Sampling-based coverage motion planning for industrial inspection application with redundant robotic system

Wei, Jing; Polden, Joseph; Goh, Chun Fan; Rajaraman, M. K.; Lin, Wei; Shimada, Kenji

doi:10.1109/iros.2017.8206411

Cited by 18 publications

(10 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For a vision-based inspection experiment consisting of a clutter-free environment and 400 potential target points, the computation time was reported to be approximately 3 hours, with the majority of this time consumed by the computation of pose-to-pose motion plans. The authors later extended this work to redundant robotic systems [10], where multiple kinematic solutions exist for each target point. While computation times for this method were not reported, one could expect a computation time several folds greater than their initial work.…”

Section: Related Workmentioning

confidence: 99%

A Novel Clustering-Based Algorithm for Solving Spatially Constrained Robotic Task Sequencing Problems

Wong

Mineo

Yang

et al. 2021

IEEE/ASME Trans. Mechatron.

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

A Novel Clustering-Based Algorithm for Solving Spatially Constrained Robotic Task Sequencing Problems

Wong

Mineo

Yang

et al. 2021

IEEE/ASME Trans. Mechatron.

View full text Add to dashboard Cite

“…So later methods find only a set of viewpoints that satisfies the inspection requirements. Then the trajectory-planning task is usually formulated using variants of the Traveling Salesman Problem (TSP) [22], [23], [24], [25]. To improve the quality of the solution, some use trajectory optimization [26], [27] while others resample viewpoints [3], [4], or adaptively sample viewpoints [6].…”

Section: Related Workmentioning

confidence: 99%

Computationally-Efficient Roadmap-based Inspection Planning via Incremental Lazy Search

Salzman

Alterovitz

2021

Preprint

View full text Add to dashboard Cite

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 570× faster than prior work.

show abstract

“…2) Path Planning and Optimization with BRKGA: Random Key Genetic Algorithm (RKGA) is a meta-heuristic framework that uses random keys in the chromosome to encode the solution [33]. RKGA has been applied to many combinatorial optimization problems [34], [35], [36]. Using random keys is to add an encoding-decoding process to map the feasible space with complex constraints to an unconstrained one, which reduces the complexity of constraint handling in solving a constrained combinatorial optimization problem.…”

Section: B Planning and Optimizing The Inspection Pathmentioning

confidence: 99%

Multi-UAV Coverage Path Planning for the Inspection of Large and Complex Structures

Wei

Deng

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

We present a multi-UAV Coverage Path Planning (CPP) framework for the inspection of large-scale, complex 3D structures. In the proposed sampling-based coverage path planning method, we formulate the multi-UAV inspection applications as a multi-agent coverage path planning problem. By combining two NP-hard problems: Set Covering Problem (SCP) and Vehicle Routing Problem (VRP), a Set-Covering Vehicle Routing Problem (SC-VRP) is formulated and subsequently solved by a modified Biased Random Key Genetic Algorithm (BRKGA) with novel, efficient encoding strategies and local improvement heuristics. We test our proposed method for several complex 3D structures with the 3D model extracted from OpenStreetMap. The proposed method outperforms previous methods, by reducing the length of the planned inspection path by up to 48%.

show abstract

Sampling-based coverage motion planning for industrial inspection application with redundant robotic system

Cited by 18 publications

References 23 publications

A Novel Clustering-Based Algorithm for Solving Spatially Constrained Robotic Task Sequencing Problems

A Novel Clustering-Based Algorithm for Solving Spatially Constrained Robotic Task Sequencing Problems

Computationally-Efficient Roadmap-based Inspection Planning via Incremental Lazy Search

Multi-UAV Coverage Path Planning for the Inspection of Large and Complex Structures

Contact Info

Product

Resources

About