[Open Source] DDS AutoDiag v0.1.0 — ROS 2 DDS Diagnostics with AI-Assisted Troubleshooting
Overview
DDS AutoDiag v0.1.0 is an independent, MIT-licensed open-source tool for diagnosing ROS 2 communication problems across robots, computers, and containers. It offers 23 diagnostic checks covering DDS discovery, network configuration, middleware settings, and QoS compatibility. The tool provides a graphical interface for visual diagnostics and includes AI-assisted troubleshooting that explains findings, suggests configuration changes, and references curated documentation. Additionally, it supports natural-language instructions to configure RViz visualizations. This is the first public release, with the author seeking community feedback on diagnostic accuracy, missing failure scenarios, and workflow improvements.
Generated from attributed reports · Updated 1 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · diagnostic checks: 23 other · Basis not reported
“Diag v0.1.0, an independent, MIT-licensed open-source tool for diagnosing ROS 2 communication problems across robots, computers, and containers. What does it offer? 23 diagnostic checks Investigates common ROS 2 communication issues, including DDS discovery, network configuration, middleware settings, and QoS compatibility. Visual diagnostics Provides a graphical interface to explore diagnostic results and identify p”
Exact source · revision 2Source owner not reported
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- ROS Discourse — Community updatesSignal[Open Source] DDS AutoDiag v0.1.0 — ROS 2 DDS Diagnostics with AI-Assisted Troubleshooting
DDS AutoDiag v0.1.0 is an independent, MIT-licensed open-source tool for diagnosing ROS 2 communication problems across robots, computers, and containers. It offers 23 diagnostic checks covering DDS discovery, network configuration, middleware settings, and QoS compatibility. The tool provides a graphical interface for visual diagnostics and includes AI-assisted troubleshooting that explains findings, suggests configuration changes, and references curated documentation. Additionally, it supports natural-language instructions to configure RViz visualizations. This is the first public release, with the author seeking community feedback on diagnostic accuracy, missing failure scenarios, and workflow improvements.
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