Automation Middleware and Algorithms for Robotic Underwater Sensor Networks

Abstract

Abstract : The long term goals of the project are: (1) To establish systems and algorithms for controlled Lagrangian particle tracking that will be used to improve the accuracy of model based prediction of trajectories of controlled underwater vehicles subjected to ocean current. (2) To achieve a mission planning system for robotic underwater sensor networks that are able to perform automatic or semiautomatic adaptation to extreme ocean conditions and platform failure, deployment, and recovery. We develop a set of automation middleware that implement a set of novel algorithms for robotic underwater sensor networks serving applications of ocean sampling and ocean model improvement. We design novel model adjustment, cooperative control, and distributed sensing algorithms that will be implemented through the automation middleware. The technical objectives include the following: 1. To investigate a new data assimilation procedure---the controlled Lagrangian particle tracking (CLPT)---and its ability to provide feedback adjustments on ocean modelling systems. To design a validation and adjustment algorithm for ocean models based on CLPT. 2. To develop an automatic middleware that integrates ocean models, robot models, and vehicle control systems towards more accurate prediction of the controlled trajectories of robots in the ocean. 3. To investigate cooperative filters and their ability to improve data quality collected by robotic underwater sensor networks. 4. To design automatic mission planning algorithms for missions with multiple objectives and multiple resolutions. To design a set of efficient and effective control and navigation algorithms that utilize ocean flow to increase mobility with guaranteed sampling performance. 5. To develop a mission planning and optimization system that automatically generates control laws and mission definitions based on user input about mission goals and constraints.