[Paper Review] Computationally-Efficient Roadmap-based Inspection Planning via Incremental Lazy Search

Mengyu Fu, Oren Salzman+1 more|arXiv (Cornell University)|Mar 25, 2021

Robotic Path Planning Algorithms50 references6 citations

TL;DR

This paper proposes IRIS_CLI, an enhanced version of the Incremental Random Inspection-roadmap Search (IRIS) algorithm, which accelerates computationally efficient roadmap-based inspection planning via coverage-informed sampling and incremental lazy search with search effort reuse. The method achieves up to 570× speedup in reaching 60% inspection coverage while maintaining asymptotic optimality, significantly outperforming prior approaches in bridge and surgical inspection simulations.

ABSTRACT

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.

Motivation & Objective

• To address the high computational cost of roadmap-based inspection planning, especially during graph search as roadmaps grow denser.
• To improve the efficiency of IRIS, an asymptotically-optimal inspection planner, without sacrificing solution quality guarantees.
• To reduce redundant computation in graph search by reusing prior search efforts when the roadmap undergoes local changes.
• To accelerate roadmap construction by prioritizing samples that increase inspection coverage of previously unseen points of interest (POIs).
• To enable faster, high-quality inspection plans for time-sensitive applications such as medical endoscopy and UAV-based infrastructure inspection.

Proposed method

• Introduces coverage-informed sampling during roadmap construction to reduce the number of samples that fail to inspect new POIs, thereby creating more informative and compact roadmaps.
• Employs refined lazy edge-evaluation techniques to defer expensive edge validation until necessary during graph search, reducing redundant computation.
• Reuses search data structures and partial search results when the roadmap is incrementally updated, avoiding full recomputation from scratch.
• Maintains asymptotic optimality by ensuring that the search space remains fully explored as the roadmap densifies over time.
• Integrates these techniques into the IRIS framework, enabling faster convergence to high-quality inspection plans.
• Uses a configuration space model for flexible robots (e.g., CRISP) with expensive forward kinematics, making efficient search critical.

Experimental results

Research questions

• RQ1 Can lazy edge evaluation and search effort reuse significantly reduce the time spent on graph search in roadmap-based inspection planning?
• RQ2 How does coverage-informed sampling improve roadmap quality and reduce redundant sampling during roadmap construction?
• RQ3 To what extent can incremental search reuse and lazy evaluation accelerate convergence to high-quality inspection plans without losing optimality?
• RQ4 How does the performance of the enhanced IRIS algorithm compare to the original IRIS and RRTOT in real-world inspection scenarios?
• RQ5 Can the proposed method achieve substantial speedups in time-sensitive applications like medical endoscopy and UAV bridge inspection?

Key findings

• IRIS_CLI achieves 570× speedup over prior work in reaching 60% inspection coverage in bridge inspection tasks, while maintaining similar plan quality.
• In pleural cavity inspection, IRIS_CLI reaches 12% more coverage than the original IRIS and is 6× faster to achieve 67% coverage.
• IRIS_CLI is over 3,200× faster than the original IRIS in graph search time at 67% coverage in the surgical scenario.
• The algorithm achieves 23% more inspection coverage than the original IRIS at the same computation time, demonstrating improved efficiency.
• IRIS_CLI outperforms RRTOT by 44% in coverage and 830× in search efficiency at 60% coverage in bridge inspection.
• The combination of coverage-informed sampling and incremental lazy search enables significant speedups without compromising asymptotic optimality.