Industry 4.0 is waiting for you. Enroll now and develop your robot by mastering the latest tools used in the sector"

Industry 4.0 is currently experiencing its best moment, so Robotics and the field of artificial vision have opened very attractive professional fields for the future of those professionals in these sectors, including computer scientists.

This Postgraduate diploma is aimed at graduates who wish to specialize in the field of Robot Navigation Systems for which the expert teaching team has prepared a syllabus that provides students with all the knowledge in this area so that upon completing this 6-month diploma course they will be able to master the main techniques and tools currently used in the development of Robotics.

Thus, this online program delves into the vision techniques used in Robotics, the development and understanding of algorithms, the improvement of the technique of image processing and analysis, as well as visual SLAM, Robot localization and Simultaneous Mapping using the latest techniques of Artificial Vision used.

The IT professional who wishes to progress in this field has an excellent opportunity to achieve their goals in a comfortable and flexible way, since this program allows access without fixed schedules to the entire content of the syllabus In this way, you can distribute the teaching load of the modules of this this syllabus, according to your needs. This allows you to combine your personal responsibilities with quality learning.

Organizing shelves in a warehouse, parking an autonomous car or delivering a package by steering a drone in an unfamiliar environment, all this can be achieved with Slam Visual and this Postgraduate diploma. Click and sign up"

This Postgraduate diploma in Robot Navigation Systems contains the most complete and up-to-date program on the market. The most important features include:

• Development of case studies presented by experts in robotic engineering
• The graphic, schematic, and practical contents with which they are created, provide scientific and practical information on the disciplines that are essential for professional practice
• Practical exercises where self-assessment can be used to improve learning
• Its special emphasis on innovative methodologies
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

You are one step away from obtaining a Postgraduate diploma that will make you grow. Access to all the knowledge in Robotics with professionals of the sector"

The program’s teaching staff includes professionals from the sector who contribute their work experience to this educational program, as well as renowned specialists from leading societies and prestigious universities.

Its multimedia content, developed with the latest educational technology, will allow the professional a situated and contextual learning, that is, a simulated environment that will provide an immersive education programmed to prepare in real situations.

This program is designed around Problem-Based Learning, whereby the professionals must try to solve the different professional practice situations that arise throughout the program. This will be done with the help of an innovative system of interactive videos made by renowned experts.

Enroll now and don't miss the opportunity to be able to create alternative trajectories for Mobile Robots"

Master 3D Vision systems and launch your next project with this Postgraduate diploma"

The syllabus of this Postgraduate diploma has been structured in four modules, in which the teaching team has used the Relearning methodology, which allows the acquisition of learning in a progressive and natural way throughout the program. This is possible thanks to the reiteration of key concepts related to Robotics, so the student will be able to assimilate concepts more efficiently and quickly. In this way, the student will reach a high degree of specialization in the 600 teaching hours foreseen for this course.

Solves the main problems in the localization of Robots with a teaching team specialized in this area"

Module 1. Robotics. Robot Design and Modeling

1.1. Robotics and Industry 4.0

1.1.1. Robotics and Industry 4.0
1.1.2. Application Fields and Use Cases
1.1.3. Sub-Areas of Specialization in Robotics

1.2. Robot Hardware and Software Architectures

1.2.1. Hardware Architectures and Real-Time
1.2.2. Robot Software Architectures
1.2.3. Communication Models and Middleware Technologies
1.2.4. Robot Operating System (ROS) Software Integration

1.3. Mathematical Modeling of Robots

1.3.1. Mathematical Representation of Rigid Solids
1.3.2. Rotations and Translations
1.3.3. Hierarchical State Representation
1.3.4. Distributed Representation of the State in ROS (TF Library)

1.4. Robot Kinematics and Dynamics

1.4.1. Kinematics
1.4.2. Dynamics
1.4.3. Underactuated Robots
1.4.4. Redundant Robots

1.5. Robot Modeling and Simulation

1.5.1. Robot Modeling Technologies
1.5.2. Robot Modeling with URDF
1.5.3. Robot Simulation
1.5.4. Modeling with Gazebo Simulator

1.6. Robot Manipulators

1.6.1. Types of Manipulator Robots
1.6.2. Kinematics
1.6.3. Dynamics
1.6.4. Simulation

1.7. Terrestrial Mobile Robots

1.7.1. Types of Terrestrial Mobile Robots
1.7.2. Kinematics
1.7.3. Dynamics
1.7.4. Simulation

1.8. Aerial Mobile Robots

1.8.1. Types of Aerial Mobile Robots
1.8.2. Kinematics
1.8.3. Dynamics
1.8.4. Simulation

1.9. Aquatic Mobile Robots

1.9.1. Types of Aquatic Mobile Robots
1.9.2. Kinematics
1.9.3. Dynamics
1.9.4. Simulation

1.10. Bioinspired Robots

1.10.1. Humanoids
1.10.2. Robots with Four or More Legs
1.10.3. Modular Robots
1.10.4. Robots with Flexible Parts (Soft-Robotics)

Module 2. Planning Algorithms in Robots

2.1. Classical Planning Algorithms

2.1.1. Discrete Planning: State Space
2.1.2. Planning Problems in Robotics. Robotic Systems Models
2.1.3. Classification of Planners

2.2. The Trajectory Planning Problem in Mobile Robots

2.2.1. Forms of Environment Representation: Graphs
2.2.2. Search Algorithms in Graphs
2.2.3. Introduction of Costs in Networks
2.2.4. Search Algorithms in Heavy Networks
2.2.5. Algorithms with any Angle Approach

