Artificial intelligence in navigation course
Why this course?
The Artificial Intelligence in Navigation Course
This course prepares you to lead the technological revolution in the maritime sector. Learn to implement and manage intelligent systems that optimize the safety, efficiency, and sustainability of naval operations. Master the techniques of machine learning, computer vision, and predictive analytics applied to autonomous navigation, fleet management, and risk prediction. This course will allow you to transform data into strategic decisions and become a highly sought-after professional in the industry.
Differential Advantages
- Practical Applications: Development of real-world projects through simulation and case study analysis.
- Industry Experts: Training delivered by professionals with experience in AI and navigation.
- Cutting-Edge Tools: Access to leading artificial intelligence software and platforms.
- Networking: Connection with a community of professionals and innovative companies.
- Certification: Obtain a certificate that validates your knowledge and skills in AI applied to navigation.
- Modality: Online
- Level: Cursos
- Hours: 150 H
- Start date: 25-04-2026
Availability: 1 in stock
Who is it aimed at?
- Bridge officers, captains, and fleet management personnel looking to optimize routes, reduce fuel costs, and improve safety through AI.
- Naval engineers and software developers interested in implementing AI algorithms in maritime navigation and control systems.
- Data analysts and maritime consultants who want to understand the potential of AI for strategic decision-making in the maritime industry.
- Researchers and academics looking to explore new applications of AI in autonomous navigation and maritime risk prediction.
- Nautical and marine engineering students who want to acquire AI skills to excel in the future of the industry maritime.
Learning Flexibility
Adapted to professionals with busy schedules: asynchronous online modules, discussion forums, and optional live sessions for Q&A.
Objectives and competencies
Dynamically adapt routes:
Consider sea state, currents, weather forecasts, and vessel performance.
Optimize fuel consumption:
“Manage the speed and power of the main engine efficiently according to sea and wind conditions.”
Managing autonomous maritime traffic:
“Dynamically assess risks, prioritize safety, and optimize the route, considering environmental and navigational factors.”
Predicting and avoiding collisions:
“Use all available information (radar, AIS, visual observation) to anticipate risk scenarios and maneuver in advance, communicating intentions clearly.”
Improve navigation accuracy in adverse conditions:
Interpreting information from instruments (radar, AIS, ECDIS) and anticipating risk situations, adjusting the course with judgment and communicating intentions.
Automate critical decision-making:
Assess risks dynamically (probability/consequence), selecting effective countermeasures and prioritizing navigational safety and environmental protection.
Curriculum - Modules
- Comprehensive Maritime Incident Management: protocols, roles, and chain of command for coordinated response
- Operational Planning and Execution: briefing, routes, weather windows, and go/no-go criteria
- Rapid Risk Assessment: criticality matrix, scene control, and decision-making under pressure
- Operational Communication: VHF/GMDSS, standardized reports, and inter-agency liaison
- Tactical Mobility and Safe Boarding: RHIB maneuvers, approach, mooring, and recovery
- Equipment and Technologies: PPE, signaling, satellite tracking, and field data logging
- Immediate Care of the Affected: primary assessment, hypothermia, trauma, and stabilization for evacuation
- Adverse Environmental Conditions: swell, Visibility, flows, and operational mitigation
Simulation and training: critical scenarios, use of VR/AR, and exercises with performance metrics
Documentation and continuous improvement: lessons learned, indicators (MTTA/MTTR), and SOP updates
- Introduction to AI in Navigation: History, Evolution, and Future
- Machine Learning Fundamentals: Supervised, Unsupervised, and Reinforcement Learning
- Key Algorithms for Navigation: Regression, Classification, Clustering, and Neural Networks
- Navigation Data Processing: Cleaning, Transformation, and Normalization
- Sensors and Data Sources: GNSS, IMU, Radar, Lidar, Cameras, and Weather Data
- Predictive Modeling: Predicting Trajectories, Traffic Behavior, and Environmental Conditions
- Route Optimization: Search Algorithms, A*, Dijkstra’s Theorem, and Heuristics
- Autonomous Navigation: Mission Planning, Control, and Risk Management
- Vision Artificial intelligence: object detection, signal recognition, and scene analysis
Ethics and security in AI applied to navigation: responsibility, transparency, and robustness
‘
- Introduction to AI: basic concepts, types of AI, machine and deep learning.
- Maritime Sensors: types, operation, data processing (radar, lidar, cameras).
- Maritime Data: sources, formats (AIS, ECDIS, metocean), quality, and preprocessing.
