Semi-Autonomous Teleoperation Using Differential Flatness of a Crane Robot for Aircraft In-Wing Inspection
Wade Marquette, Kyle Schultz, Vamsi Jonnalagadda, Benjamin Wong, Joseph Garbini, Santosh Devasia
AI summary
Problem
Inspecting aircraft wings requires mechanics to enter hazardous, confined spaces, while existing robotic solutions are cumbersome to reinstall per bay and difficult to teleoperate due to payload swing and collision risks in narrow channels.
Approach
The authors developed a compact crane robot that traverses the entire wing via stringer channels and a semi-autonomous controller that exploits differential flatness to automatically generate smooth, collision-free trajectories that compensate for payload dynamics in real time.
Key results
- Novel crane robot design enabling full-span wing traversal through narrow stringer channels
- Flatness-based controller reduces undesired payload oscillations by 89%
- Teleoperation collisions eliminated (reduced from 33% to 0%) across 12 user trials
- 18.7% improvement in task completion efficiency compared to unassisted control
Why it matters
Enables safer, faster, and more ergonomic remote inspection of aircraft wings, reducing the need for humans to enter hazardous confined spaces during aerospace manufacturing and maintenance.
Abstract
Visual inspection of confined spaces such as aircraft wings is ergonomically challenging for human mechanics. This work presents a novel crane robot that can travel the entire span of the aircraft wing, enabling mechanics to perform inspection from outside of the confined space. However, teleoperation of the crane robot can still be a challenge due to the need to avoid obstacles in the workspace and potential oscillations of the camera payload. The main contribution of this work is to exploit the differential flat- ness of the crane-robot dynamics for designing reduced-oscillation, collision-free time trajectories of the camera payload for use in teleoperation. Autonomous experiments verify the efficacy of re- moving undesired oscillations by 89%. Furthermore, teleoperation experiments demonstrate that the controller eliminated collisions (from 33% to 0%) when 12 participants performed an inspection task with the use of proposed trajectory selection when compared to the case without it. Moreover, even discounting the failures due to collisions, the proposed approach improved task efficiency by 18.7% when compared to the case without it.