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Bench-MR

Extend Functions

Bench-MR has support for various steer functions used by sampling-based planners, and kinodynamic robot models for forward-propagating planners.

Steer Functions

The steer functions can be provided by a list of function names (see below).

MPB.set_steer_functions(steerings: [str])

This function will set the configuration benchmark.steer_functions to the provided list of steer functions, and set the parameter benchmark.control_planners_on to False.

The following steer functions are provided with Bench-MR at the moment:

Steer function name Description
reeds_shepp Reeds-Shepp (from OMPL)
dubins Dubins (from OMPL)
posq POSQ (from palmieri/posq)
linear Linear (straight-line) steering
cc_dubins Continuous-curvature Dubins (from hbanzhaf/steering_functions)
hc_reeds_shepp Hybrid-curvature Reeds-Shepp (from hbanzhaf/steering_functions)
cc_reeds_shepp Continuous-curvature Reeds-Shepp (from hbanzhaf/steering_functions)

Robot Models

The robot models can be provided by a list of names (see below).

MPB.set_robot_models_functions(robot_models: [str])

This function will set the configuration benchmark.forward_propagations to the provided list of robot models, and set the parameter benchmark.control_planners_on to True.

The following robot models are provided with Bench-MR at the moment:

Robot model name Description
kinematic_car Kinematic Car
kinematic_single_track Kinematic Single Track

Depending on the benchmark.control_planners_on setting, each MPB instance runs all the specified steer functions or robot model functions in sequence.

Example 

def create_mpb(planner : str, extend_function : str, planning_time):
    mpb = MPB()
    mpb["max_planning_time"] = planning_time
    mpb.set_planners([planner])
    if(extend_function in steer_functions):
        mpb.set_steer_functions([extend_function])
    if(extend_function in robot_models):
        mpb.set_robot_models_functions([extend_function])
    mpb.set_corridor_grid_env(radius=5, branches = 15)
    mpb["ompl.seed"] = 0
    mpb.set_id('comparison_extend_function_%s_%s' % (planner, extend_function))
    return mpb

Comparing RRT with different extend function

In the following we compare the classical RRT algorithm, with 2 different extend function, namely the Reeds-Shepp steer function and the kinematic car model (used in forward propagation).

pool = MultipleMPB()
pool.benchmarks.append(create_mpb('rrt', 'reeds_shepp', 15))
pool.benchmarks.append(create_mpb('fprrt', 'kinematic_car', 15))
pool.run_parallel(runs=5, id='comparison_extend_functions', show_plot=True)
pool.merge('comparison_extend_functions.json')

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visualize('comparison_extend_functions.json', num_colors=10)

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plot_planner_stats('comparison_extend_functions.json', num_colors=10)

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We add now in the comparison also SST, an asymptotically near-optimal incremental version of RRT.

pool = MultipleMPB()
pool.benchmarks.append(create_mpb('rrt', 'reeds_shepp', 30))
pool.benchmarks.append(create_mpb('fprrt', 'kinematic_car', 30))
pool.benchmarks.append(create_mpb('fpsst', 'kinematic_car', 30))
pool.run_parallel(runs=5, id='comparison_extend_functions_with_sst', show_plot=True)
pool.merge('comparison_extend_functions_with_sst.json')

png

visualize('comparison_extend_functions_with_sst.json', num_colors=10)

png

plot_planner_stats('comparison_extend_functions_with_sst.json', num_colors=10)

png

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