- Computer Vision: object detection and classification (ships, buoys, obstacles).
- Natural Language Processing (NLP): communication analysis and reporting.
- Predictive Modeling: forecasting maritime traffic, swells, and weather conditions.
- Maritime Robotics: autonomous surface vehicles (USVs) and underwater vehicles (AUVs).
- Route Planning: optimization algorithms, safety and efficiency.
- Navigation assistance: early warning and decision-making systems.
- Ethics and regulation of AI in maritime navigation.
‘
- Introduction to AI: basic concepts, types, and applications in the maritime sector
- Fundamentals of optimization: classical vs. AI
- Maritime Data Collection and Preprocessing: Sources, Quality, and Formats
- AI Algorithms for Route Prediction: Regression, Time Series, and Neural Networks
- Modeling Environmental Factors: Wind, Waves, Currents, and Their Impact on Navigation
- Real-Time Route Optimization: Genetic Algorithms and Reinforcement Learning
- Safety and Energy Efficiency Considerations: Minimizing Risks and Consumption
- AI Tools and Platforms for Maritime Route Optimization: Software and APIs
- Evaluation and Validation of AI Models: Metrics, Testing, and Sensitivity Analysis
- Case Studies and Practical Applications in the Shipping Industry
‘
- Introduction to AI in Navigation: History, Trends, and Challenges
- Navigation Sensors: IMU, GNSS, LiDAR, Cameras, and Odometers
- Sensor Data Filtering: Kalman, Particle Filter, and Sensor Fusion
- Localization and Simultaneous Mapping (SLAM): Algorithms and Applications
- Route Planning: A*, Dijkstra, RRT, and AI-Based Variants
- Autonomous Navigation Control: PID, MPC, and Reinforcement Learning
- Computer Vision: Object Detection, Segmentation, and Tracking
- Navigation in Dynamic Environments: Obstacle Avoidance and Reactive Planning
- Ethics and Safety in the AI for Navigation: Considerations and Standards
Case Studies and Applications: Autonomous Vehicles, Underwater Robots, and Drones
‘
- System Architecture and Components: Structural design, materials, and subsystems (mechanical, electrical, electronic, and fluid) with selection and assembly criteria for marine environments
- Fundamentals and Principles of Operation: Physical and engineering foundations (thermodynamics, fluid mechanics, electricity, control, and materials) that explain performance and operating limits
- Safety and Environmental (SHE): Risk analysis, PPE, LOTO, hazardous atmospheres, spill and waste management, and emergency response plans
- Applicable Regulations and Standards: IMO/ISO/IEC requirements and local regulations;
- Conformance criteria, certification, and best practices for operation and maintenance
- Inspection, testing, and diagnostics: Visual/dimensional inspection, functional testing, data analysis, and predictive techniques (vibration, thermography, fluid analysis) to identify root causes
- Preventive and predictive maintenance: Hourly/cycle/seasonal plans, lubrication, adjustments, calibrations, consumable replacement, post-service verification, and operational reliability
- Instrumentation, tools, and metrology: Measuring and testing equipment, diagnostic software, calibration and traceability; selection criteria, safe use, and storage
- Onboard integration and interfaces: Mechanical, electrical, fluid, and data compatibility; Sealing and watertightness, EMC/EMI, corrosion protection, and interoperability testing.
Quality, acceptance testing, and commissioning: process and materials control, FAT/SAT, bench and sea trials, go/no-go criteria, and evidence documentation.
Technical documentation and integrated practice: logs, checklists, reports, and a complete case study (safety → diagnosis → intervention → verification → report) applicable to any system.
Plan de estudio - Módulos
- Comprehensive Maritime Incident Management: protocols, roles, and chain of command for coordinated response
- Operational Planning and Execution: briefing, routes, weather windows, and go/no-go criteria
- Rapid Risk Assessment: criticality matrix, scene control, and decision-making under pressure
- Operational Communication: VHF/GMDSS, standardized reports, and inter-agency liaison
- Tactical Mobility and Safe Boarding: RHIB maneuvers, approach, mooring, and recovery
- Equipment and Technologies: PPE, signaling, satellite tracking, and field data logging
- Immediate Care of the Affected: primary assessment, hypothermia, trauma, and stabilization for evacuation
- Adverse Environmental Conditions: swell, Visibility, flows, and operational mitigation
Simulation and training: critical scenarios, use of VR/AR, and exercises with performance metrics
Documentation and continuous improvement: lessons learned, indicators (MTTA/MTTR), and SOP updates
- Introduction to AI in Navigation: History, Evolution, and Future
- Machine Learning Fundamentals: Supervised, Unsupervised, and Reinforcement Learning
- Key Algorithms for Navigation: Regression, Classification, Clustering, and Neural Networks
- Navigation Data Processing: Cleaning, Transformation, and Normalization
- Sensors and Data Sources: GNSS, IMU, Radar, Lidar, Cameras, and Weather Data
- Predictive Modeling: Predicting Trajectories, Traffic Behavior, and Environmental Conditions
- Route Optimization: Search Algorithms, A*, Dijkstra’s Theorem, and Heuristics
- Autonomous Navigation: Mission Planning, Control, and Risk Management
- Vision Artificial intelligence: object detection, signal recognition, and scene analysis
Ethics and security in AI applied to navigation: responsibility, transparency, and robustness
‘
- Introduction to AI: basic concepts, types of AI, machine and deep learning.
