Trajectory Optimization for Underwater Vehicles in Time-Varying Ocean Flows

Author(s):  
Miguel Aguiar ◽  
Joao Borges de Sousa ◽  
Joao Miguel Dias ◽  
Jorge Estrela da Silva ◽  
Renato Mendes ◽  
...  
2011 ◽  
Vol 467-469 ◽  
pp. 1377-1385 ◽  
Author(s):  
Ming Zhong Yan ◽  
Da Qi Zhu

Complete coverage path planning (CCPP) is an essential issue for Autonomous Underwater Vehicles’ (AUV) tasks, such as submarine search operations and complete coverage ocean explorations. A CCPP approach based on biologically inspired neural network is proposed for AUVs in the context of completely unknown environment. The AUV path is autonomously planned without any prior knowledge of the time-varying workspace, without explicitly optimizing any global cost functions, and without any learning procedures. The simulation studies show that the proposed approaches are capable of planning more reasonable collision-free complete coverage paths in unknown underwater environment.


2012 ◽  
Vol 134 (3) ◽  
Author(s):  
Max Blanco ◽  
Philip A. Wilson

This article describes a literature review focused on published empirical measurements of external flows over bodies of revolution that can be employed for verification and validation of calculations of survey-class autonomous underwater vehicles (AUVs) or other like bodies. The flow regime for a survey-class AUV is defined, and a mathematically optimal velocity results for these energy-limited vehicles. A range-maximal hotel load is one of the inferences. Cavitation is shown not to affect this type of AUV. Environmental and computational problems of turbulence are discussed. A table of vital statistics of contemporary survey-class AUVs is provided.


Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Hui Zhang ◽  
Wenbin Zha ◽  
Xiangrong Xu ◽  
Yongfei Zhu

Aiming at the impact and disturbance of dual-arm robots in the process of coordinated transportation, a dual-arm cooperative trajectory optimization control based on time-varying constrained output state is proposed. According to the constraint relationship of the end-effector trajectory of the dual-arm coordinated transportation, the joint space trajectory mathematical model of the dual-arm coordinated transportation was established by using the master-slave construction method. Based on the time impact optimization index of joint trajectory, a multiobjective nonlinear equation is established. Using random probability distribution to extract the interpolation features of nonuniform quintic B-spline trajectory, the feature optimization target is selected, and the Newton numerical algorithm is used for iterative optimization. At the same time, it is combined with an elite retention genetic algorithm to further optimize the target. Based on the disturbance and tracking problem, a PD control method based on time-varying constrained output state is proposed, and the control law is designed. Its convergence is verified by establishing the Lyapunov function equation and asymmetric term. The trajectory optimization results show that the proposed trajectory optimization method can increase the individual diversity and enhance the individual local optimization, thus avoiding the premature impact of the elite retention genetic algorithm. Finally, the proposed control method is simulated on the platform of Gazebo; compared with the traditional PD control method, the results show that the proposed control algorithm has high robustness, and the rationality of the coordinated trajectory control method is verified by the double-arm handling experiment.


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