16 research outputs found
Sampling-Based Coverage Path Planning for Inspection of Complex Structures
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
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
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
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 ์ปค๋ฒ๋ฆฌ์ง ๊ฒฝ๋ก ๊ณํ์ ์ํ ํจ์จ์ ์๊ณ ๋ฆฌ์ฆ
ํ์๋
ผ๋ฌธ (์์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ๊ธฐ๊ณ๊ณตํ๋ถ, 2020. 8. ๋ฐ์ข
์ฐ.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 ๋
ผ๋ฌธ์์๋ 2์ฐจ์ ํ๋ฉด์ ๊ท ์ผ ์ปค๋ฒ๋ฆฌ์ง ๊ฒฝ๋ก ๊ณํ์ ์ค๋ช
ํ๊ณ ์ด๋ฅผ ํจ์จ์ ์ผ๋ก ํธ๋ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ์ฐ๋ฆฌ๋ ๊ฒฝ๋ก ๊ณํ ๋ฌธ์ ๋ฅผ ๋ ๊ฐ์ ํ์ ๋ฌธ์ ๋ก ๋ถ๋ฆฌํ์ฌ ๊ฐ๊ฐ ํธ๋ ๊ธฐ์กด์ ๋ฐฉ์์ ๋ณด์ํ์ฌ ๋ ๊ฐ์ ํ์๋ฌธ์ ๋ฅผ ํ ๋ฒ์ ํ๋ฉด์ ๊ณ์ฐ์๊ฐ์ ์ค์ด๋ ๋ฐฉ๋ฒ์ ์ ์ํ์๋ค. ๋ํ ๊ฒฝ์ฐ์ ๋ฐ๋ผ ์ฃผ์ด์ง ๋ฒกํฐ ํ๋์ ๋๋ํ ๋ฐฉํฅ์ผ๋ก ๊ฒฝ๋ก๊ฐ ์์ฑ๋ ํ์๊ฐ ์๋๋ฐ ์ด๋ฅผ ์ํด ๊ฑฐ์ค๋ฆ ๊ฑฐ๋ฆฌ(upstream distance)์ ๊ฐ๋
์ ์ ์ํ์์ผ๋ฉฐ ์ฌํ ์ธํ์ ๋ฌธ์ (Traveling Salesman Problem)๋ฅผ ํ ๋ ์ด๋ฅผ ์ ์ฉํ์๋ค. ์ฐ๋ฆฌ๋ ์ฐจ๋ ๋์ฅ ์์ฉ๋ถ์ผ์ ๊ท ์ผ ์ปค๋ฒ๋ฆฌ์ง ๊ฒฝ๋ก ๊ณํ๋ฒ์ ์ ์ฉํ์์ผ๋ฉฐ ๋์ฅ ์์คํ
์ ๊ณ ๋ คํ์ฌ ๊ท ์ผํ ํ์ธํธ ๋๊ป๋ฅผ ๋ณด์ฅํ๋ ๋ฐฉ๋ฒ์ ๊ฐ์ด ์ ์ํ์๋ค. ๋ค ๊ฐ์ง ํ์
์ 2์ฐจ์ ๊ณก๋ฉด์ ๋ํด ์๋ฎฌ๋ ์ด์
์ ์งํํ์์ผ๋ฉฐ ๊ธฐ์กด์ ๋ฐฉ๋ฒ์ ๋นํด ๋ ์ ์ ๊ณ์ฐ์๊ฐ์ ์๊ตฌํ๋ฉด์๋ ํฉ๋ฆฌ์ ์ธ ์์ค์ ํ์ธํธ ๊ท ์ผ๋๋ฅผ ๋ฌ์ฑํจ์ ๊ฒ์ฆํ์๋ค.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
๊ตญ๋ฌธ์ด๋ก 52Maste
Advanced perception, navigation and planning for autonomous in-water ship hull inspection
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