2.3. Planning in High Dimensional Robotic Systems

2.3.1. High Dimensionality Robotics Problems: Manipulators
2.3.2. Direct/Inverse Kinematic Model
2.3.3. Sampling Planning Algorithms PRM and RRT
2.3.4. Planning Under Dynamic Constraints

2.4. Optimal Sampling Planning

2.4.1. Problem of Sampling-Based Planners
2.4.2. RRT* Probabilistic Optimality Concept
2.4.3. Reconnection Step: Dynamic Constraints
2.4.4. CForest. Parallelizing Planning

2.5. Real Implementation of a Motion Planning System

2.5.1. Global Planning Problem. Dynamic Environments
2.5.2. Cycle of Action, Sensorization. Acquisition of Information from the Environment
2.5.3. Local and Global Planning

2.6. Coordination in Multi-Robot Systems I: Centralized System

2.6.1. Multirobot Coordination Problem
2.6.2. Collision Detection and Resolution: Trajectory Modification with Genetic Algorithms
2.6.3. Other Bio-Inspired Algorithms: Particle Swarm and Fireworks
2.6.4. Collision Avoidance by Choice of Maneuver Algorithm

2.7. Coordination in Multi-Robot Systems II: Distributed Approaches I

2.7.1. Use of Complex Objective Functions
2.7.2. Pareto Front
2.7.3. Multi-Objective Evolutionary Algorithms

2.8. Coordination in Multi-Robot Systems III: Distributed Approaches II

2.8.1. Order 1 Planning Systems
2.8.2. ORCA Algorithm
2.8.3. Addition of Kinematic and Dynamic Constraints in ORCA

2.9. Decision Planning Theory

2.9.1. Decision Theory
2.9.2. Sequential Decision Systems
2.9.3. Sensors and Information Spaces
2.9.4. Planning for Uncertainty in Sensing and Actuation

2.10. Reinforcement Learning Planning Systems

2.10.1. Obtaining the Expected Reward of a System
2.10.2. Mean Reward Learning Techniques
2.10.3. Inverse Reinforcement Learning

Module 3. Artificial Vision Techniques in Robotics: Image Processing and Analysis

3.1. Computer Vision

3.1.1. Computer Vision
3.1.2. Elements of a Computer Vision System
3.1.3. Mathematical Tools

3.2. Optical Sensors for Robotics

3.2.1. Passive Optical Sensors
3.2.2. Active Optical Sensors
3.2.3. Non-Optical Sensors

3.3. Image Acquisition

3.3.1. Image Representation
3.3.2. Color Space
3.3.3. Digitizing Process

3.4. Image Geometry

3.4.1. Lens Models
3.4.2. Camera Models
3.4.3. Camera Calibration

3.5. Mathematical Tools

3.5.1. Histogram of an Image
3.5.2. Convolution
3.5.3. Fourier Transform

3.6. Image Preprocessing

3.6.1. Noise Analysis
3.6.2. Image Smoothing
3.6.3. Image Enhancement

3.7. Image Segmentation

3.7.1. Contour-Based Techniques
3.7.3. Histogram-Based Techniques
3.7.4. Morphological Operations

3.8. Image Feature Detection

3.8.1. Point of Interest Detection
3.8.2. Feature Descriptors
3.8.3. Feature Matching

3.9. 3D Vision Systems

3.9.1. 3D Perception
3.9.2. Feature Matching between Images
3.9.3. Multiple View Geometry

3.10. Computer Vision based Localization

3.10.1. The Robot Localization Problem
3.10.2. Visual Odometry
3.10.3. Sensory Fusion

Module 4. Visual SLAM. Robot Localization and Simultaneous Mapping by Computer Vision Techniques

4.1. Simultaneous Localization and Mapping (SLAM)

4.1.1. Simultaneous Localization and Mapping. SLAM
4.1.2. SLAM Applications
4.1.3. SLAM Operation

4.2. Projective Geometry

4.2.1. Pin-Hole Model
4.2.2. Estimation of Intrinsic Parameters of a Chamber
4.2.3. Homography, Basic Principles and Estimation
4.2.4. Fundamental Matrix, Principles and Estimation

4.3. Gaussian Filters

4.3.1. Kalman Filter
4.3.2. Information Filter
4.3.3. Adjustment and Parameterization of Gaussian Filters

4.4. Stereo EKF-SLAM

4.4.1. Stereo Camera Geometry
4.4.2. Feature Extraction and Search
4.4.3. Kalman Filter for Stereo SLAM
4.4.4. Stereo EKF-SLAM Parameter Setting

4.5. Monocular EKF-SLAM

4.5.1. EKF-SLAM Landmark Parameterization
4.5.2. Kalman Filter for Monocular SLAM
4.5.3. Monocular EKF-SLAM Parameter Tuning

4.6. Loop Closure Detection

4.6.1. Brute Force Algorithm
4.6.2. FABMAP
4.6.3. Abstraction Using GIST and HOG
4.6.4. Deep Learning Detection

4.7. Graph-SLAM

4.7.1. Graph-SLAM
4.7.2. RGBD-SLAM
4.7.3. ORB-SLAM

4.8. Direct Visual SLAM

4.8.1. Analysis of the Direct Visual SLAM Algorithm
4.8.2. LSD-SLAM
4.8.3. SVO

4.9. Visual Inertial SLAM

4.9.1. Integration of Inertial Measurements
4.9.2. Low Coupling: SOFT-SLAM
4.9.3. High Coupling: Vins-Mono

4.10. Other SLAM Technologies

4.10.1. Applications Beyond Visual SLAM
4.10.2. Lidar-SLAM
4.10.2. Range-Only SLAM

Discover the technological applications used in Robotics beyond the visual SLAM with this Postgraduate diploma"