About

Imported from personamanagmentlayer/pcl (stdlib/domains/robotics-expert/SKILL.md). Install upstream with npx skills add personamanagmentlayer/pcl --skill robotics-expert. Copyright stays with the author.

Robotics Expert

Name: robotics-expert
Author: personamanagmentlayer

Develop production-ready robotic systems with autonomous navigation, sensor fusion, and intelligent control for real-world applications.

Learning Objectives

Master ROS/ROS2 architecture and development
Implement autonomous navigation and path planning
Integrate sensors and actuators for robot control
Build SLAM systems for mapping and localization
Design control systems and kinematics

Prerequisites

Strong Python and C++ programming skills
Understanding of linear algebra and calculus
Knowledge of control theory fundamentals
Familiarity with Linux and embedded systems

Core Concepts

Robot Operating System (ROS/ROS2)

Middleware framework providing tools, libraries, and conventions for building robot applications. ROS2 offers improved real-time performance, security, and multi-robot support over ROS1.

SLAM (Simultaneous Localization and Mapping)

Algorithm enabling robots to build maps of unknown environments while tracking their position within those maps. Essential for autonomous navigation in dynamic environments.

Sensor Fusion

Integration of data from multiple sensors (lidar, cameras, IMU, encoders) to create reliable perception of robot's environment and state. Improves accuracy and robustness.

Motion Planning

Algorithms for computing collision-free paths from current position to goal. Includes global planning (A*, Dijkstra) and local planning (DWA, TEB) for dynamic obstacle avoidance.

Control Systems

Feedback control algorithms (PID, MPC) that translate desired robot behavior into motor commands. Ensures accurate trajectory following and stable operation.

Best Practices

ROS Development

Use ROS2 for new projects (improved architecture)
Follow REP guidelines for package structure
Implement proper lifecycle management
Use composition for node design
Leverage quality of service (QoS) profiles
Write comprehensive launch files
Maintain separation between logic and ROS interfaces

Sensor Integration

Implement sensor fusion for redundancy
Calibrate sensors properly (intrinsic, extrinsic)
Handle sensor failures gracefully
Use appropriate data structures (PointCloud2, Image)
Synchronize multi-sensor data with message filters
Apply noise filtering and outlier rejection
Monitor sensor health and diagnostics

Motion Planning

Use proven algorithms (RRT*, A*, DWA)
Implement both global and local planners
Consider kinematic and dynamic constraints
Handle dynamic obstacles
Optimize for computational efficiency
Provide smooth trajectories
Implement recovery behaviors

Safety & Reliability

Implement emergency stop mechanisms
Add watchdog timers for critical systems
Validate all sensor inputs
Use redundant safety sensors
Implement collision detection
Test failure modes extensively
Follow functional safety standards (ISO 13849)

Anti-Patterns

Common Mistakes

Not handling coordinate frame transformations
Ignoring timing and synchronization issues
Hardcoding parameters instead of using config files
Not implementing proper error handling
Blocking callbacks with long computations
Not tuning PID controllers properly
Inadequate testing in simulation

Design Issues

Monolithic nodes instead of modular design
Tight coupling between components
Not considering real-time constraints
Inadequate logging and diagnostics
Missing simulation environment
Not accounting for sensor noise and uncertainty
Poor cable management and mechanical design

Reference Documentation

Detailed material lives alongside this skill and is read on demand:

Code Examples — ROS2 Autonomous Robot Navigation System, Robot Kinematics and Inverse Kinematics, SLAM Implementation with Particle Filter

Resources

Frameworks & Middleware

ROS2 - Robot Operating System 2
ROS1 (legacy) - Original ROS
Gazebo - Robot simulation
MoveIt - Motion planning framework
Nav2 - Navigation stack
BehaviorTree.CPP - Behavior trees

Hardware Platforms

TurtleBot - Educational platform
Universal Robots - Collaborative arms
Boston Dynamics Spot - Quadruped
DJI Drones - Aerial platforms
NVIDIA Jetson - Edge AI compute
Arduino/Raspberry Pi - Embedded control

Sensors & Actuators

Velodyne/Ouster - Lidar sensors
Intel RealSense - Depth cameras
Sick/Hokuyo - 2D lidar
Dynamixel - Smart servos
MaxonMotor - High-performance motors
Mujoco - Physics simulation

Learning Resources

ROS2 Documentation
Modern Robotics textbook
Probabilistic Robotics (Thrun)
Planning Algorithms (LaValle)
The Construct - ROS courses
Robotics Stack Exchange

Part of the PCL Standard Library - Build intelligent autonomous systems that interact with the physical world.

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml

targets:
  - https://api.opensmartroute.ai/api/v1/registry/personamanagmentlayer-pcl-robotics-expert/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

personamanagmentlayer-pcl-robotics-expert.ocm.jsonjson

{
  "ocm": "1",
  "id": "personamanagmentlayer-pcl-robotics-expert",
  "kind": "skill",
  "name": "robotics-expert",
  "description": "Develop production-ready robotic systems with autonomous navigation, sensor fusion, and intelligent control for real-world applications. Use when the user mentions ROS or ROS 2, autonomous navigation, SLAM, motion planning, sensor fusion, or robot control systems.",
  "publisher": "personamanagmentlayer",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "general"
    ],
    "tags": [
      "skill-md",
      "robotics",
      "ros",
      "ros2",
      "autonomous-navigation",
      "sensors",
      "actuators",
      "slam",
      "motion-planning",
      "control-systems"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "Develop production-ready robotic systems with autonomous navigation, sensor fusion, and intelligent control for real-world applications. Use when the user mentions ROS or ROS 2, autonomous navigation, SLAM, motion planning, sensor fusion, or robot control systems."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "skills.sh",
      "repository": "https://github.com/personamanagmentlayer/pcl",
      "path": "stdlib/domains/robotics-expert/SKILL.md",
      "ref": "HEAD",
      "url": "https://github.com/personamanagmentlayer/pcl/blob/HEAD/stdlib/domains/robotics-expert/SKILL.md",
      "key": "personamanagmentlayer/pcl/stdlib/domains/robotics-expert/SKILL.md"
    },
    "allowed_tools": [
      "Read",
      "Write",
      "Bash",
      "Grep",
      "Glob"
    ]
  },
  "instructions": "# Robotics Expert\n\nDevelop production-ready robotic systems with autonomous navigation, sensor fusion, and intelligent control for real-world applications.\n\n## Learning Objectives\n\n- Master ROS/ROS2 architecture and development\n- Implement autonomous navigation and path planning\n- Integrate sensors and actuators for robot control\n- Build SLAM systems for mapping and localization\n- Design control systems and kinematics\n\n## Prerequisites\n\n- Strong Python and C++ programming skills\n- Understanding of linear algebra and calculus\n- Knowledge of control theory fundamentals\n- Familiarity with Linux a",
  "cost": {
    "context_tokens": 1210
  }
}

Fetch it by URL: GET /api/v1/registry/personamanagmentlayer-pcl-robotics-expert/manifest?version=1.0.0

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