A neural network approach to complete coverage path planning

By S.X. Yang, Chaomin Luo. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2004

439 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/8f4ef9e58e5581237497f4a37f3cdbd2462f155a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. R. Mohd-Mokhtar
2. M. Carreras
3. Juntong Qi
4. Enric Galceran
5. Jianda Han
6. Jizhong Xiao
7. Xia Yong
8. Dalei Song

In Control and Systems Engineering

1. R. Mohd-Mokhtar
2. M. Carreras
3. Enric Galceran
4. Chee Sheng Tan
5. Mohd Rizal Arshad

In Mechanical Engineering

1. R. Mohd-Mokhtar
2. M. Carreras
3. Enric Galceran
4. Chee Sheng Tan
5. Mohd Rizal Arshad

Citations in the corpus

• A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms (2021, background): “Besides, it also plays a crucial part in enhancing the performance of CPP. Yang and Luo [270] presented a non-learning neural network-based CPP approach for cleaning robots to avoid obstacles while planning a collision-free coverage path, but the environment is assumed to be off-line.”
• A survey on coverage path planning for robotics (2013, background): “[Luo et al., 2002; Yang and Luo, 2004] propose to use a neural network to achieve CPP on-line targeted to a floor cleaning application.”
• Survey of Robot 3D Path Planning Algorithms (2016, methodology): “NN was introduced first introduced by Glasius et al. [113] to avoid obstacle as well as navigation and then became popular and implemented to path planning in various areas [114–116].”