Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR

Faria, Margarida; Ferreira, António Sérgio; Pérez-León, Héctor; Maza, Iván; Viguria, Antidio

doi:10.3390/s19224849

Cited by 13 publications

(7 citation statements)

References 57 publications

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“…This work collected informative and useful data from deep mines using a LiDAR system, assembled with visual and thermal cameras as well as an inertial measurement unit. The work presented in [18] used an unmanned aerial vehicle (UAV) equipped with a 2D LiDAR system for exploring complex environments. Their system was developed and evaluated based on a hardware-in-the-loop (HitL) simulation environment.…”

Section: Cmes 2023 2 Related Workmentioning

confidence: 99%

A Systematic Approach for Exploring Underground Environment Using LiDAR-Based System

Alhmiedat¹,

Marei²,

Albelwi³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Agricultural projects in different parts of the world depend on underground water wells. Recently, there have been many unfortunate incidents in which children have died in abandoned underground wells. Providing topographical information for these wells is a prerequisite to protecting people from the dangers of falling into them, especially since most of these wells become buried over time. Many solutions have been developed recently, most with the aim of exploring these well areas. However, these systems suffer from several limitations, including high complexity, large size, or inefficiency. This paper focuses on the development of a smart exploration unit that is able to investigate underground well areas, build a 3D map, search for persons and animals, and determine the levels of oxygen and other gases. The exploration unit has been implemented and validated through several experiments using various experiment testbeds. The results proved the efficiency of the developed exploration unit, in terms of 3D modeling, searching, communication, and measuring the level of oxygen. The average accuracy of the 3D modeling function is approximately 95.5%. A benchmark has been presented for comparing our results with related works, and the comparison has proven the contributions and novelty of the proposed system's results.

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Section: Cmes 2023 2 Related Workmentioning

confidence: 99%

A Systematic Approach for Exploring Underground Environment Using LiDAR-Based System

Alhmiedat¹,

Marei²,

Albelwi³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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“…Meanwhile, [184] improved the efficiency of the Lazy Theta* algorithm by reducing the number of generated neighbors to reduce the computation cost with a fewer number of line-of-sight checks. Faria et al [185] added the flyby sampling technique in the exploration system, including frontier and Lazy Theta* planner for global searching, CPP, and target inspection to produce a smooth path and cover the region without overlapped albeit the path length is not guaranteed to be optimal.…”

Section: ) Theta* Algorithmmentioning

confidence: 99%

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Mohd‐Mokhtar²,

2021

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The small battery capacities of the mobile robot and the un-optimized planning efficiency of the industrial robot bottlenecked the time efficiency and productivity rate of coverage tasks in terms of speed and accuracy, putting a great constraint on the usability of the robot applications in various planning strategies in specific environmental conditions. Thus, it became highly desirable to address the optimization problems related to exploration and coverage path planning (CPP). In general, the goal of the CPP is to find an optimal coverage path with generates a collision-free trajectory by reducing the travel time, processing speed, cost energy, and the number of turns along the path length, as well as low overlapped rate, which reflect the robustness of CPP. This paper reviews the principle of CPP and discusses the development trend, including design variations and the characteristic of optimization algorithms, such as classical, heuristic, and most recent deep learning methods. Then, we compare the advantages and disadvantages of the existing CPP-based modeling in the area and target coverage. Finally, we conclude numerous open research problems of the CPP and make suggestions for future research directions to gain insights. INDEX TERMSCoverage path planning, exploration, heuristic algorithm, deep reinforcement learning.

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“…The earliest frontier exploration algorithm makes the robot visit the closest frontiers to gain knowledge of an unknown region [24], proving the success in the ground robot. [4] extends this algorithm to support fast flying speed in UAV exploration, and [6] modifies this method to achieve 3D reconstruction by a low-cost 2D sensor. In contrast, the information-theoretic method [2] [3] focuses on reducing the current map entropy and use the formulated objective function to guide the exploration based on uncertainty.…”

Section: Related Workmentioning

confidence: 99%

Autonomous UAV Exploration of Dynamic Environments Via Incremental Sampling and Probabilistic Roadmap

Deng

Shimada

2021

IEEE Robot. Autom. Lett.

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Autonomous exploration requires robots to generate informative trajectories iteratively. Although sampling-based methods are highly efficient in unmanned aerial vehicle exploration, many of these methods do not effectively utilize the sampled information from the previous planning iterations, leading to redundant computation and longer exploration time. Also, few have explicitly shown their exploration ability in dynamic environments even though they can run real-time. To overcome these limitations, we propose a novel dynamic exploration planner (DEP) for exploring unknown environments using incremental sampling and Probabilistic Roadmap (PRM). In our sampling strategy, nodes are added incrementally and distributed evenly in the explored region, yielding the best viewpoints. To further shortening exploration time and ensuring safety, our planner optimizes paths locally and refine it based on the Euclidean Signed Distance Function (ESDF) map. Meanwhile, as the multiquery planner, PRM allows the proposed planner to quickly search alternative paths to avoid dynamic obstacles for safe exploration. Simulation experiments show that our method safely explores dynamic environments and outperforms the frontierbased planner and receding horizon next-best-view planner in terms of exploration time, path length, and computational time.

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Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR

Cited by 13 publications

References 57 publications

A Systematic Approach for Exploring Underground Environment Using LiDAR-Based System

A Systematic Approach for Exploring Underground Environment Using LiDAR-Based System

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Autonomous UAV Exploration of Dynamic Environments Via Incremental Sampling and Probabilistic Roadmap

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