ompl_planning.yaml

planner_configs:
  AnytimePathShortening:
    type: geometric::AnytimePathShortening
    shortcut: true # Attempt to shortcut all new solution paths
    hybridize: true # Compute hybrid solution trajectories
    max_hybrid_paths: 24 # Number of hybrid paths generated per iteration
    num_planners: 4 # The number of default planners to use for planning
    planners: "" # A comma-separated list of planner types (e.g., "PRM,EST,RRTConnect"Optionally, planner parameters can be passed to change the default:"PRM[max_nearest_neighbors=5],EST[goal_bias=.5],RRT[range=10. goal_bias=.1]"
  SBL:
    type: geometric::SBL
    range: 0.0 # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
  EST:
    type: geometric::EST
    range: 0.0 # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0 setup()
    goal_bias: 0.05 # When close to goal select goal, with this probability. default: 0.05
  LBKPIECE:
    type: geometric::LBKPIECE
    range: 0.0 # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
    border_fraction: 0.9 # Fraction of time focused on boarder default: 0.9
    min_valid_path_fraction: 0.5 # Accept partially valid moves above fraction. default: 0.5
  BKPIECE:
    type: geometric::BKPIECE
    range: 0.0 # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
    border_fraction: 0.9 # Fraction of time focused on boarder default: 0.9
    failed_expansion_score_factor: 0.5 # When extending motion fails, scale score by factor. default: 0.5
    min_valid_path_fraction: 0.5 # Accept partially valid moves above fraction. default: 0.5
  KPIECE:
    type: geometric::KPIECE
    range: 0.0 # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
    goal_bias: 0.05 # When close to goal select goal, with this probability. default: 0.05
    border_fraction: 0.9 # Fraction of time focused on boarder default: 0.9 (0.0,1.]
    failed_expansion_score_factor: 0.5 # When extending motion fails, scale score by factor. default: 0.5
    min_valid_path_fraction: 0.5 # Accept partially valid moves above fraction. default: 0.5
  RRT:
    type: geometric::RRT
    range: 0.0 # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
    goal_bias: 0.05 # When close to goal select goal, with this probability? default: 0.05
  RRTConnect:
    type: geometric::RRTConnect
    range: 0.0 # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
  RRTstar:
    type: geometric::RRTstar
    range: 0.0 # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
    goal_bias: 0.05 # When close to goal select goal, with this probability? default: 0.05
    delay_collision_checking: 1 # Stop collision checking as soon as C-free parent found. default 1
  TRRT:
    type: geometric::TRRT
    range: 0.0 # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
    goal_bias: 0.05 # When close to goal select goal, with this probability? default: 0.05
    max_states_failed: 10 # when to start increasing temp. default: 10
    temp_change_factor: 2.0 # how much to increase or decrease temp. default: 2.0
    min_temperature: 10e-10 # lower limit of temp change. default: 10e-10
    init_temperature: 10e-6 # initial temperature. default: 10e-6
    frontier_threshold: 0.0 # dist new state to nearest neighbor to disqualify as frontier. default: 0.0 set in setup()
    frontier_node_ratio: 0.1 # 1/10, or 1 nonfrontier for every 10 frontier. default: 0.1
    k_constant: 0.0 # value used to normalize expresssion. default: 0.0 set in setup()
  PRM:
    type: geometric::PRM
    max_nearest_neighbors: 10 # use k nearest neighbors. default: 10
  PRMstar:
    type: geometric::PRMstar
  FMT:
    type: geometric::FMT
    num_samples: 1000 # number of states that the planner should sample. default: 1000
    radius_multiplier: 1.1 # multiplier used for the nearest neighbors search radius. default: 1.1
    nearest_k: 1 # use Knearest strategy. default: 1
    cache_cc: 1 # use collision checking cache. default: 1
    heuristics: 0 # activate cost to go heuristics. default: 0
    extended_fmt: 1 # activate the extended FMT*: adding new samples if planner does not finish successfully. default: 1
  BFMT:
    type: geometric::BFMT
    num_samples: 1000 # number of states that the planner should sample. default: 1000
    radius_multiplier: 1.0 # multiplier used for the nearest neighbors search radius. default: 1.0
    nearest_k: 1 # use the Knearest strategy. default: 1
    balanced: 0 # exploration strategy: balanced true expands one tree every iteration. False will select the tree with lowest maximum cost to go. default: 1
