Kalman Filter-based navigation system for the Amphibious Spherical Robot

Abstract

Robust and performing navigation systems for Autonomous Underwater Vehicles (AUVs) play a discriminant role towards the success of complex underwater missions. This paper presents a new design and development of a low-cost INS (Inertial Navigation System) using Miro-Electro-Mechanical-System (MEMS) inertial sensor and the pressure sensor (PS). The intensive pre-processing and modeling MEMES sensor's primitive, noisy motion data are outline, these techniques transform the erroneous motion data into practical motion indicators illustrated in 3D position, 3D velocity and 3D orientation. INS acts as a dead reckoning device. The pressure sensor is used to detect the depth data of underwater Vehicles. The quality of the filtering algorithm for the estimation of the AUV navigation state strongly affects the performance of the overall system. In this paper, the authors present adapt the Kalman Filter (KF) approach. Experiments were conducted to improve the navigation system performance of the INS and PS installed on the Amphibious Spherical Robot III (ASR III) for motion and attitude estimation. Lastly the experiment results are evaluated and verified using the sensor data from the navigation system.