Genetic Algorithms for Coverage Path Planning using Mobile Robots
DOI:
https://doi.org/10.54097/hset.v41i.6798Keywords:
sub-region, mesh method, genetic algorithms.Abstract
Using the grid method to establish the environmental model, the map is divided into several sub-regions according to the environmental characteristics, and the connection between the sub-regions is established. Next, the Genetic Algorithm encodes all sub-regions, and the base point information between and within each region is established to obtain the optimal coverage sequence. Partial coverage is achieved in each sub-region by means of reciprocating motion, which turns the complete coverage problem into a traveling salesman problem (TSP). Finally, the Genetic Algorithm is used to conduct an experimental simulation to obtain the correlation between the fitness and the number of iterations, and obtain the optimal traversal sequence, which proves that the method is effective and feasible.
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References
K. Tiwari and N. Young Chong 2020 Coverage Path Planning (CPP): Maximal area coverage (Academic Press) pp 75-84
S. Choi and J. Lee and W. Yu 2010 Fast any-angle path planning on grid maps with non-collision pruning (2010 IEEE International Conference on Robotics and Biomimetics) pp 1051-1056
M. Hassan and D. Liu 2018 A Deformable Spiral Based Algorithm to Smooth Coverage Path Planning for Marine Growth Removal (2018 IEEE/RSJ International Conference on Intelligent Robots and Systems) pp 1913-1918
H. Choset and P. Pignon 1998 Coverage Path Planning: The Boustrophedon Cellular Decomposition (London Zelinsky: Field and Service Robotics) pp 203-209
V. Kroumov and J. Yu 2011 Neural Networks Based Path Planning and Navigation of Mobile Robots in Recent Advances in Mobile Robotics (London, United Kingdom: IntechOpen)
S. Sivanandam and S. Deepa 2008 Genetic Algorithm Optimization Problems (Springer, Berlin, Heidelberg: Introduction to Genetic Algorithms)
C. Zeng and Q. Zhang and X.P. Wei 2011 Global Path Planning for Full-Area Coverage Robotic Systems by Employing an Active Genetic Algorithm (Advanced Materials Research) pp 1881-1886
E. Shimon 2011 Depth-First Search (Cambridge: Graph Algorithms) pp 46-64
J. Chen and P. Dames 2020 Distributed multi-target search and tracking using a coordinated team of ground and aerial robots (Robotics science and systems)
J. Chen and P. Dames 2020 Collision-free distributed multi-target tracking using teams of mobile robots with localization uncertainty (2020 IEEE/RSJ International Conference on Intelligent Robots and Systems) pp 6968-6974
J. Chen and P. Dames 2020 Distributed Multi-Target Tracking for Heterogeneous Mobile Sensing Networks with Limited Field of Views (2021 IEEE International Conference on Robotics and Automation) pp 9058-9064
D. Davendra and M. Bialic-Davendra 2020 Introductory Chapter: Traveling Salesman Problem - An Overview (London: Novel Trends in the Traveling Salesman Problem)
G. Vahdati and M. Yaghoubi and M. Poostchi and M. B. Naghibi S. A 2009 New Approach to Solve Traveling Salesman Problem Using Genetic Algorithm Based on Heuristic Crossover and Mutation Operator (International Conference of Soft Computing and Pattern Recognition) pp 112-116
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