Research On Real Time Path Planning Method Of Autonomous Underwater Vehicle

In the 21st century, the ocean will be investigated, developed and utilized peacefully. With the development of the sea, more and more international and domestic researchers apply themselves to the development of autonomous underwater vehicle (AUV) which can explore the underwater circumstance and accomplish missions. As a vehicle which works in complicated oceanic environments, automation and safety are the main characters of AUV. And the mean of autonomous ability refers that underwater vehicle is of the interactive ability with external environment. The important aspect of interactive ability is of ability of autonomous movement plan and the real-time control, and can complete the complex mission under the complex ocean environment. Intelligent control is the key technology to keep AUV autonomous and safe. Intelligent control includes autonomous mission planning, control and status monitoring. It's important to research autonomous mission planning, motion control and status monitoring of AUV for improving AUV's intelligence and widening the application.The motion planning of AUV is mostly under the direction of global planning, making use of the online local information to avoid the unknown obstacles. However, because of the complicated and varied circumstances, the AUV not only has the characters of essential nonlinearity, tight coupling and frequent changeability, but also has been affected by many undetermined factors. Starting from the adaptation and real-time of AUV, ignoring the integrality of the environment knowledge, and solving the disadvantage which the off-linearity of overall planning and real-time partial planning can't optimize the path. A real-time path planning method of AUV based on the combine of behavior and rolling planning is presented. The method of real-time path planning for underwater vehicle in the uncertain environment is researched by fuzzy control strategy. The real-time path planning method is researched for underwater vehicle in the static uncertain environment. Through the allocation of the AUV's sensor system and the detection of the obstacles, depending on the apperceiving of the ambience at every sampling time, the compartmentalization of the kinds of ambience is established. And the real-time rolling path planning instrument basing on fuzzy control is designed, and built the fuzzy rule library based on expert experiences.The anthropopathic real-time rolling path planning algorithm is presented aiming at the local path planning. Computer simulation and experiment are performed in the environment existing dynamic and static obstacles. And experiment results validated the feasibility of the real-time rolling path planning algorithm.When the AUV is walking under the environment that existing concave obstacles, the method of switching between the real target and virtual target is presented. The computer simulation and experiment results show that the method is effective and feasible for vibration and hover problem when AUV walking in concave obstacles district based on the real-time sensor information.AUV path planning method in the working circumstances with dynamic obstacles is researched. The processing method of dynamic obstacles—random movement in wide extension and constant speed movement in sampling period combine together. The uncertain location of the dynamic obstacles is estimated by maximum likelihood estimation method. The initiative obstacles avoidance method is presented aiming on collision avoidance problem of the dynamic obstacles. And the computer simulation and experiment research of path planning are carried out under the circumstances with dynamic obstacles. The results of simulation and experiment improved the initiative obstacles avoidance method is feasible and effective.Predigestion of the fuzzy control rule using fuzzy neural network and study method of correlation parameter is researched. Based on the fuzzy adaptive learning control network (FALCON), using distributed learning method to adjust the network architecture and parameter, using the fuzzy C average value cluster method to initiate the membership function of the input and the output variable, extracting and simplifying the fuzzy rules by the maximum value method based on the weight matrix, using the genetic algorithm to adjust the parameter of the membership function. The computer simulation results confirm the feasibility of the research method.According to correlation property of input and output datum, the adapting nerve fuzzy movement planning instrument of AUV is designed based on subtractive-clustering algorithm of density. The initial structure and parameters of fuzzy system are optimized by mixing parameters learning method. The simulation results of adapting nerve fuzzy movement planning instrument and fuzzy planning instrument of the same condition are compared and analyzed. The results show that adapting nerve fuzzy movement planning instrument is better than fuzzy planning instrument.