Demo obstacle-triggered replanning with the layered global map - #3
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This PR adds support for replanning when a progress error is raised. We also add similar nav2 support for handling such progress errors. Signed-off-by: Arjo Chakravarty <arjoc@intrinsic.ai>
Signed-off-by: Arjo Chakravarty <arjoc@intrinsic.ai>
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Here is a summary of issues addressed:
For example:
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* Adds support for replanning when a progress error is raised This PR adds support for replanning when a progress error is raised. We also add similar nav2 support for handling such progress errors. Signed-off-by: Arjo Chakravarty <arjoc@intrinsic.ai> * Add launch testing Signed-off-by: Arjo Chakravarty <arjoc@intrinsic.ai> * fix(nav2): stage lifecycle startup after robot readiness * fix(path-server): conservatively downsample fine occupancy grids * fix(nav2-traffic): resend unchanged targets for new plans * fix(nav2-traffic): ignore aborts from superseded goals * fix(plan-executor): replan when map updates block active routes * fix(plan-executor): align incremental targets with route direction * fix(map-demo): retain scans while filtering the robot body * feat(map-demo): add simple and warehouse replan scenarios * feat(map-demo): color-code robots, regions, goals, and paths * feat(map-demo): automate obstacle spawning and replan checks * feat(map-demo): launch integrated obstacle replan scenarios * docs: describe obstacle-driven replanning workflow --------- Signed-off-by: SamuelFoo <fooenzesamuel@gmail.com> Co-authored-by: Arjo Chakravarty <arjoc@intrinsic.ai> Signed-off-by: Samuel Foo Enze <fooenzesamuel@gmail.com>
New feature implementation
Implemented feature
This PR integrates the layered global occupancy map with the obstacle replanning flow from open-rmf/next_gen_prototype#39.
Two robots publish local scan observations into the combined
/map. After a long obstacle is spawned, the plan executor detects blocked active routes and requests new plans using the updated global map.Implementation description
This PR adds:
1.0 mPiBT plannerThe demo documentation describes both scenarios, launch arguments, visualization, and expected replanning sequence.
simple_replan_compressed.mp4
warehouse_replan_compressed.mp4
Next steps
reset_sourcebehavior directly to those layers.GenAI Use
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Generated-by: GPT-5
@arjo129 ready for review!