    optimality: 1 # termination strategy: optimality true finishes when the best possible path is found. Otherwise, the algorithm will finish when the first feasible path is found. default: 1
    heuristics: 1 # activates cost to go heuristics. default: 1
    cache_cc: 1 # use the collision checking cache. default: 1
    extended_fmt: 1 # Activates the extended FMT*: adding new samples if planner does not finish successfully. default: 1
  PDST:
    type: geometric::PDST
  STRIDE:
    type: geometric::STRIDE
    range: 0.0 # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
    goal_bias: 0.05 # When close to goal select goal, with this probability. default: 0.05
    use_projected_distance: 0 # whether nearest neighbors are computed based on distances in a projection of the state rather distances in the state space itself. default: 0
    degree: 16 # desired degree of a node in the Geometric Near-neightbor Access Tree (GNAT). default: 16
    max_degree: 18 # max degree of a node in the GNAT. default: 12
    min_degree: 12 # min degree of a node in the GNAT. default: 12
    max_pts_per_leaf: 6 # max points per leaf in the GNAT. default: 6
    estimated_dimension: 0.0 # estimated dimension of the free space. default: 0.0
    min_valid_path_fraction: 0.2 # Accept partially valid moves above fraction. default: 0.2
  BiTRRT:
    type: geometric::BiTRRT
    range: 0.0 # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
    temp_change_factor: 0.1 # how much to increase or decrease temp. default: 0.1
    init_temperature: 100 # initial temperature. default: 100
    frontier_threshold: 0.0 # dist new state to nearest neighbor to disqualify as frontier. default: 0.0 set in setup()
    frontier_node_ratio: 0.1 # 1/10, or 1 nonfrontier for every 10 frontier. default: 0.1
    cost_threshold: 1e300 # the cost threshold. Any motion cost that is not better will not be expanded. default: inf
  LBTRRT:
    type: geometric::LBTRRT
    range: 0.0 # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
    goal_bias: 0.05 # When close to goal select goal, with this probability. default: 0.05
    epsilon: 0.4 # optimality approximation factor. default: 0.4
  BiEST:
    type: geometric::BiEST
    range: 0.0 # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
  ProjEST:
    type: geometric::ProjEST
    range: 0.0 # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
    goal_bias: 0.05 # When close to goal select goal, with this probability. default: 0.05
  LazyPRM:
    type: geometric::LazyPRM
    range: 0.0 # Max motion added to tree. ==> maxDistance_ default: 0.0, if 0.0, set on setup()
  LazyPRMstar:
    type: geometric::LazyPRMstar
  SPARS:
    type: geometric::SPARS
    stretch_factor: 3.0 # roadmap spanner stretch factor. multiplicative upper bound on path quality. It does not make sense to make this parameter more than 3. default: 3.0
    sparse_delta_fraction: 0.25 # delta fraction for connection distance. This value represents the visibility range of sparse samples. default: 0.25
    dense_delta_fraction: 0.001 # delta fraction for interface detection. default: 0.001
    max_failures: 1000 # maximum consecutive failure limit. default: 1000
  SPARStwo:
    type: geometric::SPARStwo
    stretch_factor: 3.0 # roadmap spanner stretch factor. multiplicative upper bound on path quality. It does not make sense to make this parameter more than 3. default: 3.0
    sparse_delta_fraction: 0.25 # delta fraction for connection distance. This value represents the visibility range of sparse samples. default: 0.25
    dense_delta_fraction: 0.001 # delta fraction for interface detection. default: 0.001
    max_failures: 5000 # maximum consecutive failure limit. default: 5000
  AITstar:
    type: geometric::AITstar
    use_k_nearest: 1 # whether to use a k-nearest RGG connection model (1) or an r-disc model (0). Default: 1
    rewire_factor: 1.001 # rewire factor of the RGG. Valid values: [1.0:0.01:3.0]. Default: 1.001
    samples_per_batch: 100 # batch size. Valid values: [1:1:1000]. Default: 100
    use_graph_pruning: 1 # enable graph pruning (1) or not (0). Default: 1
    find_approximate_solutions: 0 # track approximate solutions (1) or not (0). Default: 0
    set_max_num_goals: 1 # maximum number of goals sampled from sampleable goal regions. Valid values: [1:1:1000]. Default: 1
  ABITstar:
    type: geometric::ABITstar
    use_k_nearest: 1 # whether to use a k-nearest RGG connection model (1) or an r-disc model (0). Default: 1
    rewire_factor: 1.001 # rewire factor of the RGG. Valid values: [1.0:0.01:3.0]. Default: 1.001
    samples_per_batch: 100 # batch size. Valid values: [1:1:1000]. Default: 100
    use_graph_pruning: 1 # enable graph pruning (1) or not (0). Default: 1
