Evolutionary artificial potential fields and their application in real time robot path planning

By Prahlad Vadakkepat, Kay Chen Tan, Ming-Liang Wang. Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512), 2000

369 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/9ce05551e733dc41bbb3b3320aaa6d477f0f904c

How researchers see this paper

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

In Aerospace Engineering

1. Dusist Niyato
2. Ping Wang
3. Ying-Chang Liang
4. Shimin Gong
5. Dinh Thai Hoang
6. Dong In Kim
7. Andreas Bollig

In Biomedical Engineering

1. Dusist Niyato
2. Dong In Kim

In Computer Vision and Pattern Recognition

1. Ying-Chang Liang
2. Dusist Niyato
3. Dinh Thai Hoang
4. Nguyen Cong Luong
5. Dong In Kim

Citations in the corpus

• Applications of Deep Reinforcement Learning in Communications and Networking: A Survey (2018, methodology): “To tackle the connectivity preservation problem, the Artificial Potential Field (APF) algorithm [134] is used.”
• Path Planning for the Mobile Robot: A Review (2018, background): “[55] proposed a new approach called evolutionary APF (EAPF) for path planning of the real-time robot.”