CT-CPP: Coverage Path Planning for 3D Terrain Reconstruction Using Dynamic Coverage Trees

Shen, Zongyuan; Song, Junnan; Mittal, Khushboo; Gupta, Savita

doi:10.1109/lra.2021.3119870

Cited by 11 publications

(8 citation statements)

References 31 publications

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“…CT-CPP follows a TSP-optimized node traversal sequence of an incrementally built CT for complete 3D coverage. The letter extends our preliminary work [17] by presenting a detailed formulation of the CT-CPP method with a comparative evaluation showing significant reductions in trajectory length, energy consumption and reconstruction error.…”

Section: Introductionmentioning

confidence: 60%

“…In general, CPP methods can be categorized into offline methods or online methods (i.e., sensor-based). While offline methods assume the environment to be a priori known, online methods compute the coverage paths in situ, and are thus adaptive to the environment [2], [12], [17]. While a variety of methods are available for 2D CPP, only a few have focused on 3D CPP.…”

Section: Related Workmentioning

confidence: 99%

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CT-CPP: Coverage Path Planning for 3D Terrain Reconstruction Using Dynamic Coverage Trees

Shen,

Song,

Mittal

et al. 2020

Preprint

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This letter addresses the 3D coverage path planning (CPP) problem for terrain reconstruction of unknown obstaclerich environments. Due to sensing limitations, the proposed method, called CT-CPP, performs layered scanning of the 3D region to collect terrain data, where the traveling sequence is optimized using the concept of a coverage tree (CT). A modified TSP-based tree traversal strategy is proposed, and compared with breadth-first search (BFS) and depth-first search (DFS) methods, with TSP providing the shortest trajectory lengths. The CT-CPP method is validated on a high-fidelity underwater simulator and the results are evaluated in comparison to an existing terrain following CPP method (TF-CPP). The CT-CPP with TSP optimizer yields significant improvements in trajectory length, energy consumption, and reconstruction error.

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Section: Introductionmentioning

confidence: 60%

Section: Related Workmentioning

confidence: 99%

CT-CPP: Coverage Path Planning for 3D Terrain Reconstruction Using Dynamic Coverage Trees

Shen,

Song,

Mittal

et al. 2020

Preprint

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“…Over the past few decades, UAVs have been widely used in military and civilian applications [24]. The UAVs can be used for search and rescue [25,26], photogrammetry [27,28], structures inspection [29,30], model reconstruction [31,32], smart farming [33,34], post-earthquake assessment [35] etc. Many of these UAVs applications involve Coverage Path Planning (CPP) technique, which requires building a path that guarantees that an agent will explore every location in a given scenario [36] .…”

Section: Introductionmentioning

confidence: 99%

Grid-Based coverage path planning with NFZ avoidance for UAV using parallel self-adaptive ant colony optimization algorithm in cloud IoT

et al. 2022

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In recent years, with the development of Unmanned Aerial Vehicle (UAV) and Cloud Internet-of-Things (Cloud IoT) technology, data collection using UAVs has become a new technology hotspot for many Cloud IoT applications. Due to constraints such as the limited power life, weak computing power of UAV and no-fly zones restrictions in the environment, it is necessary to use cloud server with powerful computing power in the Internet of Things to plan the path for UAV. This paper proposes a coverage path planning algorithm called Parallel Self-Adaptive Ant Colony Optimization Algorithm (PSAACO). In the proposed algorithm, we apply grid technique to map the area, adopt inversion and insertion operators to modify paths, use self-adaptive parameter setting to tune the pattern, and employ parallel computing to improve performance. This work also addresses an additional challenge of using the dynamic Floyd algorithm to avoid no-fly zones. The proposal is extensively evaluated. Some experiments show that the performance of the PSAACO algorithm is significantly improved by using parallel computing and self-adaptive parameter configuration. Especially, the algorithm has greater advantages when the areas are large or the no-fly zones are complex. Other experiments, in comparison with other algorithms and existing works, show that the path planned by PSAACO has the least energy consumption and the shortest completion time.

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“…For example, unmanned air vehicles (UAVs) are used for survey, monitoring and mapping [1]. On the other hand, unmanned underwater vehicles (UUVs) are used for exploration [2], oceanic data collection (e.g., salinity and temperature), seabed mapping [3], [4], [5], [6], oil spill cleaning [7], and mine hunting [8]. Despite recent advances, the autonomy of these vehicles is limited, especially for curvatureconstrained vehicles that operate in environments with many obstacles where finding the time-optimal path is difficult, and is in fact NP-Hard [9].…”

Section: Introductionmentioning

confidence: 99%

T$^{\star}$-Lite: A Fast Time-Risk Optimal Motion Planning Algorithm for Multi-Speed Autonomous Vehicles

Wilson,

Shen,

Gupta

et al. 2020

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In this paper, we develop a new algorithm, called T -Lite, that enables fast time-risk optimal motion planning for variable-speed autonomous vehicles. The T -Lite algorithm is a significantly faster version of the previously developed T algorithm. T -Lite uses the novel time-risk cost function of T ; however, instead of a grid-based approach, it uses an asymptotically optimal sampling-based motion planner. Furthermore, it utilizes the recently developed Generalized Multi-speed Dubins Motion-model (GMDM) for sample-to-sample kinodynamic motion planning. The sample-based approach and GMDM significantly reduce the computational burden of T while providing reasonable solution quality. The sample points are drawn from a four-dimensional configuration space consisting of two position coordinates plus vehicle heading and speed. Specifically, T -Lite enables the motion planner to select the vehicle speed and direction based on its proximity to the obstacle to generate faster and safer paths. In this paper, T -Lite is developed using the RRT * motion planner, but adaptation to other motion planners is straightforward and depends on the needs of the planner.

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CT-CPP: Coverage Path Planning for 3D Terrain Reconstruction Using Dynamic Coverage Trees

Cited by 11 publications

References 31 publications

CT-CPP: Coverage Path Planning for 3D Terrain Reconstruction Using Dynamic Coverage Trees

CT-CPP: Coverage Path Planning for 3D Terrain Reconstruction Using Dynamic Coverage Trees

Grid-Based coverage path planning with NFZ avoidance for UAV using parallel self-adaptive ant colony optimization algorithm in cloud IoT

T$^{\star}$-Lite: A Fast Time-Risk Optimal Motion Planning Algorithm for Multi-Speed Autonomous Vehicles

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