    prune_threshold_as_fractional_cost_change: 0.1 # fractional change in the solution cost AND problem measure necessary for pruning to occur. Default: 0.1
    delay_rewiring_to_first_solution: 0 # delay (1) or not (0) rewiring until a solution is found. Default: 0
    use_just_in_time_sampling: 0 # delay the generation of samples until they are * necessary. Only works with r-disc connection and path length minimization. Default: 0
    drop_unconnected_samples_on_prune: 0 # drop unconnected samples when pruning, regardless of their heuristic value. Default: 0
    stop_on_each_solution_improvement: 0 # stop the planner each time a solution improvement is found. Useful for debugging. Default: 0
    use_strict_queue_ordering: 0 # sort edges in the queue at the end of the batch (0) or after each rewiring (1). Default: 0
    find_approximate_solutions: 0 # track approximate solutions (1) or not (0). Default: 0
    initial_inflation_factor: 1000000 # inflation factor for the initial search. Valid values: [1.0:0.01:1000000.0]. Default: 1000000
    inflation_scaling_parameter: 10 # scaling parameter for the inflation factor update policy. Valid values: [1.0:0.01:1000000.0]. Default: 0
    truncation_scaling_parameter: 5.0 # scaling parameter for the truncation factor update policy. Valid values: [1.0:0.01:1000000.0]. Default: 0
  BITstar:
    type: geometric::BITstar
    use_k_nearest: 1 # whether to use a k-nearest RGG connection model (1) or an r-disc model (0). Default: 1
    rewire_factor: 1.001 # rewire factor of the RGG. Valid values: [1.0:0.01:3.0]. Default: 1.001
    samples_per_batch: 100 # batch size. Valid values: [1:1:1000]. Default: 100
    use_graph_pruning: 1 # enable graph pruning (1) or not (0). Default: 1
    prune_threshold_as_fractional_cost_change: 0.1 # fractional change in the solution cost AND problem measure necessary for pruning to occur. Default: 0.1
    delay_rewiring_to_first_solution: 0 # delay (1) or not (0) rewiring until a solution is found. Default: 0
    use_just_in_time_sampling: 0 # delay the generation of samples until they are * necessary. Only works with r-disc connection and path length minimization. Default: 0
    drop_unconnected_samples_on_prune: 0 # drop unconnected samples when pruning, regardless of their heuristic value. Default: 0
    stop_on_each_solution_improvement: 0 # stop the planner each time a solution improvement is found. Useful for debugging. Default: 0
    use_strict_queue_ordering: 0 # sort edges in the queue at the end of the batch (0) or after each rewiring (1). Default: 0
    find_approximate_solutions: 0 # track approximate solutions (1) or not (0). Default: 0
arm_1:
  planner_configs:
    - AnytimePathShortening
    - SBL
    - EST
    - LBKPIECE
    - BKPIECE
    - KPIECE
    - RRT
    - RRTConnect
    - RRTstar
    - TRRT
    - PRM
    - PRMstar
    - FMT
    - BFMT
    - PDST
    - STRIDE
    - BiTRRT
    - LBTRRT
    - BiEST
    - ProjEST
    - LazyPRM
    - LazyPRMstar
    - SPARS
    - SPARStwo
    - AITstar
    - ABITstar
    - BITstar
  projection_evaluator: joints(j_arm_1_1,j_arm_1_2)
  longest_valid_segment_fraction: 0.004
arm_2:
  planner_configs:
    - AnytimePathShortening
    - SBL
    - EST
    - LBKPIECE
    - BKPIECE
    - KPIECE
    - RRT
    - RRTConnect
    - RRTstar
    - TRRT
    - PRM
    - PRMstar
    - FMT
    - BFMT
    - PDST
    - STRIDE
    - BiTRRT
    - LBTRRT
    - BiEST
    - ProjEST
    - LazyPRM
    - LazyPRMstar
    - SPARS
    - SPARStwo
    - AITstar
    - ABITstar
    - BITstar
  projection_evaluator: joints(j_arm_2_1,j_arm_2_2)
  longest_valid_segment_fraction: 0.004
both_arms:
  planner_configs:
    - AnytimePathShortening
    - SBL
    - EST
    - LBKPIECE
    - BKPIECE
    - KPIECE
    - RRT
    - RRTConnect
    - RRTstar
    - TRRT
    - PRM
    - PRMstar
    - FMT
    - BFMT
    - PDST
    - STRIDE
    - BiTRRT
    - LBTRRT
    - BiEST
    - ProjEST
    - LazyPRM
    - LazyPRMstar
    - SPARS
    - SPARStwo
    - AITstar
    - ABITstar
    - BITstar
  projection_evaluator: joints(j_arm_1_1,j_arm_1_2)
  longest_valid_segment_fraction: 0.004
torso:
  planner_configs:
    - AnytimePathShortening
    - SBL
    - EST
    - LBKPIECE
    - BKPIECE
    - KPIECE
    - RRT
    - RRTConnect
    - RRTstar
    - TRRT
    - PRM
    - PRMstar
    - FMT
    - BFMT
    - PDST
    - STRIDE
    - BiTRRT
    - LBTRRT
    - BiEST
    - ProjEST
    - LazyPRM
    - LazyPRMstar
    - SPARS
    - SPARStwo
    - AITstar
    - ABITstar
    - BITstar
  projection_evaluator: joints(j_torso_1)
  longest_valid_segment_fraction: 0.004