  The inspection-planning problem calls for computing motions for a robot that
allow it to inspect a set of points of interest (POIs) while considering plan
quality (e.g., plan length). This problem has applications across many domains
where robots can help with inspection, including infrastructure maintenance,
construction, and surgery. Incremental Random Inspection-roadmap Search (IRIS)
is an asymptotically-optimal inspection planner that was shown to compute
higher-quality inspection plans orders of magnitudes faster than the prior
state-of-the-art method. In this paper, we significantly accelerate the
performance of IRIS to broaden its applicability to more challenging real-world
applications. A key computational challenge that IRIS faces is effectively
searching roadmaps for inspection plans -- a procedure that dominates its
running time. In this work, we show how to incorporate lazy edge-evaluation
techniques into \iris's search algorithm and how to reuse search efforts when a
roadmap undergoes local changes. These enhancements, which do not compromise
IRIS's asymptotic optimality, enable us to compute inspection plans much faster
than the original IRIS. We apply IRIS with the enhancements to simulated bridge
inspection and surgical inspection tasks and show that our new algorithm for
some scenarios can compute similar-quality inspection plans 570x faster than
prior work.
