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

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.

[1]  Anders la Cour-Harbo,et al.  Side-to-side 3D coverage path planning approach for agricultural robots to minimize skip/overlap areas between swaths , 2016, Robotics Auton. Syst..

[2]  Tran Hiep Dinh,et al.  Enhanced discrete particle swarm optimization path planning for UAV vision-based surface inspection , 2017, ArXiv.

[3]  Raúl Simarro,et al.  Recursive Rewarding Modified Adaptive Cell Decomposition (RR-MACD): A Dynamic Path Planning Algorithm for UAVs , 2019, Electronics.

[4]  Didier Devaurs,et al.  Optimal Path Planning in Complex Cost Spaces With Sampling-Based Algorithms , 2016, IEEE Transactions on Automation Science and Engineering.

[5]  Herman Augusto Lepikson,et al.  Multiobjective coverage path planning: Enabling automated inspection of complex, real-world structures , 2017, Appl. Soft Comput..

[6]  Vishnu G. Nair,et al.  MR-SimExCoverage: Multi-robot Simultaneous Exploration and Coverage , 2020, Comput. Electr. Eng..

[7]  Lei Xie,et al.  A path planning approach based on multi-direction A* algorithm for ships navigating within wind farm waters , 2019, Ocean Engineering.

[8]  Ping Ren,et al.  Optimal UAV Route Planning for Coverage Search of Stationary Target in River , 2019, IEEE Transactions on Control Systems Technology.

[9]  Mohsen Guizani,et al.  Ant-Colony-Based Complete-Coverage Path-Planning Algorithm for Underwater Gliders in Ocean Areas With Thermoclines , 2020, IEEE Transactions on Vehicular Technology.

[10]  Thomas Gustafsson,et al.  2D visual area coverage and path planning coupled with camera footprints , 2018, Control Engineering Practice.

[11]  Rajesh Elara Mohan,et al.  Complete coverage path planning using reinforcement learning for Tetromino based cleaning and maintenance robot , 2020 .

[12]  Kam K. Leang,et al.  Near-Optimal Area-Coverage Path Planning of Energy-Constrained Aerial Robots With Application in Autonomous Environmental Monitoring , 2020, IEEE Transactions on Automation Science and Engineering.

[13]  Robert J. Wood,et al.  Science, technology and the future of small autonomous drones , 2015, Nature.

[14]  Charles Norman Macleod,et al.  Machining-Based Coverage Path Planning for Automated Structural Inspection , 2018, IEEE Transactions on Automation Science and Engineering.

[15]  Roland Siegwart,et al.  Three-dimensional coverage path planning via viewpoint resampling and tour optimization for aerial robots , 2015, Autonomous Robots.

[16]  Amin Hammad,et al.  LiDAR-equipped UAV path planning considering potential locations of defects for bridge inspection , 2020 .

[17]  José L. Verdegay,et al.  Coverage path planning with unmanned aerial vehicles for 3D terrain reconstruction , 2016, Expert Syst. Appl..

[18]  Wen-Hua Chen,et al.  Optimal Polygon Decomposition for UAV Survey Coverage Path Planning in Wind , 2018, Sensors.

[19]  Shalabh Gupta,et al.  $\varepsilon ^{\star }$: An Online Coverage Path Planning Algorithm , 2018, IEEE Transactions on Robotics.

[20]  Giorgio C. Buttazzo,et al.  Energy-Aware Spiral Coverage Path Planning for UAV Photogrammetric Applications , 2018, IEEE Robotics and Automation Letters.

[21]  Taua M. Cabreira,et al.  Survey on Coverage Path Planning with Unmanned Aerial Vehicles , 2019, Drones.

[22]  M. Er,et al.  Coverage path planning for UAVs based on enhanced exact cellular decomposition method , 2011 .

[23]  Isabelle Fantoni,et al.  UAVs that fly forever: Uninterrupted structural inspection through automatic UAV replacement , 2017, Ad Hoc Networks.

[24]  Lasse Damtoft Nielsen,et al.  Convex Decomposition for a Coverage Path Planning for Autonomous Vehicles: Interior Extension of Edges , 2019, Sensors.

[25]  Charlie C. L. Wang,et al.  Energy-Efficient Coverage Path Planning for General Terrain Surfaces , 2019, IEEE Robotics and Automation Letters.

[26]  Juan Carlos Herrera-Lozada,et al.  Coverage Path Planning for 2D Convex Regions , 2019, Journal of Intelligent & Robotic Systems.

[27]  Enrico Natalizio,et al.  PPS: Energy-Aware Grid-Based Coverage Path Planning for UAVs Using Area Partitioning in the Presence of NFZs , 2020, Sensors.

[28]  Xinyue Kan,et al.  Online Exploration and Coverage Planning in Unknown Obstacle-Cluttered Environments , 2020, IEEE Robotics and Automation Letters.

[29]  Bahram Parvin,et al.  A Practical Methodology for Generating High-Resolution 3D Models of Open-Pit Slopes Using UAVs: Flight Path Planning and Optimization , 2020, Remote. Sens..

[30]  Lorenzo Comba,et al.  Unsupervised detection of vineyards by 3D point-cloud UAV photogrammetry for precision agriculture , 2018, Comput. Electron. Agric..

[31]  Giovanni Muscato,et al.  Complete coverage path planning for aerial vehicle flocks deployed in outdoor environments , 2019, Comput. Electr. Eng..

[32]  Rudi Penne,et al.  A Gradient-Based Inspection Path Optimization Approach , 2018, IEEE Robotics and Automation Letters.

[33]  Kun Zhou,et al.  Method and bench-marking framework for coverage path planning in arable farming , 2020 .

[34]  Dionysis Bochtis,et al.  Coverage planning for capacitated field operations, part II: Optimisation , 2015 .