OMPL (Open Motion Planning Library)

OMPL (Open Motion Planning Library)#

OMPL, the Open Motion Planning Library, is a generic C++ library for state-of-the-art motion planning algorithms. OMPL is compatible with different robot morphologies and planning spaces which make it widely used in robotics for motion planning problems both in 2D and 3D spaces. (for more details check the official documentation of OMPL)

OMPL with Kompass#

Kompass provides Python bindings (Pybind11 bindings) for OMPL in its navigation core package (kompass_navigation). The bindings enable setting and solving a planning problem using:

  • ‘SE2StateSpace’ which is convient for 2D motion planning. It provides an ‘SE2State’ consisting of a 2D position and rotation in the plane: SE(2): (x, y, yaw)

  • OMPL geometric planners (listed below)

  • Built-in ‘StateValidityChecker’ which implements a collision checker (using FCL) to insure finding collision free paths.

Configuring OMPL in Kompass#

The following is an example YAML configuration of OMPL. In the section below we list all the available configuration parameters for each of the planners along with their default values.

ompl:
    log_level: 'WARN'
    planning_timeout: 10.0  # (secs) Return and consider the planning to fail if solving takes more than planning_timeout
    simplification_timeout: 0.01 # # (secs) abort path simplification if it takes more than simplification_timeout
    goal_tolerance: 0.01        # (meters) Distance to consider that the robot is at goal point, i.e. no planning is performed
    optimization_objective: 'PathLengthOptimizationObjective'
    planner_id: 'ompl.geometric.KPIECE1'

Available algorithms from OMPL#

The following geometric planners are supported in Kompass:

Planners test results#

A planning problem is simulated using the Turtlebot3 Gazebo simulation Waffle map. The following table shows the planning results using each of the previous planners. The problem is simulated for 20 repetitions using each planners and the table below shows the average result values. ‘solved’ is set to False if in any repetition the method was unable to find a solution. The solution search timeout is set to 2seconds.

Tip

To check/run the test script and the associated test resources refer to kompass_navigation ompl test

method

solved

solution time (s)

solution length (m)

simplification time (s)

Conversion/Publishing ROS2 (s)

ABITstar

True

1.071

2.948

0.0075

0.00056

BFMT

True

0.1128

3.487

0.0066

0.00017

BITstar

True

1.0732

2.962

0.0061

0.00015

BKPIECE1

True

0.0703

4.469

0.01777

0.00019

BiEST

True

0.06189

4.418

0.01078

0.00014

EST

True

0.0640

4.059

0.01074

0.00013

FMT

True

0.1330

3.6277

0.00630

0.00016

InformedRRTstar

True

1.0679

2.962

0.00457

0.00013

KPIECE1

True

0.0676

5.439

0.0148

0.00017

LBKPIECE1

True

0.07543

5.174

0.02

0.00017

LBTRRT

True

1.0696

3.221

0.005

0.00012

LazyLBTRRT

True

1.0672

3.305

0.0053

0.00013

LazyPRM

False

1.0811

0.0

0.0

0.0

LazyPRMstar

True

1.0701

3.03

0.0063

0.00012

LazyRRT

True

0.0982

4.5196

0.016

0.00013

PDST

True

0.06781

3.836

0.009

0.00013

PRM

True

1.0672

3.306

0.0068

0.00012

PRMstar

True

1.0740

3.72

0.0085

0.00012

ProjEST

True

0.0683

4.19

0.0082

0.00013

RRT

True

0.09062

4.86

0.019

0.00014

RRTConnect

True

0.07535

4.78

0.014

0.00014

RRTXstatic

True

1.0712

3.03

0.0041

0.00012

RRTsharp

True

1.0680

3.01

0.0052

0.00013

RRTstar

True

1.0672

2.96

0.0042

0.00013

SBL

True

0.0800

4.039

0.0121

0.00015

SST

True

1.0681

2.63

0.0012

0.00018

STRIDE

True

0.0679

4.12

0.0098

0.00015

TRRT

True

0.0798

4.11

0.01094

0.00015

Planners Default Parameters#

ABITstar#

ABITstar Default Parameters:

  • delay_rewiring_to_first_solution: False

  • drop_unconnected_samples_on_prune: False

  • find_approximate_solutions: False

  • inflation_scaling_parameter: 10.0

  • initial_inflation_factor: 1000000.0

  • prune_threshold_as_fractional_cost_change: 0.05

  • rewire_factor: 1.1

  • samples_per_batch: 100

  • stop_on_each_solution_improvement: False

  • truncation_scaling_parameter: 5.0

  • use_graph_pruning: True

  • use_just_in_time_sampling: False

  • use_k_nearest: True

  • use_strict_queue_ordering: True

AITstar#

AITstar Default Parameters:

  • find_approximate_solutions: True

  • rewire_factor: 1.0

  • samples_per_batch: 100

  • use_graph_pruning: True

  • use_k_nearest: True

BFMT#

BFMT Default Parameters:

