TY - JOUR
T1 - Improved BINN-Based Underwater Topography Scanning Coverage Path Planning for AUV in Internet of Underwater Things
AU - Cai, Wenyu
AU - Zhang, Shuai
AU - Zhang, Meiyan
AU - Wang, Chengcai
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2023/10/15
Y1 - 2023/10/15
N2 - Deep understanding the special nature of underwater topography plays an important role for Internet of Underwater Things (IoUT). Nowadays, underwater topography scanning with autonomous underwater vehicle (AUV) has been becoming the chief methodology of knowing seabed topography and geomorphology. How to design topography scanning trajectory can be mathematically described as a full coverage path planning (CPP) problem. In this article, facing the complete CPP problem of mobile AUV, a new strategy based on bio-inspired neural network (BINN) algorithm with improved activity value of each neuron is discussed in detail. The original activity value function in BINN is instead of a piecewise linear function to reduce computational complexity. In addition, to overcome traditional dead-zone problem, an A∗ path planning-based dead-zone escape method along the shorter path as early as possible to the recently uncovered area is described in deep. Extensive simulation results and practical experiments verify the performance of proposed Improved BINN (IBINN in short)-based algorithm.
AB - Deep understanding the special nature of underwater topography plays an important role for Internet of Underwater Things (IoUT). Nowadays, underwater topography scanning with autonomous underwater vehicle (AUV) has been becoming the chief methodology of knowing seabed topography and geomorphology. How to design topography scanning trajectory can be mathematically described as a full coverage path planning (CPP) problem. In this article, facing the complete CPP problem of mobile AUV, a new strategy based on bio-inspired neural network (BINN) algorithm with improved activity value of each neuron is discussed in detail. The original activity value function in BINN is instead of a piecewise linear function to reduce computational complexity. In addition, to overcome traditional dead-zone problem, an A∗ path planning-based dead-zone escape method along the shorter path as early as possible to the recently uncovered area is described in deep. Extensive simulation results and practical experiments verify the performance of proposed Improved BINN (IBINN in short)-based algorithm.
KW - Autonomous underwater vehicles (AUVs)
KW - Internet of Underwater Things (IoUT)
KW - coverage path planning (CPP)
KW - improved bio-inspired neural network
KW - topography map sweeping
UR - https://www.scopus.com/pages/publications/85161054846
U2 - 10.1109/JIOT.2023.3280035
DO - 10.1109/JIOT.2023.3280035
M3 - 文章
AN - SCOPUS:85161054846
SN - 2327-4662
VL - 10
SP - 18375
EP - 18386
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 20
ER -