Reducing Simulation Jitter: Tuning ODE Physics and Collision Hulls in Gazebo
Learn how to eliminate simulation jitter in Gazebo by tuning ODE physics parameters and optimizing collision hulls for better stability and performance.
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Learn how to eliminate simulation jitter in Gazebo by tuning ODE physics parameters and optimizing collision hulls for better stability and performance.
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In my development workstation I compile a Gazebo model plugin and run a world file that uses it; the plugin initializes without errors and the simulation behaves as expected. When I package the same compiled plugin and world file into a production container (using the same Gazebo version and base image), Gazebo starts but the plugin fails to load, producing
When using Gazebo's world snapshot feature to pause physics, serialize the simulation state to an SDF file, and later restore it, the primary goal is to confirm that all relevant simulation data—including model poses, joint states, and any plugin‑internal information—is accurately recovered. The default snapshot mechanism captures only SDF‑defined elements s
Scenario I am setting up a Gazebo 13.x simulation pipeline where a world repeatedly spawns and deletes high-poly models during long runs. I need to pick a physics engine and configure the real-time factor, but the documented trade-offs leave me without a clear choice. Constraints ODE is the default and gives deterministic behavior, which matters because our
Gazebo plugin binaries compiled against an earlier major release (e.g., Gazebo 11) may fail to load on a subsequent version due to removed struct fields and unresolved symbol references. This API surface shift occurs between major Gazebo releases without a stable ABI layer, leaving no native mechanism for plugin version pinning or automatic compatibility shi
Concurrency Behavior Under Load Gazebo's physics engine processes simulation steps on a single thread, which serializes incoming ROS service requests such as /set_model_state or /apply_body_wrench . When multiple clients issue concurrent requests, the server queues them, and the observed latency grows proportionally to the request rate. The goal is to charac