  • balanced: False

  • cache_cc: True

  • extended_fmt: True

  • heuristics: True

  • nearest_k: True

  • num_samples: 1000

  • optimality: True

  • radius_multiplier: 1.0

BITstar#

BITstar Default Parameters:

  • delay_rewiring_to_first_solution: False

  • drop_unconnected_samples_on_prune: False

  • find_approximate_solutions: False

  • prune_threshold_as_fractional_cost_change: 0.05

  • rewire_factor: 1.1

  • samples_per_batch: 100

  • stop_on_each_solution_improvement: False

  • use_graph_pruning: True

  • use_just_in_time_sampling: False

  • use_k_nearest: True

  • use_strict_queue_ordering: True

BKPIECE1#

BKPIECE1 Default Parameters:

  • border_fraction: 0.9

  • range: 0.0

BiEST#

BiEST Default Parameters:

  • range: 0.0

EST#

EST Default Parameters:

  • goal_bias: 0.5

  • range: 0.0

FMT#

FMT Default Parameters:

  • cache_cc: True

  • extended_fmt: True

  • heuristics: False

  • num_samples: 1000

  • radius_multiplier: 1.1

  • use_k_nearest: True

InformedRRTstar#

InformedRRTstar Default Parameters:

  • delay_collision_checking: True

  • goal_bias: 0.05

  • number_sampling_attempts: 100

  • ordered_sampling: False

  • ordering_batch_size: 1

  • prune_threshold: 0.05

  • range: 0.0

  • rewire_factor: 1.1

  • use_k_nearest: True

KPIECE1#

KPIECE1 Default Parameters:

  • border_fraction: 0.9

  • goal_bias: 0.05

  • range: 0.0

LBKPIECE1#

LBKPIECE1 Default Parameters:

  • border_fraction: 0.9

  • range: 0.0

LBTRRT#

LBTRRT Default Parameters:

  • epsilon: 0.4

  • goal_bias: 0.05

  • range: 0.0

LazyLBTRRT#

LazyLBTRRT Default Parameters:

  • epsilon: 0.4

  • goal_bias: 0.05

  • range: 0.0

LazyPRM#

LazyPRM Default Parameters:

  • max_nearest_neighbors: 8

  • range: 0.0

LazyPRMstar#

LazyPRMstar Default Parameters:

LazyRRT#

LazyRRT Default Parameters:

  • goal_bias: 0.05

  • range: 0.0

PDST#

PDST Default Parameters:

  • goal_bias: 0.05

PRM#

PRM Default Parameters:

  • max_nearest_neighbors: 8

PRMstar#

PRMstar Default Parameters:

ProjEST#

ProjEST Default Parameters:

  • goal_bias: 0.05

  • range: 0.0

RRT#

RRT Default Parameters:

  • goal_bias: 0.05

  • intermediate_states: False

  • range: 0.0

RRTConnect#

RRTConnect Default Parameters:

  • intermediate_states: False

  • range: 0.0

RRTXstatic#

RRTXstatic Default Parameters:

  • epsilon: 0.0

  • goal_bias: 0.05

  • informed_sampling: False

  • number_sampling_attempts: 100

  • range: 0.0

  • rejection_variant: 0

  • rejection_variant_alpha: 1.0

  • rewire_factor: 1.1

  • sample_rejection: False

  • update_children: True

  • use_k_nearest: True

RRTsharp#

RRTsharp Default Parameters:

  • goal_bias: 0.05

  • informed_sampling: False

  • number_sampling_attempts: 100

  • range: 0.0

  • rejection_variant: 0

  • rejection_variant_alpha: 1.0

  • rewire_factor: 1.1

  • sample_rejection: False

  • update_children: True

  • use_k_nearest: True

RRTstar#

RRTstar Default Parameters:

  • delay_collision_checking: True

  • focus_search: False

  • goal_bias: 0.05

  • informed_sampling: True

  • new_state_rejection: False

  • number_sampling_attempts: 100

  • ordered_sampling: False

  • ordering_batch_size: 1

  • prune_threshold: 0.05

  • pruned_measure: False

  • range: 0.0

  • rewire_factor: 1.1

  • sample_rejection: False

  • tree_pruning: False

  • use_admissible_heuristic: True

  • use_k_nearest: True

SBL#

SBL Default Parameters:

  • range: 0.0

SST#

SST Default Parameters:

  • goal_bias: 0.05

  • pruning_radius: 3.0

  • range: 5.0

  • selection_radius: 5.0

STRIDE#

STRIDE Default Parameters:

  • degree: 16

  • estimated_dimension: 3.0

  • goal_bias: 0.05

  • max_degree: 18

  • max_pts_per_leaf: 6

  • min_degree: 12

  • min_valid_path_fraction: 0.2

  • range: 0.0

  • use_projected_distance: False

TRRT#

TRRT Default Parameters:

  • goal_bias: 0.05

  • range: 0.0

  • temp_change_factor: 0.1

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