Design of Location System for an Underwater Autonomous Robot

Multi-sensor data fusion is to combine multi-sensor´s information, which is redundant or complementary in the space or the time to obtain the uniform description or the understanding to the measured object according to a certain criterion. Data fusion methods are widely used in autonomous robots´ measurement system in order to acquire more comprehensive and more exact information. The paper analyzed several methods of multi-sensor data fusion such as Bayesian theorem, Kalman filter, Dempster-Shafer evidence theory and so on. Move-in-mud robot is an autonomous robot, which can excavate hole in the mud underwater. It can be used in sunken wreck salvage to improve the efficiency of excavating the hole. Location system of move-in-mud robot is designed in the paper and location principle of move-in-mud robot is analyzed. Fuzzy Kalman filter is applied to fuse redundant location information of the robot. The data fusion method is simulated and the simulation result shows fuzzy Kalman filter can get better location accuracy than Kalman filter especially when the errors are big.