Path Planning and Navigation of Oceanic Autonomous Sailboats and Vessels: A Survey

Oceanic autonomous surface vehicles(ASVs) are one kind of autonomous marine robots that have advantages of energy saving and is flexible to use. Nowadays, ASVs are playing an important role in marine science, maritime industry, and national defense. It could improve the efficiency of oceanic data collection, ensure marine transportation safety, and protect national security. One of the core challenges for ASVs is how to plan a safe navigation autonomously under the complicated ocean environment. Based on the type of marine vehicles, ASVs could be divided into two categories: autonomous sailboats and autonomous vessels. In this article, we review the challenges and related solutions of ASVs' autonomous navigation, including modeling analysis, path planning and implementation. Finally, we make a summary of all of those in four tables and discuss about the future research directions.

Cooperative Multi-Agent Control for Autonomous Ship Towing Under Environmental Disturbances

Among the promising application of autonomous surface vessels(ASVs)is the utilization of multiple autonomous tugs for manipulating a floating object such as an oil platform,a broken ship,or a ship in port areas.Considering the real conditions and operations of maritime practice,this paper proposes a multi-agent control algorithm to manipulate a ship to a desired position with a desired heading and velocity under the environmental disturbances.The control architecture consists of a supervisory controller in the higher layer and tug controllers in the lower layer.The supervisory controller allocates the towing forces and angles between the tugs and the ship by minimizing the error in the position and velocity of the ship.The weight coefficients in the cost function are designed to be adaptive to guarantee that the towing system functions well under environmental disturbances,and to enhance the efficiency of the towing system.The tug controller provides the forces to tow the ship and tracks the reference trajectory that is computed online based on the towing angles calculated by the supervisory controller.Simulation results show that the proposed algorithm can make the two autonomous tugs cooperatively tow a ship to a desired position with a desired heading and velocity under the(even harsh)environmental disturbances.

Autonomous Vessel Scheduling

This study deals with an autonomous vessel scheduling problem when collaboration exists between port operators and an autonomous vessel company.A mixedinteger nonlinear programming model is developed,including decisions in assigning autonomous vessels to berths at each port and the optimal arrival time of each vessel at each port in an entire autonomous shipping network.This study aims to minimize the total cost of fuel consumption and the delay penalty of an autonomous vessel company.The nonlinear programming model is linearized and further solved using off-the-shelf solvers.Several experiments are conducted to test the effectiveness of the model and to draw insights for commercializing autonomous vessels.Results show that a company may speed up an autonomous vessel with short-distance voyage once fuel price decreases to gain additional benefits.

WARA-PS:a research arena for public safety demonstrations and autonomous collaborative rescue robotics experimentation

A research arena(WARA-PS)for sensing,data fusion,user interaction,planning and control of collaborative autonomous aerial and surface vehicles in public safety applications is presented.The objective is to demonstrate scientific discoveries and to generate new directions for future research on autonomous systems for societal challenges.The enabler is a computational infrastructure with a core system architecture for industrial and academic collaboration.This includes a control and command system together with a framework for planning and executing tasks for unmanned surface vehicles and aerial vehicles.The motivating application for the demonstration is marine search and rescue operations.A state-of-art delegation framework for the mission planning together with three specific applications is also presented.The first one concerns model predictive control for cooperative rendezvous of autonomous unmanned aerial and surface vehicles.The second project is about learning to make safe real-time decisions under uncertainty for autonomous vehicles,and the third one is on robust terrain-aided navigation through sensor fusion and virtual reality tele-operation to support a GPS-free positioning system in marine environments.The research results have been experimentally evaluated and demonstrated to industry and public sector audiences at a marine test facility.It would be most difficult to do experiments on this large scale without the WARA-PS research arena.Furthermore,these demonstrator activities have resulted in effective research dissemination with high public visibility,business impact and new research collaborations between academia and industry.