This paper takes the wave glider as the research object. The wave glider is studied comprehensively from the aspects of motion mechanism, motion law and hydrodynamic performance. Firstly, based on the turbulence model of RANS equation, dynamic mesh method, the hydrodynamic performance of two-dimensional tandem hydrofoils is analyzed, which has been developed twice. The paper puts forward the a obstacle avoidance algorithm of wave glider based on fuzzy control. Combining the principle of line-of-sight navigation with the idea of fuzzy control, the path planning of wave glider is carried out. The simulation and experimental results show that the algorithm can provide real-time and stable motion path of the wave glider, effectively avoid obstacles and safely reach the target point.
Graf PlessenM., BernardiniD., EsenH. and BemporadA., Spatial-based predictive control and geometric corridor planning for control coupled with obstacle avoidance, IEEE Transactions on Control Systems Technology (99) (2017), 1–13.
2.
LiuJ., JayakumarP., SteinJ.L. and ErsalT., Combined speed and steering control in high-speed autonomous ground vehicles for obstacle using model predictive control, IEEE Transactions on Vehicular Technology66(10) (2017), 1–1.
3.
SederM., BaotićM. and PetrovićI., Receding horizon control for convergent navigation of a differential drive mobile robot, IEEE Transactions on Control Systems Technology25(2) (2017), 1–8.
4.
BencloucifA.M., NguyenA.T., SentouhC. and PopieulJ.C., Cooperative trajectory planning for haptic shared control between driver and automation in highway driving, IEEE Transactions on Industrial Electronics (99) (2019), 1–1.
5.
ZongL., LuoJ., WangM. and YuanJ., Obstacle avoidance handling and mixed integer control for space robots, Advances in Space Research61(8) (2018), S027311771830070X.
6.
LiX., SongS. and YongG., Robust finite-time tracking control for euler– lagrange systems with obstacle avoidance, Nonlinear Dynamics93(11) (2018), 1–9.
7.
ChenY., PengH. and GrizzleJ., Obstacle avoidance for low-speed autonomous vehicles with barrier function, IEEE Transactions on Control Systems Technology (99) (2018), 1–13.
8.
MousazadehH., JafarbigluH., AbdolmalekiH., OmraniE., MonhaseriF., AbdollahzadehM.R., et al., Developing a navigation, guidance and obstacle avoidance algorithm for an unmanned surface vehicle (usv) by algorithms fusion, Ocean Engineering159 (2018), 56–65.
9.
ZongL., LuoJ., WangM. and YuanJ., Obstacle avoidance handling and mixed integer control for space robots, Advances in Space Research61(8) (2018), S027311771830070X.
10.
LiuX., WangX., RenP., CaoY., YanZ. and LiuY., Automatic fishing net detection and based on optical gated viewing for underwater obstacle avoidance, Optical Engineering56(8) (2017), 083101.
11.
SchaubA., BaumgartnerD. and BurschkaD., Reactive obstacle avoidance for highly maneuverable vehicles based on a two-stage optical flow clustering, IEEE Transactions on Intelligent Transportation Systems18(8) (2017), 2137–2152.
12.
WenG., ChenP. and LiuY.J., Formation control with avoidance for a class of stochastic multi-agent systems, IEEE Transactions on Industrial Electronics (99) (2017), 1–1.
13.
KangM.C., ChaeS.H., SunJ.Y., LeeS.H. and KoS.J., An enhanced obstacle avoidance method for the visually impaired using deformable grid, IEEE Transactions on Consumer Electronics63(2) (2017), 169–177.
14.
JieJ., KhajepourA., MelekW.W. and HuangY., Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints, IEEE Transactions on Vehicular Technology66(2) (2017), 952–964.
15.
Khansari-ZadehS.M. and KhatibO., Learning potential functions from human with encapsulated dynamic and compliant behaviors, Autonomous Robots41(1) (2017), 45–69.
16.
ParkJ., KarumanchiS. and IagnemmaK., Homotopy-based divide-and-conquer strategy for optimal trajectory planning via mixed-integer programming, IEEE Transactions on Robotics31(5) (2017), 1101–1115.
17.
TijmonsS., CroonG.C.H.E.D., RemesB.D.W., WagterC.D. and MulderM., Obstacle avoidance strategy using onboard stereo vision on a flaing wing mav, IEEE Transactions on Robotics33(4) (2017), 858–874.
18.
WenG., ChenP. and LiuY.J., Formation control with obstacle avoidance for a class of multi-agent systems, IEEE Transactions on Industrial Electronics (99) (2017), 1–1.
19.
WuS., Nonlinear information data mining based on time series for fractional differential operators, Chaos29 (2019), 013144. 10.1063/1.5085430.
20.
KeQ., WuS., WangM. and ZouY., Evaluation of developer efficiency based on improved DEA model, Wireless Personal Communications102(4) (2018), 3843–3849.
21.
WuS., WangM. and ZouY., Sewage information monitoring system based on wireless sensor, Desalination and Water Treatment121 (2018), 73–83.
22.
Orcioli-SilvaD., Vitã RioR., Lirani-SilvaE., PcrS., BerettaV.S. and LtbG., Objective measures of unobstructed walking and obstacle avoidance in parkinson’s disease subtypes, Gait & Posture62 (2017), 405–408.
23.
MarigoldD.S., ChangA.J. and LajoieK., Cutaneous reflex modulation during obstacle avoidance under conditions of normal and degraded visual input, Experimental Brain Research235(8) (2017), 2483–2493.
24.
SederM., BaotićM. and PetrovićI., Receding horizon control for navigation of a differential drive mobile robot, IEEE Transactions on Control Systems Technology25(2) (2017), 1–8.
