Comments (2)
Per the README and the target_pose_generator
interface documentation, the target waypoints produced by the target_pose_generator
interface implementations are expected to be relative to the robot kinematic base frame. This is a change that was introduced as a part of the update to version 1.0.0
You can still use the workflow of creating a point cloud using PCL mesh sampling and loading that point cloud into the reach study via the point_cloud_target_pose_generator
. The only difference is that the point cloud needs to be relative to the kinematic base frame of the robot. This can be done in a number of ways:
- Moving the origin of the sampled mesh to the kinematic base frame
- Manually applying a transformation to the point cloud before/after saving it (e.g.,
pcl_transform_cloud
, etc.) - Creating a new ROS-enabled
point_cloud_target_pose_generator
which can load a point cloud relative to an arbitrary location and transforming it using TF to the kinematic base frame
I use option 1 in the reach_ros
demo, but I could see option 3 being potentially useful as well and it wouldn't be difficult to create
from reach.
Closing; addressed in above comment and in linked PRs
from reach.
Related Issues (15)
- Could not find library plugin reach_core/plugins/MultiplicativeFactory HOT 3
- Trouble creating initial database HOT 19
- Reach ROS2 branch HOT 7
- Does this tool require a mesh to perform a reach analysis or will a point set work?
- Efficient Nearest Neighbors Search HOT 2
- Update Documentation
- PYTHONPATH does not include REACH Python interface with colcon build HOT 1
- Customizable creation of target points HOT 1
- Is it possible to do a reachability study in a volume ? HOT 1
- Reach study results heat map HOT 5
- robot_reach_study_node crash if there are too much points in point cloud
- Fix Python bindings for heat map generation HOT 1
- reach_demo refers to motoman model that is not part of repository HOT 4
- Very simple custom plugin fails with segmentation fault HOT 3
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