  Autonomous multi-robot optical inspection systems are increasingly applied
for obtaining inline measurements in process monitoring and quality control.
Numerous methods for path planning and robotic coordination have been developed
for static and dynamic environments and applied to different fields. However,
these approaches may not work for the autonomous multi-robot optical inspection
system due to fast computation requirements of inline optimization, unique
characteristics on robotic end-effector orientations, and complex large-scale
free-form product surfaces. This paper proposes a novel task allocation
methodology for coordinated motion planning of multi-robot inspection.
Specifically, (1) a local robust inspection task allocation is proposed to
achieve efficient and well-balanced measurement assignment among robots; (2)
collision-free path planning and coordinated motion planning are developed via
dynamic searching in robotic coordinate space and perturbation of probe poses
or local paths in the conflicting robots. A case study shows that the proposed
approach can mitigate the risk of collisions between robots and environments,
resolve conflicts among robots, and reduce the inspection cycle time
significantly and consistently.
