Characteristic evaluation via multi-sensor information fusion strategy for spherical underwater robots

Currently, most of the existing fusion methods ignore the rich multi-source information of different types of sensor nodes in the underwater unknown environment, which makes it challenging for Autonomous Underwater Vehicles (AUVs) to accurately perceive the external environment and make actionable decisions. Considering the key issues such as attitude estimation, positioning and obstacle avoidance involved in performing AUV tasks, this paper proposed a Multi-Source Information Fusion (MSIF) model for Spherical Underwater Robots (SURs) we developed based on various low-cost sensors. Multi-source information from an Inertial Measurement Unit (IMU), Pressure Sensor Array (PSA), Obstacle Avoidance Sensor Array (OASA), Depth Sensor (DS), Looking-Down Camera (LDC) and Acoustic Communication System (ACS) were fused to enable SUR to obtain high-precision estimated data for attitude estimation, positioning and obstacle avoidance, etc. More precisely, according to the correlation between the sensors, the optimized model was constructed to compensate for angle errors, velocity errors, orientation errors, etc. Subsequently, a machine learning method using Back Propagation Neural Network (BPNN) was proposed to improve the accuracy and effectiveness of the MSIF model through feature selection, data training, and feature estimation, etc. Finally, a series of experiments were performed under different scenarios, such as motion and obstacle avoidance experiments. The theoretical derivation and comprehensive evaluations demonstrated the effectiveness and feasibility of the proposed model, which provided a new reference value for solving issues such as attitude estimation, positioning and obstacle avoidance of AUVs.