16 research outputs found

    Sampling-Based Coverage Path Planning for Inspection of Complex Structures

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    We present several new contributions in sampling-based coverage path planning, the task of finding feasible paths that give 100% sensor coverage of complex structures in obstacle-filled and visually occluded environments. First, we establish a framework for analyzing the probabilistic completeness of a sampling-based coverage algorithm, and derive results on the completeness and convergence of existing algorithms. Second, we introduce a new algorithm for the iterative improvement of a feasible coverage path; this relies on a sampling-based subroutine that makes asymptotically optimal local improvements to a feasible coverage path based on a strong generalization of the RRT algorithm. We then apply the algorithm to the real-world task of autonomous in-water ship hull inspection. We use our improvement algorithm in conjunction with redundant roadmap coverage planning algorithm to produce paths that cover complex 3D environments with unprecedented efficiency.United States. Office of Naval Research (ONR Grant N0014-06-10043

    A Co-optimal Coverage Path Planning Method for Aerial Scanning of Complex Structures

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    The utilization of unmanned aerial vehicles (UAVs) in survey and inspection of civil infrastructure has been growing rapidly. However, computationally efficient solvers that find optimal flight paths while ensuring high-quality data acquisition of the complete 3D structure remains a difficult problem. Existing solvers typically prioritize efficient flight paths, or coverage, or reducing computational complexity of the algorithm โ€“ but these objectives are not co-optimized holistically. In this work we introduce a co-optimal coverage path planning (CCPP) method that simultaneously co-optimizes the UAV path, the quality of the captured images, and reducing computational complexity of the solver all while adhering to safety and inspection requirements. The result is a highly parallelizable algorithm that produces more efficient paths where quality of the useful image data is improved. The path optimization algorithm utilizes a particle swarm optimization (PSO) framework which iteratively optimizes the coverage paths without needing to discretize the motion space or simplify the sensing models as is done in similar methods. The core of the method consists of a cost function that measures both the quality and efficiency of a coverage inspection path, and a greedy heuristic for the optimization enhancement by aggressively exploring the viewpoints search spaces. To assess the proposed method, a coverage path quality evaluation method is also presented in this research, which can be utilized as the benchmark for assessing other CPP methods for structural inspection purpose. The effectiveness of the proposed method is demonstrated by comparing the quality and efficiency of the proposed approach with the state-of-art through both synthetic and real-world scenes. The experiments show that our method enables significant performance improvement in coverage inspection quality while preserving the path efficiency on different test geometries

    Online Informative Path Planning for Active Information Gathering of a 3D Surface

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    This paper presents an online informative path planning approach for active information gathering on three-dimensional surfaces using aerial robots. Most existing works on surface inspection focus on planning a path offline that can provide full coverage of the surface, which inherently assumes the surface information is uniformly distributed hence ignoring potential spatial correlations of the information field. In this paper, we utilize manifold Gaussian processes (mGPs) with geodesic kernel functions for mapping surface information fields and plan informative paths online in a receding horizon manner. Our approach actively plans information-gathering paths based on recent observations that respect dynamic constraints of the vehicle and a total flight time budget. We provide planning results for simulated temperature modeling for simple and complex 3D surface geometries (a cylinder and an aircraft model). We demonstrate that our informative planning method outperforms traditional approaches such as 3D coverage planning and random exploration, both in reconstruction error and information-theoretic metrics. We also show that by taking spatial correlations of the information field into planning using mGPs, the information gathering efficiency is significantly improved.Comment: 7 pages, 7 figures, to be published in 2021 IEEE International Conference on Robotics and Automation (ICRA

    End-to-end Reinforcement Learning for Online Coverage Path Planning in Unknown Environments

