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develop-robot-vision-navigation

Trace sensor-to-actuator feedback loops in robot vision and navigation systems.

Updated Jul 15, 2026
Search Tags:#codex#navigation#computer vision#robotics#slam#control systems
One-click install
npx skills add https://github.com/liukong1220/vision-nav_skill --skill develop-robot-vision-navigation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: develop-robot-vision-navigation
Source: https://github.com/liukong1220/vision-nav_skill/tree/main
Command: npx skills add https://github.com/liukong1220/vision-nav_skill --skill develop-robot-vision-navigation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the complexity of developing, debugging, and optimizing robot vision and navigation systems by providing a structured, evidence-based framework that treats both domains as equal, first-class engineering entities.

Core Features & Use Cases

  • Evidence-First Analysis: Provides a rigorous methodology to distinguish confirmed facts from hypotheses, ensuring that system changes are based on verifiable data rather than assumptions.
  • Closed-Loop Tracing: Enables developers to trace the entire sensor-to-actuator feedback loop, identifying bottlenecks in perception, estimation, planning, and control.
  • Use Case: When a robot fails to follow a target, use this Skill to audit the interface contracts between the vision tracker and the navigation planner, ensuring coordinate frames, timestamps, and QoS policies are correctly aligned.

Quick Start

Use the develop-robot-vision-navigation skill to analyze the current robot project and identify the first violated invariant in the vision-to-navigation integration chain.

Frequently Asked Questions about develop-robot-vision-navigation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I debug robot navigation failures caused by vision tracking issues?▼

Debug robot navigation failures by auditing interface contracts between the vision tracker and navigation planner, verifying that coordinate frames, timestamps, and QoS policies are correctly aligned.

What is the best way to trace sensor-to-actuator feedback loops in robotics?▼

Trace sensor-to-actuator feedback loops by analyzing the complete chain from visual perception and SLAM through trajectory optimization to control, identifying bottlenecks across the entire system.

How does evidence-based analysis improve SLAM and trajectory optimization?▼

Evidence-based analysis improves SLAM and trajectory optimization by distinguishing confirmed facts from hypotheses, ensuring system changes rely on verifiable data rather than assumptions.

Can I use this approach for staged real-robot deployment and cross-domain integration?▼

Yes, this approach satisfies requirements for staged real-robot deployment and cross-domain integration by verifying interface contracts and tracing the complete sensor-to-actuator feedback loop.

Why does my robot fail to follow a target despite correct control system commands?▼

A robot may fail to follow a target due to violated invariants in the vision-to-navigation integration chain, requiring an evidence-first audit of the sensor-to-actuator feedback loop.

Do I need specific hardware to optimize computer vision and navigation systems?▼

No specific hardware dependencies are required; the framework applies to general robotics projects requiring visual perception, SLAM, trajectory optimization, and cross-domain integration.

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