  The optical scanning gauges mounted on the robots are commonly used in
quality inspection, such as verifying the dimensional specification of sheet
structures. Coverage path planning (CPP) significantly influences the accuracy
and efficiency of robotic quality inspection. Traditional CPP strategies focus
on minimizing the number of viewpoints or traveling distance of robots under
the condition of full coverage inspection. The measurement uncertainty when
collecting the scanning data is less considered in the free-form surface
inspection. To address this problem, a novel CPP method with the optimal
viewpoint sampling strategy is proposed to incorporate the measurement
uncertainty of key measurement points (MPs) into free-form surface inspection.
At first, the feasible ranges of measurement uncertainty are calculated based
on the tolerance specifications of the MPs. The initial feasible viewpoint set
is generated considering the measurement uncertainty and the visibility of MPs.
Then, the inspection cost function is built to evaluate the number of selected
viewpoints and the average measurement uncertainty in the field of views (FOVs)
of all the selected viewpoints. Afterward, an enhanced rapidly-exploring random
tree (RRT*) algorithm is proposed for viewpoint sampling using the inspection
cost function and CPP optimization. Case studies, including simulation tests
and inspection experiments, have been conducted to evaluate the effectiveness
of the proposed method. Results show that the scanning precision of key MPs is
significantly improved compared with the benchmark method.
