+<!--This does not replace URDF, and is not an extension of URDF.
+ This is a format for representing semantic information about the robot structure.
+ A URDF file must exist for this robot as well, where the joints and the links that are referenced are defined
+-->
+<robot name="mr12urdf20240605">
+ <!--GROUPS: Representation of a set of joints and links. This can be useful for specifying DOF to plan for, defining arms, end effectors, etc-->
+ <!--LINKS: When a link is specified, the parent joint of that link (if it exists) is automatically included-->
+ <!--JOINTS: When a joint is specified, the child link of that joint (which will always exist) is automatically included-->
+ <!--CHAINS: When a chain is specified, all the links along the chain (including endpoints) are included in the group. Additionally, all the joints that are parents to included links are also included. This means that joints along the chain and the parent joint of the base link are included in the group-->
+ <!--SUBGROUPS: Groups can also be formed by referencing to already defined group names-->
+ <!--GROUP STATES: Purpose: Define a named state for a particular group, in terms of joint values. This is useful to define states like 'folded arms'-->
+ <group_state name="home" group="manipulator">
+ <joint name="joint1" value="0"/>
+ <joint name="joint2" value="0"/>
+ <joint name="joint3" value="0"/>
+ <joint name="joint4" value="0"/>
+ <joint name="joint5" value="0"/>
+ <joint name="joint6" value="0"/>
+ </group_state>
+ <!--DISABLE COLLISIONS: By default it is assumed that any link of the robot could potentially come into collision with any other link in the robot. This tag disables collision checking between a specified pair of links. -->
+ 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
+ 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
+ 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
+ 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
+ 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
+ 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
+ An automatically generated package with all the configuration and launch files for using the mr12urdf20240605 with the MoveIt Motion Planning Framework
+ <!-- This package is referenced in the warehouse launch files, but does not build out of the box at the moment. Commented the dependency until this works. -->