ROS Control Architecture & Debugging
Design, debug, and optimize ROS control architectures for robotic systems
What You Can Do
You can architect multi-node ROS control systems from scratch, including node design patterns, communication flows, and real-time constraints. You'll debug complex node interactions using publisher/subscriber analysis, service call tracing, and action server diagnostics. You can optimize motion controllers through PID tuning, trajectory planning validation, and performance profiling to achieve precise, responsive robotic behavior.
Features
Create scalable node hierarchies with clear separation of concerns and optimal message routing
Analyze pub/sub topics, service calls, and action servers to identify bottlenecks and latency sources
Tune PID gains, validate trajectories, and profile real-time loop performance
Design and validate transform hierarchies for multi-body kinematics and sensor fusion
Generate comprehensive test plans that verify node interactions and system behavior under load
Document and validate ROS parameter configurations across different hardware setups
Set up realistic simulation environments (Gazebo, RVIZ) that match real robot hardware
Identify CPU bottlenecks and latency sources using rosbag analysis and timing instrumentation
Example Output
Example 1: Node Architecture Design
Proposed Architecture:
- /motor_controller node (C++) — handles PWM output, 100Hz control loop
- /feedback_aggregator node (Python) — fuses encoder + IMU sensor data
- /trajectory_planner node (C++) — generates smooth velocity profiles
- /system_monitor node (Python) — logs performance metrics and diagnostics
Communication Graph:
- trajectory_planner → /cmd_velocity → motor_controller
- feedback_aggregator → /sensor_fusion → system_monitor
- All nodes call /get_status service on motor_controller for health checks
Example 2: PID Tuning Analysis
Current Performance: 28% overshoot, 850ms settling time
Root Cause: Kd too low, inadequate damping
Proposed Gains:
Kp: 2.5 (↑ from 2.0) — increase step response speed
Ki: 0.12 (↓ from 0.15) — reduce oscillation
Kd: 1.2 (↑ from 0.5) — improve damping ratio
Expected Result: <5% overshoot, 300ms settling time
Validation: Run step response test with 100 iterations
Example 3: Latency Debug Report
- ⚠️ Problem: 120ms latency spike every 4 seconds
Analysis: feedback_aggregator blocks on I2C read (contention with other threads)
Solution: Move sensor read to dedicated callback thread with timeout
Test: Capture 60 seconds of data, verify max latency <40ms
✓ Expected improvement: Consistent <50ms end-to-end latency
What's Included
- SKILL.md: Complete ROS control architecture framework with decision trees and step-by-step workflows
- Node design templates: Starter code for C++ and Python node types (publishers, subscribers, services, actions)
- Debugging checklists: Topic connectivity verification, service availability testing, frame transform validation
- PID tuning workflow: Systematic gain selection methodology with validation procedures
- tf configuration guide: Best practices for transform tree design, singularity detection, and coordinate frame validation
- Integration test templates: Example pytest and rostest scenarios for multi-node system verification
- Performance profiling guide: rosbag analysis techniques, rqt_graph interpretation, and CPU bottleneck identification
Who It's For
- Robotics software engineers — designing and maintaining distributed control systems
- Control systems engineers — optimizing motion performance and stability
- ROS middleware developers — debugging multi-node interactions and communication flows
- Autonomous vehicle developers — architecting real-time control stacks with strict timing requirements
- Manufacturing automation specialists — integrating multi-axis controllers with industrial robots
Best For
- Designing multi-node ROS control systems from architecture through deployment and testing
- Debugging communication bottlenecks and latency issues in distributed robotics systems
- Optimizing motor control loops and trajectory planning performance
- Setting up and validating tf (transform) frame hierarchies for complex kinematic chains
- Integrating custom motion controllers with ROS hardware interfaces and middleware
You might also like
Rapidly analyze production incidents by correlating observability signals—logs, metrics, traces, and events—to identify the true root cause instead of symptoms. You'll reconstruct incident timelines, quantify impact scope, and generate both immediate remediation steps and long-term prevention measures.
Claude analyzes your code diffs and generates standardized commit messages that follow the Conventional Commits specification. The skill automatically determines the correct commit type, scope, and description based on the changes you've made, ensuring your messages are parseable by automation tools while remaining human-readable for code reviewers.
You'll transform high-level feature requirements into a complete development plan covering API design, frontend implementation, testing strategy, and documentation. The skill helps you identify dependencies, parallelize work across frontend and backend teams, and establish quality gates that ensure features ship bug-free with proper error handling and logging.
Transform schema and data changes into production-ready migration packages that protect data integrity and minimize downtime. You can handle column additions, renames, type conversions, and backfills using proven patterns like expand-contract and shadow tables. The skill generates explicit rollback scripts, validation checklists, and phased execution plans that keep applications stable throughout the migration.
You can create comprehensive performance testing strategies tailored to your application's architecture, analyze test results to identify bottlenecks, and generate prioritized recommendations for optimization. The skill helps you design load tests, interpret metrics like latency and throughput, and create action plans for performance improvements.
You can design comprehensive performance test strategies tailored to your application architecture, analyze performance metrics and logs to identify bottlenecks, and receive specific optimization recommendations. The skill guides you through creating realistic load scenarios, interpreting performance data, and prioritizing fixes based on impact analysis.
You can submit your Solidity smart contracts for comprehensive security analysis, vulnerability detection, and gas optimization recommendations. Claude reviews your code against known attack vectors, design patterns, and best practices—identifying reentrancy risks, overflow/underflow issues, access control flaws, and inefficient operations. You receive actionable fix recommendations with explanations of the security implications and gas savings potential.
You can accelerate your entire antenna design workflow—from initial concept to optimized specifications—by leveraging Claude's knowledge of electromagnetic theory, antenna types, and design trade-offs. Claude helps you parameterize designs, identify optimal configurations for your frequency band and application, and prepare detailed briefs for simulation and manufacturing. You'll generate design reviews, impedance-matching solutions, and validation checklists without context-switching to multiple tools.