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Optimal trajectory planning and execution control for long-range Autonomous Surface Vehicles

It took only a few decades for robotic underwater, surface and air vehicles to revolutionize ocean exploration. But this is just the beginning. Vehicle heterogeneity and functional diversity will enable new concepts of operation that could have not been imagined before. These include long endurance Atlantic operations to be undertaken by Autonomous Surface Vehicles (ASV).

The AutoNaut is a self-powered ASV developed by MOST (Autonomous Vessel) Ltd (https://www.autonautusv.com/). The ASV is designed to be a cost-effective, low manpower data collection platform, with zero-emission, extreme persistence, and the capability of surviving extreme weather conditions. Zero-emission is achieved solely by wave and solar power. A patented Wave-propulsion Technology converts energy from the pitch and roll of the waves. The AutoNaut is equipped with spring-loaded foils attached to the struts under the keel. These foils exploit the wave-induced vessel motion, caused by waves lifting the vessel, out of the water, and dropping it down again, to generate the forward propulsion speed in the order of 1-2 knots.

The main goal of this project is to develop a planning and execution control framework for long endurance operations to be performed by the AutoNaut with the goal of maximizing the scientific return of these operations while minimizing the logistical support. The candidate will explore AI-based systems, as well as other optimization and machine learning techniques, to develop an onboard trajectory optimization and execution framework on different ocean spatial and temporal scales. The onboard framework will be complemented by an onshore system incorporating forecasted wave and meteo data or observations.

The project builds on the experience of LSTS-UPORTO (https://lsts.fe.up.pt/) in the development and deployment of networked vehicle systems for ocean observation (https://schmidtocean.org/cruise/exploring_fronts_with_multiple_aerial-surface-underwater-vehicles). The proposed developments are applicable to other applications including autonomous ships.

Tasks and Responsibilities

  • Familiarize yourself with the current state-of-the-art in the fields of (1) Vehicle motion planning, (2) Artificial Intelligence (AI) based planning systems, (3) observation of dynamic features of the ocean, (4) AI-based learning of dynamic features of the ocean using physics-based models and remote sensing data, (5) software frameworks for uncrewed maritime vehicles, including the LSTS software toolchain (https://www.lsts.pt/toolchain).
  • Study the model and operational limitations of the AutoNaut ASV.
  • Develop a robust optimal trajectory planning and execution control for the Autonaut with several levels of time-space granularities.
  • Evaluate and test the planning and execution control framework in a simulation environment (LSTS toolchain) that high fidelity models of meteo-ocean conditions of the Atlantic Ocean.
  • Deploy the planning and execution control framework on an Autonaut ASV for field testing in the Atlantic.

Prerequisites
Candidate must hold a master’s degree (or equivalent) in any of the following fields: computer science, mechanical/electrical engineering, physics, mathematics, marine sciences, or related fields. The degree should have been completed in the last 5 years at most (candidates completing and defending their MSc thesis by July 2021, are welcome). The candidate must have strong analytical skills and be able to work at the intersection of science and technology. The candidate should have experience in one of the following programming languages C/C++ and Python. Experience with robotic operations and/or with the Robot Operating System (ROS) or with the LSTS software toolchain is a plus. Proficiency in written and spoken English is also required.

Hiring institution specific remarks:
Due to local regulations the University of Porto position is for 48 months

Hiring institution
University of Porto; PhD Enrollment: PhD position at the Laboratório de Sistemas e Tecnologias Subaquática – LSTS (Underwater Systems and Technologies Lab) at University of Porto (https://lsts.fe.up.pt/). The project involves collaboration with the +Atlantic Colab.

Main Academic Supervisor
Prof. João Sousa (LSTS-UPorto), contact: jtasso@fe.up.pt

Co-supervisors
Dr. Renato Mendes (+Atlantic Colab and LSTS-UPorto) and Prof. Pierre Lermusiaux (MIT).

 

Notice of the Call (english version)

Notice of the Call (portuguese version)

We are no longer accepting applications for this scholarship. Thank you.