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 manipulator: default_planner_config: RRTConnect 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