Coverage Path Planning Optimization for Slopes and Dams Inspection

Different practical applications have emerged in the last few years, requiring periodic and detailed inspections to verify possible structural changes. Inspections using Unmanned Aerial Vehicles (UAVs) should minimize flight time due to battery time restrictions and identify the terrain’s topographic features. In this sense, Coverage Path Planning (CPP) aims at finding the best path to coverage of a determined area respecting the operation’s restrictions. Photometric information from the terrain is used to create routes or even refine paths already created. Therefore, this research’s main contribution is developing a methodology that uses a metaheuristic algorithm based on point cloud data to inspect slope and dams structures. The technique was applied in a simulated and real scenario to verify its effectiveness. The results showed an increasing 3D reconstructions’ quality observing optimizing photometric and mission time criteria.

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A joint global and local path planning optimization for UAV task scheduling towards crowd air monitoring

Computer Networks ◽

10.1016/j.comnet.2021.107913 ◽

2021 ◽

Vol 193 ◽

pp. 107913

Author(s):

Yuan Tang ◽

Yiming Miao ◽

Ahmed Barnawi ◽

Bander Alzahrani ◽

Reem Alotaibi ◽

...

Keyword(s):

Path Planning ◽

Task Scheduling ◽

Air Monitoring ◽

Local Path Planning ◽

Planning Optimization ◽

Local Path ◽

Global And Local

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UAV Coverage Path Planning under Varying Power Constraints using Deep Reinforcement Learning

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ◽

10.1109/iros45743.2020.9340934 ◽

2020 ◽

Author(s):

Mirco Theile ◽

Harald Bayerlein ◽

Richard Nai ◽

David Gesbert ◽

Marco Caccamo

Keyword(s):

Reinforcement Learning ◽

Path Planning ◽

Coverage Path Planning ◽

Power Constraints

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From Coverage Path Planning to parking lot exploration

2020 23rd International Symposium on Measurement and Control in Robotics (ISMCR) ◽

10.1109/ismcr51255.2020.9263739 ◽

2020 ◽

Author(s):

Anna Barbara Adam ◽

Laszlo Kocsany ◽

Emese Gincsaine Szadeczky-Kardoss

Keyword(s):

Path Planning ◽

Coverage Path Planning ◽

Parking Lot

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A GPU-based Approach for Path Planning Optimization via Travel Length Reduction

Procedia Manufacturing ◽

10.1016/j.promfg.2021.06.034 ◽

2021 ◽

Vol 53 ◽

pp. 310-317

Author(s):

Michael Borish ◽

Charles Wade

Keyword(s):

Path Planning ◽

Planning Optimization ◽

Length Reduction ◽

Travel Length

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R-DFS: A Coverage Path Planning Approach Based on Region Optimal Decomposition

Remote Sensing ◽

10.3390/rs13081525 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1525

Author(s):

Gang Tang ◽

Congqiang Tang ◽

Hao Zhou ◽

Christophe Claramunt ◽

Shaoyang Men

Keyword(s):

Path Planning ◽

Google Earth ◽

Simulation Experiments ◽

Minimum Width ◽

Coverage Path Planning ◽

Optimal Paths ◽

Planning Approach ◽

Polygonal Area ◽

Aerial Vehicle ◽

Working Distance

Most Coverage Path Planning (CPP) strategies based on the minimum width of a concave polygonal area are very likely to generate non-optimal paths with many turns. This paper introduces a CPP method based on a Region Optimal Decomposition (ROD) that overcomes this limitation when applied to the path planning of an Unmanned Aerial Vehicle (UAV) in a port environment. The principle of the approach is to first apply a ROD to a Google Earth image of a port and combining the resulting sub-regions by an improved Depth-First-Search (DFS) algorithm. Finally, a genetic algorithm determines the traversal order of all sub-regions. The simulation experiments show that the combination of ROD and improved DFS algorithm can reduce the number of turns by 4.34%, increase the coverage rate by more than 10%, and shorten the non-working distance by about 29.91%. Overall, the whole approach provides a sound solution for the CPP and operations of UAVs in port environments.

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