# Research on Adaptive Cooperative Positioning Algorithm for Underwater Robots Based on Dolphin Group Cooperative Mechanism

## TL;DR

This paper introduces a new underwater robot positioning algorithm inspired by dolphin group behavior, improving accuracy in complex marine environments.

## Contribution

The FGAWSP algorithm adapts to heavy-tailed noise using a factor graph model and dynamic measurement weighting.

## Key findings

- FGAWSP reduces positioning errors by 22.31% compared to traditional methods.
- The algorithm dynamically adjusts measurement weights to handle anomalous range values.
- The method is robust in non-Gaussian noise environments typical of underwater acoustics.

## Abstract

Inspired by the remarkable collaborative echolocation mechanisms of dolphin pods, the paper addresses the challenge of achieving high-precision cooperative positioning for clusters of unmanned underwater vehicles (UUVs) in complex marine environments. Cooperative positioning systems for UUVs typically rely on acoustic ranging information to correct positional errors. However, the propagation characteristics of underwater acoustic signals are susceptible to environmental disturbances, often resulting in non-Gaussian, heavy-tailed distributions of ranging noise. Additionally, the strong nonlinearity of the system and the limited observability of measurement information further constrain positioning accuracy. To tackle these issues, this paper innovatively proposes a Factor Graph-based Adaptive Cooperative Positioning Algorithm (FGAWSP) suitable for heavy-tailed noise environments. The method begins by constructing a factor graph model for UUV cooperative positioning to intuitively represent the probabilistic dependencies between system states and observed variables. Subsequently, a novel factor graph estimation mechanism integrating adaptive weights with the product algorithm is designed. By conducting online assessment of residual information, this mechanism dynamically adjusts the fusion weights of different measurements, thereby achieving robust handling of anomalous range values. Experimental results demonstrate that the proposed method reduces positioning errors by 22.31% compared to the traditional algorithm, validating the effectiveness of our approach.