  An important open problem in robotic planning is the autonomous generation of
3D inspection paths -- that is, planning the best path to move a robot along in
order to inspect a target structure. We recently suggested a new method for
planning paths allowing the inspection of complex 3D structures, given a
triangular mesh model of the structure. The method differs from previous
approaches in its emphasis on generating and considering also plans that result
in imperfect coverage of the inspection target. In many practical tasks, one
would accept imperfections in coverage if this results in a substantially more
energy efficient inspection path. The key idea is using a multiobjective
evolutionary algorithm to optimize the energy usage and coverage of inspection
plans simultaneously - and the result is a set of plans exploring the different
ways to balance the two objectives. We here test our method on a set of
inspection targets with large variation in size and complexity, and compare its
performance with two state-of-the-art methods for complete coverage path
planning. The results strengthen our confidence in the ability of our method to
generate good inspection plans for different types of targets. The method's
advantage is most clearly seen for real-world inspection targets, since
traditional complete coverage methods have no good way of generating plans for
structures with hidden parts. Multiobjective evolution, by optimizing energy
usage and coverage together ensures a good balance between the two - both when
100% coverage is feasible, and when large parts of the object are hidden.
