The Open Motion Planning Library is a powerful collection of state-of-the-art sampling-based motion planning algorithms and is the default planner in MoveIt!. For more information see project webpage.
Here we review important configuration settings for OMPL. These settings can typically be found in the
ompl_planning.yaml file located in your robots
Longest Valid Segment Fraction¶
longest_valid_segment_fraction defines the discretization of robot motions used for collision checking and greatly affects the performance and reliability of OMPL-based solutions. A
motion in this context can be thought of as an edge between two nodes in a graph, where nodes are waypoints along a trajectory. The default motion collision checker in OMPL simply discretizes the edge into a number of sub-states to collision check. No continuous collision checking is currently available in OMPL/MoveIt!, though this is an area of current discussion.
longest_valid_segment_fraction is the fraction of the robot’s state space that, given the robot isn’t currently in collision, we assume the robot can travel while remaining collision free. For example, if
longest_valid_segment_fraction = 0.01, then we assume that if an edge between two nodes is less than 1/100th of the state space, then we don’t need to explicity check any sub-states along that edge, just the two nodes it connects.
In addition to the
longest_valid_segment_fraction parameter in the
ompl_planning.yaml file, there is also the
maximum_waypoint_distance, found in the dynamic reconfigure file.
maximum_waypoint_distance defines the same discretization of robot motions for collision checking, but it does so at an absolute level instead of using fractions of the state space. For example, if
maximum_waypoint_distance = 0.1, then if an edge is shorter than
0.1 in state space distance, then we don’t explicitly check any sub-states along that edge.
maximum_waypoint_distance are set, then the variable that produces the most conservative discretization (the one that would generate the most states to collision check on a given edge) is chosen.
maximum_waypoint_distance) too low, and collision checking / motion planning will be very slow. Set too high and collisions will be missed around small or narrow objects. In addition, a high collision checking resolution will cause the path smoothers to output incomprehensible motions because they are able to “catch” the invalid path and then attempt to repair them by sampling around it, but imperfectly.
A quick analysis of the effect of this parameter on two of the MoveIt! tutorial examples is documented here.
projection_evaluator can take in a list of joints or links to approximate the coverage of a configuration space. This settings is used by planners such as KPIECE, BKPIECE, LBKPIECE, and PDST. For more information read the corresponding publications.
Enforce Planning in Joint Space¶
Depending on the planning problem MoveIt chooses between
joint space and
cartesian space for problem representation.
Setting the group parameter
enforce_joint_model_state_space enforces the use of
joint space for all plans.
By default planning requests with orientation path constraints are sampled in
cartesian space so that invoking IK serves as a generative sampler.
joint space the planning process will use rejection sampling to find valid requests.
Please not that this might increase planning time considerably.
Depending on the planner you are using, other settings are available for tuning/parameter sweeping. The default values for these settings are auto-generated in the MoveIt! Setup Assistant and are listed in the
ompl_planning.yaml file - you are encouraged to tweak them.
OMPL Optimization Objectives¶
Several planners that are part of the OMPL planning library are capable of optimizing for a specified optimization objective. This tutorial describes that steps that are needed to configure these objectives. The optimal planners that are currently exposed to MoveIt! are:
And the following optimization objectives are available:
- PathLengthOptimizationObjective (Default)
The configuration of these optimization objectives can be done in the ompl_planning.yaml. A parameter with the name optimization_objective is added as a configuration parameter. The value of the parameter is set to be the name of the selected optimization objective. For example, to configure RRTstar to use the MaximizeMinClearanceObjective, the planner entry in the ompl_planning.yaml will look like:
RRTstarkConfigDefault: type: geometric::RRTstar optimization_objective: MaximizeMinClearanceObjective range: 0.0 goal_bias: 0.05 delay_collision_checking: 1
For more information on the OMPL optimal planners, the reader is referred to the OMPL - Optimal Planning documentation.
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