- Maritime Sensors: types, operation, data processing (radar, lidar, cameras).
- Maritime Data: sources, formats (AIS, ECDIS, metocean), quality, and preprocessing.
- Computer Vision: object detection and classification (ships, buoys, obstacles).
- Natural Language Processing (NLP): communication analysis and reporting.
- Predictive Modeling: forecasting maritime traffic, swells, and weather conditions.
- Maritime Robotics: autonomous surface vehicles (USVs) and underwater vehicles (AUVs).
- Route Planning: optimization algorithms, safety and efficiency.
- Navigation assistance: early warning and decision-making systems.
- Ethics and regulation of AI in maritime navigation.
‘
- Introduction to AI: basic concepts, types, and applications in the maritime sector
- Fundamentals of optimization: classical vs. AI
- Maritime Data Collection and Preprocessing: Sources, Quality, and Formats
- AI Algorithms for Route Prediction: Regression, Time Series, and Neural Networks
- Modeling Environmental Factors: Wind, Waves, Currents, and Their Impact on Navigation
- Real-Time Route Optimization: Genetic Algorithms and Reinforcement Learning
- Safety and Energy Efficiency Considerations: Minimizing Risks and Consumption
- AI Tools and Platforms for Maritime Route Optimization: Software and APIs
- Evaluation and Validation of AI Models: Metrics, Testing, and Sensitivity Analysis
- Case Studies and Practical Applications in the Shipping Industry
‘
- Introduction to AI in Navigation: History, Trends, and Challenges
- Navigation Sensors: IMU, GNSS, LiDAR, Cameras, and Odometers
- Sensor Data Filtering: Kalman, Particle Filter, and Sensor Fusion
- Localization and Simultaneous Mapping (SLAM): Algorithms and Applications
- Route Planning: A*, Dijkstra, RRT, and AI-Based Variants
- Autonomous Navigation Control: PID, MPC, and Reinforcement Learning
- Computer Vision: Object Detection, Segmentation, and Tracking
- Navigation in Dynamic Environments: Obstacle Avoidance and Reactive Planning
- Ethics and Safety in the AI for Navigation: Considerations and Standards
Case Studies and Applications: Autonomous Vehicles, Underwater Robots, and Drones
‘
- System Architecture and Components: Structural design, materials, and subsystems (mechanical, electrical, electronic, and fluid) with selection and assembly criteria for marine environments
- Fundamentals and Principles of Operation: Physical and engineering foundations (thermodynamics, fluid mechanics, electricity, control, and materials) that explain performance and operating limits
- Safety and Environmental (SHE): Risk analysis, PPE, LOTO, hazardous atmospheres, spill and waste management, and emergency response plans
- Applicable Regulations and Standards: IMO/ISO/IEC requirements and local regulations;
- Conformance criteria, certification, and best practices for operation and maintenance
- Inspection, testing, and diagnostics: Visual/dimensional inspection, functional testing, data analysis, and predictive techniques (vibration, thermography, fluid analysis) to identify root causes
- Preventive and predictive maintenance: Hourly/cycle/seasonal plans, lubrication, adjustments, calibrations, consumable replacement, post-service verification, and operational reliability
- Instrumentation, tools, and metrology: Measuring and testing equipment, diagnostic software, calibration and traceability; selection criteria, safe use, and storage
- Onboard integration and interfaces: Mechanical, electrical, fluid, and data compatibility; Sealing and watertightness, EMC/EMI, corrosion protection, and interoperability testing.
Quality, acceptance testing, and commissioning: process and materials control, FAT/SAT, bench and sea trials, go/no-go criteria, and evidence documentation.