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    Coverage path planning is the problem of finding the shortest path that covers the entire free space of a given confined area, with applications ranging from robotic lawn mowing and vacuum cleaning, to demining and search-and-rescue tasks. While offline methods can find provably complete, and in some cases optimal, paths for known environments, their value is limited in online scenarios where the environment is not known beforehand, especially in the presence of non-static obstacles. We propose an end-to-end reinforcement learning-based approach in continuous state and action space, for the online coverage path planning problem that can handle unknown environments. We construct the observation space from both global maps and local sensory inputs, allowing the agent to plan a long-term path, and simultaneously act on short-term obstacle detections. To account for large-scale environments, we propose to use a multi-scale map input representation. Furthermore, we propose a novel total variation reward term for eliminating thin strips of uncovered space in the learned path. To validate the effectiveness of our approach, we perform extensive experiments in simulation with a distance sensor, surpassing the performance of a recent reinforcement learning-based approach

    2์ฐจ์› ๊ท ์ผ ์ปค๋ฒ„๋ฆฌ์ง€ ๊ฒฝ๋กœ ๊ณ„ํš์„ ์œ„ํ•œ ํšจ์œจ์  ์•Œ๊ณ ๋ฆฌ์ฆ˜

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„๊ณตํ•™๋ถ€, 2020. 8. ๋ฐ•์ข…์šฐ.Coverage path planning (CPP) is widely used in numerous robotic applications. With progressively complex and extensive applications of CPP, automating the planning process has become increasingly important. This thesis proposes an efficient CPP algorithm based on a random sampling scheme for spray painting applications. We have improved on the conventional CPP algorithm by alternately iterating the path generation and node sampling steps. This method can reduce the computational time by reducing the number of sampled nodes. We also suggest a new distance metric called upstream distance to generate reasonable path following given vector field. This induces the path to be aligned with a desired direction. Additionally, one of the machine learning techniques, support vector regression (SVR) is utilized to identify the paint distribution model. This method accurately predict the paint distribution model as a function of the painting parameters. We demonstrate our algorithm on several types of analytic surfaces and compare the results with those of conventional methods. Experiments are conducted to assess the performance of our approach compared to the traditional method.๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” 2์ฐจ์› ํ‘œ๋ฉด์˜ ๊ท ์ผ ์ปค๋ฒ„๋ฆฌ์ง€ ๊ฒฝ๋กœ ๊ณ„ํš์„ ์„ค๋ช…ํ•˜๊ณ  ์ด๋ฅผ ํšจ์œจ์ ์œผ๋กœ ํ‘ธ๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์‹œํ•œ๋‹ค. ์šฐ๋ฆฌ๋Š” ๊ฒฝ๋กœ ๊ณ„ํš ๋ฌธ์ œ๋ฅผ ๋‘ ๊ฐœ์˜ ํ•˜์œ„ ๋ฌธ์ œ๋กœ ๋ถ„๋ฆฌํ•˜์—ฌ ๊ฐ๊ฐ ํ‘ธ๋Š” ๊ธฐ์กด์˜ ๋ฐฉ์‹์„ ๋ณด์™„ํ•˜์—ฌ ๋‘ ๊ฐœ์˜ ํ•˜์œ„๋ฌธ์ œ๋ฅผ ํ•œ ๋ฒˆ์— ํ’€๋ฉด์„œ ๊ณ„์‚ฐ์‹œ๊ฐ„์„ ์ค„์ด๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๋˜ํ•œ ๊ฒฝ์šฐ์— ๋”ฐ๋ผ ์ฃผ์–ด์ง„ ๋ฒกํ„ฐ ํ•„๋“œ์™€ ๋‚˜๋ž€ํ•œ ๋ฐฉํ–ฅ์œผ๋กœ ๊ฒฝ๋กœ๊ฐ€ ์ƒ์„ฑ๋  ํ•„์š”๊ฐ€ ์žˆ๋Š”๋ฐ ์ด๋ฅผ ์œ„ํ•ด ๊ฑฐ์Šค๋ฆ„ ๊ฑฐ๋ฆฌ(upstream distance)์˜ ๊ฐœ๋…์„ ์ œ์‹œํ•˜์˜€์œผ๋ฉฐ ์—ฌํ–‰ ์™ธํŒ์› ๋ฌธ์ œ(Traveling Salesman Problem)๋ฅผ ํ’€ ๋•Œ ์ด๋ฅผ ์ ์šฉํ•˜์˜€๋‹ค. ์šฐ๋ฆฌ๋Š” ์ฐจ๋Ÿ‰ ๋„์žฅ ์‘์šฉ๋ถ„์•ผ์— ๊ท ์ผ ์ปค๋ฒ„๋ฆฌ์ง€ ๊ฒฝ๋กœ ๊ณ„ํš๋ฒ•์„ ์ ์šฉํ•˜์˜€์œผ๋ฉฐ ๋„์žฅ ์‹œ์Šคํ…œ์„ ๊ณ ๋ คํ•˜์—ฌ ๊ท ์ผํ•œ ํŽ˜์ธํŠธ ๋‘๊ป˜๋ฅผ ๋ณด์žฅํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ๊ฐ™์ด ์ œ์‹œํ•˜์˜€๋‹ค. ๋„ค ๊ฐ€์ง€ ํƒ€์ž…์˜ 2์ฐจ์› ๊ณก๋ฉด์— ๋Œ€ํ•ด ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ์ง„ํ–‰ํ•˜์˜€์œผ๋ฉฐ ๊ธฐ์กด์˜ ๋ฐฉ๋ฒ•์— ๋น„ํ•ด ๋” ์ ์€ ๊ณ„์‚ฐ์‹œ๊ฐ„์„ ์š”๊ตฌํ•˜๋ฉด์„œ๋„ ํ•ฉ๋ฆฌ์ ์ธ ์ˆ˜์ค€์˜ ํŽ˜์ธํŠธ ๊ท ์ผ๋„๋ฅผ ๋‹ฌ์„ฑํ•จ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค.1 Introduction 1 1.1 Related Work 3 1.2 Contribution of Our Work 7 1.3 Organization of This Thesis 8 2 Preliminary Background 9 2.1 Elementary Differential Geometry of Surfaces in R3 10 2.1.1 Representation of Surfaces 10 2.1.2 Normal Curvature 10 2.1.3 Shape Operator 12 2.2 Traveling Salesman Problem 15 2.2.1 Definition 15 2.2.2 Variations of the TSP 17 2.2.3 Approximation Algorithm for TSP 19 2.3 Path Planning on Vector Fields 20 2.3.1 Randomized Path Planning 20 2.3.2 Upstream Criterion 20 2.4 Support Vector Regression 21 2.4.1 Single-Output SVR 21 2.4.2 Dual Problem of SVR 23 2.4.3 Kernel for Nonlinear System 25 2.4.4 Multi-Output SVR 26 3 Methods 29 3.1 Efficient Coverage Path Planning on Vector Fields 29 3.1.1 Efficient Node Sampling 31 3.1.2 Divide and Conquer Strategy 32 3.1.3 Upstream Distance 34 3.2 Uniform Coverage Path Planning in Spray Painting Applications 35 3.2.1 Minimum Curvature Direction 35 3.2.2 Learning Paint Deposition Model 36 4 Results 38 4.1 Experimental Setup 38 4.2 Simulation Result 41 4.3 Discussion 41 5 Conclusion 45 Bibliography 47 ๊ตญ๋ฌธ์ดˆ๋ก 52Maste

    Advanced perception, navigation and planning for autonomous in-water ship hull inspection

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    Inspection of ship hulls and marine structures using autonomous underwater vehicles has emerged as a unique and challenging application of robotics. The problem poses rich questions in physical design and operation, perception and navigation, and planning, driven by difficulties arising from the acoustic environment, poor water quality and the highly complex structures to be inspected. In this paper, we develop and apply algorithms for the central navigation and planning problems on ship hulls. These divide into two classes, suitable for the open, forward parts of a typical monohull, and for the complex areas around the shafting, propellers and rudders. On the open hull, we have integrated acoustic and visual mapping processes to achieve closed-loop control relative to features such as weld-lines and biofouling. In the complex area, we implemented new large-scale planning routines so as to achieve full imaging coverage of all the structures, at a high resolution. We demonstrate our approaches in recent operations on naval ships.United States. Office of Naval Research (Grant N00014-06-10043)United States. Office of Naval Research (Grant N00014-07-1-0791
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