High-risk spaces are hard to inspect
No GPS, poor lighting, confined structures, weak signal and high access risk make conventional inspection inefficient or unsafe. Follow-up can also fragment after a defect is found.
Proof case · CTO role
YueTie Air-Ground Robotics
A CTO role shaping air-ground collaborative robotics for confined spaces, infrastructure inspection, emergency response, industrial scenarios and safety-critical deployment logic.
Case summary
Perception-linked control concept
AINO agent orchestration, no-GPS SLAM, multi-sensor collision avoidance, AI defect recognition, 3D modelling and inventory workflows connect sensing to action.
Safety robotics operating model
The CTO role moves robotics from a demonstration mindset toward scenario libraries, field workflows and a management loop that can alert, assign, verify and archive work.
From inspection to closed-loop response
Map difficult environments
No-GPS SLAM supports positioning and real-time 3D mapping in dark, enclosed and signal-poor spaces. Multi-sensor collision avoidance supports navigation through narrow and structurally complex areas.
Connect agents to field work
The AINO intelligent agent engine is described as orchestrating alerts, work orders, intervention, verification, archiving and PDF reporting. Air-ground collaboration brings drones, wheeled platforms, robotic dogs and arms into one scenario model.
Make evidence more usable
AI imaging can support recognition of cracks, corrosion, leakage and deformation. The scenario library is described as covering 8 application domains and 31 use cases, including infrastructure, rail, energy, industrial and emergency work.
Evidence and limits
Alvin Liao
Author and practitioner: Alvin Liao, described in the public portfolio as CTO for the YueTie Technology AI and Robotics team.
20 August 2026
This public proof-case summary was updated on 20 August 2026. The linked scenario page provides the broader application-library context.
Scenario direction, not outcome data
The evidence supports the role, capabilities and scenario library described in the portfolio. No field-performance metric, deployment approval or independent validation is claimed here.
Robotics still needs governance
Competent operators, procedures, emergency controls, maintenance, cybersecurity awareness, privacy controls and human validation of AI-recognised defects remain necessary.
Related reading and service
Read How do air-ground robots change safety inspection workflows? for the operating model, then explore AI Safety Consultant for field-aware AI and safety-critical implementation support.