Technical documentation and integrated practice: logs, checklists, reports, and a complete case study (safety → diagnosis → intervention → verification → report) applicable to any system.
- Introduction to Maritime AI: History, current state, and future.
- Advanced Maritime Sensors: LiDAR, high-resolution radars, hyperspectral cameras.
- Maritime Data Processing: Calibration, sensor fusion, and big data management.
- Machine Learning for Navigation: Predictive models, neural networks, and route optimization algorithms.
- Computer Vision in Maritime Environments: Object detection, classification, and tracking in adverse conditions.
- Autonomous Navigation Systems: Architecture, control, and real-time decision-making.
- Maritime Cybersecurity: Threats, vulnerabilities, and protection strategies in autonomous systems.
- Simulation and Testing of Maritime AI Systems: Virtual environments, real-world testing, and model validation.
- Regulations and Standards for Maritime AI: Legal framework, certification, and ethical considerations.
- Case Studies and Practical Applications: Autonomous vessels, route optimization, and predictive maintenance.
‘
- Introduction to AI: History, Key Concepts, and Main Branches
- Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
- Neural Networks: Basic Architectures, Layers, and Activation Functions
- Marine Sensors: Radars, Sonars, Cameras, IMUs, GNSS, and Their Calibration
- Data Processing: Cleaning, Transformation, and Analysis of Maritime Data
- Sensor Fusion Algorithms for Autonomous Navigation
- Location and Mapping: SLAM, Visual Odometry, and Kalman Filtering
- Route Planning: A*, D*, and RRT Algorithms and Maritime Considerations
- Autonomous Control: PID, MPC, and Adaptation to the Dynamic Marine Environment
- Ethics and security in maritime AI: responsibility, transparency, and robustness
‘
- Introduction to AI in the Maritime Context: Challenges and Opportunities
- Fundamentals of Machine Learning: Types of Algorithms and Applications
- Maritime Sensors: Types, Operation, and Data Integration
- Maritime Data Processing: Cleaning, Transformation, and Analysis
- AI for Route Optimization: Predictive Models and Optimization Algorithms
- AI for Predicting Weather and Oceanographic Conditions
- Real-Time Object Detection and Tracking using AI
- AI for Maritime Traffic Management: Intelligent Control Systems
- Maritime Cybersecurity: Detecting and Preventing AI Attacks
- Ethical and Legal Considerations of AI in the Maritime Sector maritime
‘
- Introduction to AI: basic concepts, machine learning, and types.
- Marine Sensors: radars, LiDAR, cameras, sonar, and their integration.
- Data Processing: cleaning, filtering, and analyzing sensor data.
- Perception Algorithms: detecting and tracking objects in the maritime environment.
- Location and Mapping Systems: SLAM, visual and inertial odometry.
- Autonomous Route Planning: search and optimization algorithms.
- Autonomous Control Systems: predictive control, robust control, and adaptive control.
- Ethics in Maritime AI: ethical considerations in autonomous decision-making.
- Regulations and Standards: current and future regulations for Autonomous maritime systems.
Case studies: AI applications in maritime navigation and their results.
‘
Career opportunities
- Autonomous Navigation Software Developer: Design, development, and testing of AI algorithms for navigation systems.
- Marine Robotics Engineer: Integration of AI into autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs).
- Navigation Data Analyst: Processing and analysis of large navigation datasets to optimize routes and predict risks.
- AI Consultant for the Maritime Industry: Advising shipping companies and ports on the implementation of AI solutions.
- AI Researcher Applied to Navigation: Development of new AI techniques to improve navigation safety and efficiency.
- AI Navigation Simulation Specialist: Creation of realistic simulation environments for the training and testing of autonomous navigation systems.
- Autonomous Navigation Systems Cybersecurity Expert: Protecting navigation systems against cyberattacks.
- AI Project Manager in Navigation: Leading and coordinating AI development and implementation projects in the maritime industry.
“`
Admission requirements
Academic/professional profile:
Degree/Bachelor's degree in Nautical Science/Maritime Transport, Naval/Marine Engineering, or a related field; or proven professional experience in bridge/operations.
Language proficiency:
Recommended functional maritime English (SMCP) for simulations and technical materials.
5. Induction
Updated resume, copy of degree or seaman's book, ID card/passport, letter of motivation.
Technical requirements (for online):
Equipment with camera/microphone, stable connection, ≥ 24” monitor recommended for ECDIS/Radar-ARPA.
Admission process and dates
1. Online
application
(form + documents).
2. Academic review and interview
(profile/objectives/schedule compatibility).
