UAS-based Automated Structural Inspection Path Planning via Visual Data Analytics and Optimization

• URL: http://arxiv.org/abs/2312.15109v1
• Date: Fri, 22 Dec 2023 23:07:20 GMT
• Title: UAS-based Automated Structural Inspection Path Planning via Visual Data Analytics and Optimization
• Authors: Yuxiang Zhao, Benhao Lu, Mohamad Alipour
• Abstract summary: Unmanned Aerial Systems (UAS) have gained significant traction for their application in infrastructure inspections. One of the core problems in this regard is electing an optimal automated flight path. This paper presents an effective formulation for the path planning problem in the context of structural inspections.
• Score: 1.1496057626375067
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unmanned Aerial Systems (UAS) have gained significant traction for their application in infrastructure inspections. However, considering the enormous scale and complex nature of infrastructure, automation is essential for improving the efficiency and quality of inspection operations. One of the core problems in this regard is electing an optimal automated flight path that can achieve the mission objectives while minimizing flight time. This paper presents an effective formulation for the path planning problem in the context of structural inspections. Coverage is guaranteed as a constraint to ensure damage detectability and path length is minimized as an objective, thus maximizing efficiency while ensuring inspection quality. A two-stage algorithm is then devised to solve the path planning problem, composed of a genetic algorithm for determining the positions of viewpoints and a greedy algorithm for calculating the poses. A comprehensive sensitivity analysis is conducted to demonstrate the proposed algorithm's effectiveness and range of applicability. Applied examples of the algorithm, including partial space inspection with no-fly zones and focused inspection, are also presented, demonstrating the flexibility of the proposed method to meet real-world structural inspection requirements. In conclusion, the results of this study highlight the feasibility of the proposed approach and establish the groundwork for incorporating automation into UAS-based structural inspection mission planning.

Related papers

• A Distance Similarity-based Genetic Optimization Algorithm for Satellite Ground Network Planning Considering Feeding Mode [53.71516191515285]
  The low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system. We propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem.
  arXiv  Detail & Related papers  (2024-08-29T06:57:45Z)
• Research on an Autonomous UAV Search and Rescue System Based on the Improved [1.3399503792039942]
  This paper proposes an autonomous search and rescue UAV system based on an EGO-Planner algorithm. It takes the methods of inverse motor backstepping to enhance the overall flight efficiency of the UAV and miniaturization of the whole machine. At the same time, the system introduced the EGO-Planner planning tool, which is optimized by a bidirectional A* algorithm along with an object detection algorithm.
  arXiv  Detail & Related papers  (2024-06-01T17:25:29Z)
• Acquiring Better Load Estimates by Combining Anomaly and Change Point Detection in Power Grid Time-series Measurements [0.49478969093606673]
  Our approach prioritizes interpretability while ensuring robust and generalizable performance on unseen data. Results indicate the clear wasted potential when filtering is not applied. Our methodology's interpretability makes it particularly suitable for critical infrastructure planning.
  arXiv  Detail & Related papers  (2024-05-25T10:15:51Z)
• Airport take-off and landing optimization through genetic algorithms [55.2480439325792]
  This research addresses the crucial issue of pollution from aircraft operations, focusing on optimizing both gate allocation and runway scheduling simultaneously. The study presents an innovative genetic algorithm-based method for minimizing pollution from fuel combustion during aircraft take-off and landing at airports.
  arXiv  Detail & Related papers  (2024-02-29T14:53:55Z)
• An Improved Artificial Fish Swarm Algorithm for Solving the Problem of Investigation Path Planning [8.725702964289479]
  We propose a chaotic artificial fish swarm algorithm based on multiple population differential evolution (DE-CAFSA) We incorporate adaptive field of view and step size adjustments, replace random behavior with the 2-opt operation, and introduce chaos theory and sub-optimal solutions. Experimental results demonstrate that DE-CAFSA outperforms other algorithms on various public datasets of different sizes.
  arXiv  Detail & Related papers  (2023-10-20T09:35:51Z)
• PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback [106.63518036538163]
  We present a novel unified bilevel optimization-based framework, textsfPARL, formulated to address the recently highlighted critical issue of policy alignment in reinforcement learning. Our framework addressed these concerns by explicitly parameterizing the distribution of the upper alignment objective (reward design) by the lower optimal variable. Our empirical results substantiate that the proposed textsfPARL can address the alignment concerns in RL by showing significant improvements.
  arXiv  Detail & Related papers  (2023-08-03T18:03:44Z)
• Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks [70.76757529955577]
  This paper revisits prior work in this scope from the perspective of state-wise safe RL. We propose Unrolling Safety Layer (USL), a joint method that combines safety optimization and safety projection. To facilitate further research in this area, we reproduce related algorithms in a unified pipeline and incorporate them into SafeRL-Kit.
  arXiv  Detail & Related papers  (2022-12-12T06:30:17Z)
• Assembly Planning from Observations under Physical Constraints [65.83676649042623]
  The proposed algorithm uses a simple combination of physical stability constraints, convex optimization and Monte Carlo tree search to plan assemblies. It is efficient and, most importantly, robust to the errors in object detection and pose estimation unavoidable in any real robotic system.
  arXiv  Detail & Related papers  (2022-04-20T16:51:07Z)
• Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation [28.104584236205405]
  A key challenge is planning missions to maximize the value of acquired data in large environments. This is, for example, relevant for monitoring agricultural fields. We propose an online planning algorithm which adapts the UAV paths to obtain high-resolution semantic segmentations.
  arXiv  Detail & Related papers  (2022-03-03T11:03:28Z)
• Optimal Inspection and Maintenance Planning for Deteriorating Structural Components through Dynamic Bayesian Networks and Markov Decision Processes [0.0]
  Partially Observable Markov Decision Processes (POMDPs) provide a mathematical methodology for optimal control under uncertain action outcomes and observations. We provide the formulation for developing both infinite and finite horizon POMDPs in a structural reliability context. Results show that POMDPs achieve substantially lower costs as compared to their counterparts, even for traditional problem settings.
  arXiv  Detail & Related papers  (2020-09-09T20:03:42Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.