Cooperative algorithms for a team of autonomous underwater vehicles

Abstract

Multi-vehicle missions offer several advantages over single-vehicle missions in terms of mission complexity and tolerance to single-vehicle failure. However, missions involving multiple underwater vehicles pose two main challenges -- the absence of a reliable positioning reference (GPS) and the extremely limited communication bandwidth among the vehicles -- both of which limit the application of multi-vehicle cooperation techniques that are commonly used by their land and aerial counterparts.

This thesis develops two cooperative algorithms for a team of Autonomous Underwater Vehicles (AUVs) that address the challenges. First, we design a cooperative navigation strategy for a beacon vehicle to serve as navigation beacon for a team of AUVs. The exchange of navigation information between the beacon and other vehicles improves their individual position estimates. We propose dynamic positioning algorithms for the beacon vehicle and analyse their performances in minimizing the position errors of other vehicles in the team. Second, given the bathymetric terrain maps, we develop cooperative localization using a team of sensor-limited AUVs. The localization of each vehicle is performed via decentralized particle filtering on its bathymetric measurements, assisted by acoustic range and information obtained from peer vehicles through acoustic communication. We extend the filter of an individual vehicle to incorporate information received from another vehicle to better estimate its position, and investigate the impact of communication interval, sensor noise and biases on the localization performance.

Designing a Command and Control (C2) system for a single AUV that is robust and easily extensible to accommodate the requirements of multi-vehicle cooperative missions is another focus of the thesis. In particular, we develop a hierarchical agent-based C2 system for a low-cost modular AUV - the STARFISH AUV - that allocates mission, navigation and vehicle tasks to individual self-contained agents. The collective interactions among the pool of agents enables the AUV to achieve its mission objectives autonomously. The C2 system has been developed and successfully deployed for various single-vehicle, adaptive missions as well as multi-vehicle cooperative missions.

Using both simulations and field testings, we demonstrate the feasibility and capability of the developed algorithms in minimizing the position errors accumulated by the AUVs during mission execution.