3. Admission decision
(+ scholarship proposal if applicable).
4. Reservation of place
(deposit) and registration.
5. Induction
(access to campus, calendars, simulator guides).
Scholarships and grants
- AI Fundamentals: Discover the basic principles of artificial intelligence and its application in modern navigation.
- Smart Sensors: Learn how to use and understand data from advanced sensors for safer and more efficient navigation.
- Navigation Algorithms: Master the key algorithms that allow AI systems to plan optimal routes and avoid obstacles in real time.
- Automation and Safety: Explore how AI can automate navigation tasks, improve maritime safety, and reduce the risk of human error.
- Case Studies: Analyze real-world examples of AI implementation on vessels and learn from industry experiences.
Testimonials
I implemented an AI system on a fleet of merchant ships that optimized navigation routes by 12%, reducing fuel consumption and CO2 emissions, while improving expected arrival times with 98% accuracy.
The Innovation, Technology, and Marine Startups course provided me with the tools and knowledge necessary to develop my sustainable aquaculture project. I learned to analyze the market, create a viable business model, and use emerging technologies to optimize production. Thanks to the training, I secured funding, and my startup is currently in a growth phase, creating jobs and contributing to the conservation of the marine ecosystem.
I implemented an AI system that optimized the navigation routes of a fleet of cargo ships, reducing fuel consumption by 12% and delivery times by 8%, exceeding the initial expectations of the project.
I implemented an AI system on a cargo ship that optimized navigation routes by 12%, reducing fuel consumption and CO2 emissions, as well as improving delivery times by 5% compared to traditional methods.
Frequently asked questions
Improve safety and efficiency.
Yes. The itinerary includes ECDIS/Radar-ARPA/BRM with harbor, ocean, fog, storm, and SAR scenarios.
Online with live sessions; hybrid option for simulator/practical placements through agreements.
Optimizing navigation routes in real time by considering variables such as weather, ocean currents and traffic, minimizing travel time and therefore fuel consumption.
Recommended functional SMCP. We offer support materials for standard phraseology.
Yes, with a relevant degree or experience in maritime/port operations. The admissions interview will confirm suitability.
Optional (3–6 months) through Companies & Collaborations and the Alumni Network.
Simulator practice (rubrics), defeat plans, SOPs, checklists, micro-tests and applied TFM.
A degree from Navalis Magna University + operational portfolio (tracks, SOPs, reports and KPIs) useful for audits and employment.
- Introduction to AI: basic concepts, machine learning, and types.
- Marine Sensors: radars, LiDAR, cameras, sonar, and their integration.
- Data Processing: cleaning, filtering, and analyzing sensor data.
- Perception Algorithms: detecting and tracking objects in the maritime environment.
- Location and Mapping Systems: SLAM, visual and inertial odometry.
- Autonomous Route Planning: search and optimization algorithms.
- Autonomous Control Systems: predictive control, robust control, and adaptive control.
- Ethics in Maritime AI: ethical considerations in autonomous decision-making.
- Regulations and Standards: current and future regulations for Autonomous maritime systems.
Case studies: AI applications in maritime navigation and their results.
‘
Request information
- Complete the Application Form
- Attach your CV/Qualifications (if you have them to hand).
- Indicate your preferred cohort (January/May/September) and whether you want the hybrid option with simulator sessions.
Teachers
Eng. Tomás Riera
Full Professor
Eng. Tomás Riera
Full Professor
Naval engineer specializing in the selection, integration, and optimization of propulsion systems for yachts, high-speed craft, and service vessels.
Eng. Sofía Marquina
Full Professor
Eng. Sofía Marquina
Full Professor
Materials engineer specializing in marine composites and corrosion protection systems for ships, yachts, and port structures.
Eng. Javier Bañuls
Full Professor
Eng. Javier Bañuls
Full Professor
Naval electrical engineer with over fifteen years of experience in the design, integration, and operation of power and automation systems.
Dr. Nuria Llobregat
Full Professor
Dr. Nuria Llobregat
Full Professor
Specialist in meteorological and operational decision-making for coastal and offshore navigation, integrating wind, wave, and current models.
Dr. Pau Ferrer
Full Professor
Dr. Pau Ferrer
Full Professor
Naval engineer with a PhD in applied hydrodynamics and over a decade of experience designing high-performance hulls and marine structures.
Cap. Javier Abaroa (MCA)
Full Professor
Cap. Javier Abaroa (MCA)
Full Professor
MCA-certified captain with command of ocean-going vessels, specializing in advanced maneuvering, navigation management, and bridge safety.