An AI agent skill that enables real-time Lidar obstacle detection and collision warnings by connecting to ROS2 services on a Raspberry Pi.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install radar-collision-warning
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install radar-collision-warning using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Radar Collision Warning?
The Radar Collision Warning skill provides a critical bridge between AI agents and the physical world. By utilizing Openclaw Skills, this tool allows your agent to interact directly with ROS2 (Robot Operating System) environments. It communicates with a Raspberry Pi via the rosbridge WebSocket protocol to request data from Lidar sensors, specifically designed to detect obstacles and prevent collisions.
This skill is ideal for developers working on robotics, smart environments, or automated safety systems. It enables an agent to either perform a point-in-time distance check or run in a continuous monitoring mode, returning structured data that the AI can use to make safety-critical decisions or inform the user of nearby hazards.
Radar Collision Warning Use Cases
- Real-time obstacle detection for DIY mobile robots and autonomous platforms.
- Automated safety monitoring for laboratory or smart home environments.
- Remote physical proximity sensing using voice or text commands through Openclaw Skills.
- Integration of Lidar data into complex automated workflows for spatial awareness.
How Radar Collision Warning Works
- The AI agent triggers the skill script, passing optional parameters like distance thresholds.
- The skill establishes a WebSocket connection to a rosbridge_server running on a networked Raspberry Pi.
- A ROS2 Service request is dispatched to the /radar_collision_warning endpoint.
- The ROS2 node on the Raspberry Pi processes incoming /scan (LaserScan) data from the Lidar hardware.
- The nearest obstacle distance is calculated and compared against the safety threshold.
- The result is returned as a JSON object containing distance metrics, a warning boolean, and status messages for the agent to process.
Radar Collision Warning Setup
Configure your Raspberry Pi connection details in ~/.openclaw/workspace/TOOLS.md or via environment variables:
export RADAR_HOST=192.168.137.100 # Raspberry Pi IP
export RADAR_PORT=8080 # rosbridge port
To perform a single distance check:
node ~/.openclaw/workspace/skills/radar-collision-warning/scripts/call_radar_service.js
To enable continuous background monitoring:
node ~/.openclaw/workspace/skills/radar-collision-warning/scripts/call_radar_service.js --monitor --interval 2.0
Radar Collision Warning Data Schema & Taxonomy
The skill returns a structured JSON payload for every request made through Openclaw Skills:
| Property | Type | Description |
|---|---|---|
| success | boolean | Whether the service call succeeded |
| distance_m | number | Nearest obstacle distance in meters |
| distance_cm | number | Nearest obstacle distance in centimeters |
| warning | boolean | True if the distance is below the safety threshold |
| message | string | A human-readable status or warning message |
| threshold_m | number | The current safety threshold setting |
Radar Collision Warning Advanced Features
- Continuous Monitoring Mode: Run the skill in the background with customizable polling intervals for persistent safety oversight.
- Dynamic Thresholding: Override default collision distances (5cm) on the fly using CLI arguments to suit different environments.
- Zero-Dependency Client: Built using Node.js 18+ native WebSockets, ensuring lightweight execution without additional package overhead.
- ROS2 Service Integration: Seamlessly interacts with standard ROS2 service architectures, making it compatible with a wide range of Lidar hardware supported by the ROS ecosystem.
SKILL.md
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