Motion planning algorithm for nonholonomic autonomous underwater vehicle in disturbance using reinforcement learning and teaching method

2003 ◽  
Author(s):  
H. Kawano ◽  
T. Ura
Keyword(s):  
Reinforcement Learning ◽  
Motion Planning ◽  
Autonomous Underwater Vehicle ◽  
Teaching Method ◽  
Underwater Vehicle ◽  
Learning And Teaching ◽  
Planning Algorithm

scholarly journals A unified dynamics-based motion planning algorithm for autonomous underwater vehicle-manipulator systems (UVMS)

Robotica ◽  
2004 ◽  
Vol 22 (1) ◽  
pp. 117-128 ◽  
Author(s):  
Tarun Kanti Podder ◽  
Nilanjan Sarkar
Keyword(s):  
Motion Planning ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Dynamic Responses ◽  
Hydrodynamic Drag ◽  
Actuator Faults ◽  
Kinematic Redundancy ◽  
Planning Algorithm ◽  
Dynamic Bandwidth ◽  
Reference Trajectories

A new unified motion planning algorithm for autonomous Underwater Vehicle-Manipulator Systems (UVMS) has been presented in this paper. Commonly, a UVMS consists of two sub-systems, a vehicle and a manipulator, having vastly different dynamic responses. The proposed algorithm considers the variability in dynamic bandwidth of the complex UVMS system and generates not only kinematically admissible but also dynamically feasible reference trajectories. Additionally, this motion planning algorithm exploits the inherent kinematic redundancy of the whole system and provides reference trajectories that accommodates other important criteria such as thruster/actuator faults and saturations, and also minimizes hydrodynamic drag. Effectiveness of the proposed unified motion planning algorithm has been verified by extensive computer simulation. The results are quite promising.

Autonomous Underwater Vehicle Motion Planning via Sampling Based Model Predictive Control

2014 ◽  
Vol 670-671 ◽  
pp. 1370-1377 ◽  
Author(s):  
Lin Lin Wang ◽  
Hong Jian Wang ◽  
Li Xin Pan
Keyword(s):  
Model Predictive Control ◽  
Motion Planning ◽  
Predictive Control ◽  
Autonomous Underwater Vehicle ◽  
Angular Rate ◽  
Control Variable ◽  
Underwater Vehicle ◽  
Planning Optimization ◽  
Sampling Points ◽  
Rolling Optimization

In order to improve the ability of independent planning for AUV (Autonomous Underwater Vehicle), a new method of motion planning based on SBMPC (Sampling Based Model Predictive Control) is proposed, which is combined with model predictive control theory. Input sampling is directly made in control variable space, and sampling data is substituted into the predictive model of AUV motion. Then surge velocity and yaw angular rate in next sampling time are obtained through calculations. If predictive states are evaluated according to the performance index previously defined, optimal prediction of AUV states in next sampling can be used to realize motion planning optimization. Effects of three sampling methods (viz. uniform sampling, Halton sampling and CVT sampling) on motion planning performance are also compared in simulations. Statistical analysis demonstrates that CVT sampling points has the most uniform coverage in two-dimensional plane when amount of sampling points is the same for three methods. Simulation results show that it is effective and feasible to plan a route for AUV by using CVT sampling and rolling optimization of MPC (Model Predictive Control).

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