run.A table beneath the plot reports each planner’s data-point and missing-value counts.Source: §III · p. 4
- LBKPIECE tends to be among the fastest planners but also tends to produce longer paths, so planner choice can depend on application priorities.Bidirectional planners such as RRT-Connect tend to be faster than unidirectional planners such as RRT, but the comparison is not universal.Source: §III · p. 4
- Progress plots show smoothed mean path-cost convergence with a 95% confidence interval, and report how many measurements are available at each one-second interval.Individual measurements can optionally appear as semitransparent dots.Source: §III · pp. 4–5
- Regression plots use bars with error bars to compare planner performance across OMPL versions and help identify unintended performance side effects.They also support comparing user modifications with the latest official release.Source: §III · p. 5
- Planner Arena’s default database contains nine problems, spanning simple rigid-body, optimizing-planner, kinodynamic, and multi-robot cases.Some problems are designed with suboptimal decoy homotopy classes for optimizing planners.Source: §III · p. 5, Figure 4 · p. 5
IV. DISCUSSION
The discussion proposes developing benchmarks with community input and extensive simulations and experiments. It also calls for interoperable formats and notes that the current Planner Arena problem set is not claimed as a standard.
IV. DISCUSSION
- The authors propose toy problems that isolate common planning difficulties and a separate suite whose measured performance predicts performance on more complex real-world scenarios.Examples of toy difficulties include narrow passages and false leads.Source: §IV · p. 5
- Standard input and output formats would let other planning libraries use the same benchmark problems and directly compare algorithm implementations.The authors describe their log format and database schema as general enough to adapt to other motion-planning software.Source: §IV · p. 5
- The current Planner Arena problems are representative of types used in prior work, but are not claimed to form a standard benchmark set; more results are expected over time.The authors expect community input and extensive simulations and experiments to help create a benchmark suite.Source: §IV · p. 5