Complete Area Coverage Path-Planning with Arbitrary Shape Obstacles

2021 ◽  
pp. 80-85
Author(s):  
Gene Eu Jan ◽  
Chaomin Luo ◽  
Hsien-Tang Lin ◽  
Kevin Fung
Keyword(s):  
Path Planning ◽  
Arbitrary Shape ◽  
Area Coverage ◽  
Coverage Path Planning

Global Path Planning for Full-Area Coverage Robotic Systems by Employing an Active Genetic Algorithm

2011 ◽  
Vol 328-330 ◽  
pp. 1881-1886
Author(s):  
Cen Zeng ◽  
Qiang Zhang ◽  
Xiao Peng Wei
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
High Performance ◽  
Search Space ◽  
Area Coverage ◽  
Robotic Systems ◽  
Genetic Operators ◽  
A Algorithm ◽  
Coverage Path Planning ◽  
Search Spaces

Genetic algorithm (GA), a kind of global and probabilistic optimization algorithms with high performance, have been paid broad attentions by researchers world wide and plentiful achievements have been made.This paper presents a algorithm to develop the path planning into a given search space using GA in the order of full-area coverage and the obstacle avoiding automatically. Specific genetic operators (such as selection, crossover, mutation) are introduced, and especially the handling of exceptional situations is described in detail. After that, an active genetic algorithm is introduced which allows to overcome the drawbacks of the earlier version of Full-area coverage path planning algorithms.The comparison between some of the well-known algorithms and genetic algorithm is demonstrated in this paper. our path-planning genetic algorithm yields the best performance on the flexibility and the coverage. This meets the needs of polygon obstacles. For full-area coverage path-planning, a genotype that is able to address the more complicated search spaces.

CPC Algorithm: Exact Area Coverage by a Mobile Robot Using Approximate Cellular Decomposition

Robotica ◽  
2020 ◽  
pp. 1-22
Author(s):  
K. R. Guruprasad ◽  
T. D. Ranjitha
Keyword(s):  
Operating System ◽  
Path Planning ◽  
Mobile Robot ◽  
Formal Analysis ◽  
Area Coverage ◽  
Cell Permeability ◽  
Cellular Decomposition ◽  
Robot Operating System ◽  
Coverage Path Planning

SUMMARY A new coverage path planning (CPP) algorithm, namely cell permeability-based coverage (CPC) algorithm, is proposed in this paper. Unlike the most CPP algorithms using approximate cellular decomposition, the proposed algorithm achieves exact coverage with lower coverage overlap compared to that with the existing algorithms. Apart from a formal analysis of the algorithm, the performance of the proposed algorithm is compared with two representative approximate cellular decomposition-based coverage algorithms reported in the literature. Results of demonstrative experiments on a TurtleBot mobile robot within the robot operating system/Gazebo environment and on a Fire Bird V robot are also provided.

scholarly journals Matheuristics for Multi-UAV Routing and Recharge Station Location for Complete Area Coverage

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1705
Author(s):  
Rafael Santin ◽  
Luciana Assis ◽  
Alessandro Vivas ◽  
Luciano C. A. Pimenta
Keyword(s):  
Path Planning ◽  
Mathematical Formulation ◽  
Area Coverage ◽  
Exact Method ◽  
Neighborhood Search ◽  
Nondominated Solutions ◽  
Vehicle Performance ◽  
Coverage Path Planning ◽  
High Level ◽  
Station Location

This paper presents matheuristics for routing a heterogeneous group of capacitated unmanned air vehicles (UAVs) for complete coverage of ground areas, considering simultaneous minimization of the coverage time and locating the minimal number of refueling stations. Whereas coverage path planning (CPP) is widely studied in the literature, previous works did not combine heterogeneous vehicle performance and complete area coverage constraints to optimize UAV tours by considering both objectives. As this problem cannot be easily solved, we designed high-level path planning that combines the multiobjective variable neighborhood search (MOVNS) metaheuristic and the exact mathematical formulation to explore the set of nondominated solutions. Since the exact method can interact in different ways with MOVNS, we evaluated four different strategies using four metrics: execution time, coverage, cardinality, and hypervolume. The experimental results show that applying the exact method as an intraroute operator into the variable neighborhood descent (VND) can return solutions as good as those obtained by the closest to optimal strategy but with higher efficiency.

2D multi-area coverage path planning using L-SHADE in simulated ocean survey

2021 ◽  
pp. 107754
Author(s):  
Guanzhong Chen ◽  
Yue Shen ◽  
Yixiao Zhang ◽  
Wenfeng Zhang ◽  
Dianrui Wang ◽  
...  
Keyword(s):  
Path Planning ◽  
Area Coverage ◽  
Coverage Path Planning

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

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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