Short course300 h · Online · 5 modules

Artificial Intelligence in Navigation Course

This course prepares you to take part in the technological transformation of navigation. You will learn how intelligent systems improve the safety, efficiency and sustainability of vessel operations, and how machine learning, computer vision and predictive analytics are applied to route optimization, collision avoidance, fleet management and autonomous navigation, turning navigation data into well-founded decisions on the bridge and ashore.

Duration300 h
ModeOnline
Start1 December
Modules5
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About the program

What is the Artificial Intelligence in Navigation Course?

This short course explains how artificial intelligence is applied to navigation, from the data that feeds it to its integration on the bridge. You will study the differences between automation, advanced analytics, machine learning and intelligent systems; the functional architecture of an AI solution for navigation, from sensors and data to inference and decision support; data sources such as radar, AIS, GNSS, cameras, echo sounders, weather and electronic charts, and how to clean, synchronize and fuse them; supervised, unsupervised and reinforcement learning; predictive models for weather, traffic, fuel consumption and collision risk; AI-based route optimization and maneuver recommendation; anomaly detection; and the integration of AI with ECDIS, AIS, radar and autopilot in manned and uncrewed vessels.

What sets this course apart: practical applications through simulation exercises and the analysis of case studies; work with current software tools and platforms used in artificial intelligence; a constant focus on safety, situational awareness and human validation of AI recommendations; coverage of both crewed and autonomous or semi-autonomous vessels; and an integrating project in which you design an AI solution for a real or simulated navigation problem and assess its accuracy, limitations and risks.

Program typeShort course
ModeOnline
Duration300 hours
Modules5
Start1 December

Program highlights

  • AI foundations: automation, machine learning and decision support on the bridge.
  • Data and sensors: radar, AIS, GNSS, cameras, echo sounder and multimodal data fusion.
  • Machine learning models: prediction of weather, traffic, consumption and collision risk.
  • Route optimization: AI algorithms balancing distance, safety, time and fuel.
  • Bridge integration: ECDIS, autopilot, intelligent interfaces and edge computing.
  • Integrating project: an AI solution designed and evaluated for a navigation case.
Syllabus

Syllabus of the Artificial Intelligence in Navigation Course

5 modules. Select a module to see its content.

Module 1

Fundamentals of artificial intelligence applied to navigation, autonomous systems and assisted decision-making in maritime environments

  • 1.1. The concept of artificial intelligence and its evolution in next-generation maritime, inland and leisure navigation systems
  • 1.2. The differences between automation, advanced analytics, machine learning and intelligent systems applied to navigation
  • 1.3. Functional architecture of an AI solution for navigation: sensors, data, models, inference, interface and decision support
  • 1.4. Critical variables of the navigational environment that AI can address: traffic, weather, route, risk, fuel consumption and vessel behavior
  • 1.5. The relationship between artificial intelligence, operational safety, route efficiency, situational awareness and the reduction of human error
At this stage
60 hestimated workload
5 wkweeks at your pace
Module 2

Data, sensors and intelligent perception systems for AI-based navigation

  • 2.1. Data sources for AI in navigation: radar, AIS, GNSS, cameras, echo sounder, weather, electronic charts and onboard sensors
  • 2.2. Quality, consistency and traceability of navigation data: cleaning, synchronization, labeling and preparation for model training
  • 2.3. Multimodal sensing and perception of the environment: detection of obstacles, maritime traffic, sea conditions and spatial references
  • 2.4. Data fusion for intelligent navigation: integrating heterogeneous signals into a single operational picture
  • 2.5. Anomaly detection and pattern recognition in navigation variables using machine learning techniques
At this stage
60 hestimated workload
5 wkweeks at your pace
Module 3

Machine learning models and AI algorithms for prediction, optimization and navigation assistance

  • 3.1. Fundamentals of supervised, unsupervised and reinforcement learning applied to navigation problems
  • 3.2. Predictive models for weather, traffic, fuel consumption, collision risk and the dynamic behavior of the vessel
  • 3.3. AI algorithms for route and track optimization: distance, safety, time, consumption and metocean conditions
  • 3.4. Maneuver recommendation systems and tactical decision support in coastal, ocean and port navigation
  • 3.5. Classification and detection models for relevant events: intrusion, route deviation, irregular navigation and abnormal operating conditions
At this stage
60 hestimated workload
5 wkweeks at your pace
Module 4

Integration of artificial intelligence with navigation systems, the digital bridge and maritime control platforms

  • 4.1. Integrating AI with ECDIS, AIS, radar, autopilot, fleet management systems and integrated bridge consoles
  • 4.2. Designing intelligent interfaces for assisted navigation: display of alerts, recommendations and priorities and cognitive support for the operator
  • 4.3. AI for advanced situational awareness: correlation of contacts, trajectory prediction and dynamic assessment of the maritime environment
  • 4.4. Applying artificial intelligence to autonomous and semi-autonomous navigation systems in crewed and uncrewed vessels
  • 4.5. Real-time monitoring, edge computing and onboard processing for fast decisions in changing navigation conditions
At this stage
60 hestimated workload
5 wkweeks at your pace
Module 5

Integrating project: designing and applying artificial intelligence to a real or simulated navigation case

  • 5.1. Definition of the case study: type of vessel, mission profile, navigation problem and improvement objectives for AI
  • 5.2. Selection of the data, sensors and variables needed to build an intelligent solution for the scenario
  • 5.3. Design of the AI model or system: functional architecture, inference logic, operational integration and performance criteria
  • 5.4. Development of the workflow in navigation: data capture, processing, generation of alerts or recommendations and human validation
  • 5.5. Technical and operational evaluation of the proposal: expected accuracy, limitations, risks, practical usefulness and scalability
At this stage
60 hestimated workload
5 wkweeks at your pace
Plan your pace

Duration and format of the Artificial Intelligence in Navigation Course

It is 300 hours. Tell us how many you can study each week and we will tell you when you will really finish.

