Paper Info
Title
The Robotic Fish Strategy Based on The Extreme Learning Machine Optimized by Particle Swarm Optimization Algorithm
Abstract
Aiming at URWPGSim2D platform, in order to realize rapid and accurate adjustment of the robotic fish, action decision strategy based on the extreme learning machine optimized by particle swarm algorithm is put forward. According to the current environmental information of robotic fish, use the extreme learning machine to choose the optimal hitting point independently, and to determine the optimal combination of velocity and angular velocity of robotic fish. At the same time, the particle swarm optimization algorithm is introduced to optimize the extreme learning machine, which can improve the accuracy and robustness of the extreme learning machine. Verified by URWPGSim2D platform show that: the robotic fish path can be adjusted according to the strategy, realize combinatorial optimization of speed and direction, and find the target in the shortest time and distance. This shows that action decision-making strategy based on extreme learning machine can fully consider the real-time information of robotic fish and water polo, choose a different strategy in different cases, have a strong ability to adapt, meet the requirements of robotic fish for the action decisions.
Year
DOI
Venue
2018
10.1109/ICIS.2018.8466496
2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS)
Keywords
Field
DocType
URWPGSim2D,robotic fish,particle swarm algorithm,extreme learning machine,action decision-making
Particle swarm optimization,Feedforward neural network,Angular velocity,Extreme learning machine,Computer science,Robot kinematics,Algorithm,Combinatorial optimization,Robustness (computer science),Decision strategy
Conference
ISBN
Citations 
PageRank 
978-1-5386-5893-2
0
0.34
References 
Authors
5
6
Name
Order
Citations
PageRank
Xuexi Zhang100.34
Shuibiao Chen200.68
zhiguang cao36811.12
Shuting Cai4629.16
Zerong Peng500.34
Xiaoming Xiong602.70