4 h30 h
With 12 h per week
6
months to finish
25 weeks
weeks in total

This is the pace at which most students finish. It fits well with the installment plan.

Faculty

Academic leadership and faculty

The Navalis teaching team: working professionals who teach and supervise the programs.

Dra. Marina AlcoverDr. Marina AlcoverAcademic Director and Head of Quality Assurance
Ing. Diego CifuentesEng. Diego CifuentesSenior Lecturer in Naval Welding and Non-Destructive Testing; Head of QA/QC for Welded Joints
Cap. Javier Abaroa (MCA)Capt. Javier Abaroa (MCA)Senior Lecturer in Ship Handling, Electronic Navigation (ECDIS/Radar/ARPA) and GMDSS; STCW Compliance and Bridge Resource Management Coordinator
Dr. Pau FerrerDr. Pau FerrerSenior Lecturer in Naval Architecture and Simulation; Director of the Hydrodynamics and CFD Laboratory
Dra. Nuria LlobregatDr. Nuria LlobregatSenior Lecturer in Marine Meteorology, SAR and Operational Safety; Navigation Risk Management Coordinator
Ing. Javier BañulsEng. Javier BañulsSenior Lecturer in Electrical Engineering, Automation and ETO Engineering; Electronic Integration and Engine Alarms Coordinator
Ing. Sofía MarquinaEng. Sofía MarquinaSenior Lecturer in Materials, Composites and Corrosion Protection; Coordinator of the Advanced Paints and Coatings Line
Ing. Tomás RieraEng. Tomás RieraSenior Lecturer in Propulsion, Alternative Energy and Hybrid Systems; Energy Performance and Vibration Coordinator
Objectives

Objectives of the Artificial Intelligence in Navigation Course

01Adapt routes dynamically, taking into account sea state, currents, weather forecasts and the performance of the vessel.
02Reduce fuel consumption by managing speed and main engine power efficiently according to sea and wind conditions.
03Manage traffic in autonomous navigation scenarios, assessing risk dynamically, putting safety first and optimizing the route with environmental and navigational factors in mind.
04Predict and avoid collisions using all available information, such as radar, AIS and visual observation, to anticipate risk scenarios and act early.
05Improve navigational accuracy in adverse conditions by interpreting radar, AIS and ECDIS data and adjusting the track with sound judgment.
06Support critical decisions with AI, assessing risk by probability and consequence and selecting countermeasures that protect navigation and the environment.
Careers

Career opportunities after the Artificial Intelligence in Navigation Course

01Autonomous navigation software developerdesigning and testing AI algorithms for navigation systems.
02Marine robotics engineerintegrating AI into autonomous underwater vehicles and remotely operated vehicles.
03Navigation data analystprocessing large navigation datasets to optimize routes and predict risks.
04AI consultant for the maritime industryadvising shipping companies and ports on AI solutions.
05Navigation simulation specialistbuilding realistic simulation environments to train and test autonomous systems.
06AI project manager in navigationleading the development and deployment of AI projects in the maritime sector.
Frequently asked questions

Questions about the Artificial Intelligence in Navigation Course

Better safety and efficiency: AI helps detect risks earlier, optimize routes and reduce the workload and errors of the people on the bridge.

By optimizing routes and speed in real time according to weather, currents and traffic, which shortens passages and avoids unnecessary power demand.

Basic programming, preferably in Python, is recommended to follow the modeling content, together with foundations of algebra and statistics.

No. The course treats AI as decision support: recommendations and alerts are always subject to human validation and to the rules of safe navigation.

Yes. One module covers the application of AI to autonomous and semi-autonomous navigation systems in both crewed and uncrewed vessels.

You define a vessel, mission and navigation problem, select data and sensors, design an AI solution and evaluate its accuracy, limitations, risks and scalability.

The course comprises 300 hours of study across 5 modules of 60 hours each, with asynchronous online modules, discussion forums and optional live sessions for questions.

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Online admission

Admission process

No waiting for a call back. If your documents are in order, you can enroll in the same session.

Entry requirements
  • Profile: aimed at deck officers, masters and fleet management staff, naval engineers and software developers, data analysts and maritime consultants, researchers, and students of nautical studies and marine engineering; a degree in computer science, mathematics, physics or a related field, or experience in software development or robotics, is recommended, together with basic programming (preferably Python), linear algebra and statistics.
  • Language: sufficient command of English to follow the classes and the technical documentation.
  • Technical resources: a computer with a webcam, microphone and stable internet connection, an installed and configured development environment and, optionally, access to cloud computing platforms.
  • Admission: online application, review of your profile and a short interview with the academic team.
  1. 01
    Application
    Your details, background and documents.
  2. 02
    Instant review
    We check that everything is complete and in order.
  3. 03
    Enrollment
    Choose full payment or instalments and reserve your place.
Start my applicationFrom $196 deposit

Admission is provisional until the committee validates the documents provided. If you do not meet the requirements, the full amount is refunded.

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