Introduction to the Program

Do you have a project in mind but lack expertise? In this program, an expert teaching team in Robotics gives you the tools to advance in Industry 4.0"

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Robotics is part of our daily lives. Machines are not only present in the industrial sector, which has grown enormously thanks to technical and scientific advances, but Robotics has also been brought closer to the public. It is no longer rare to see how any person with any kind of qualifications can drive a drone, have virtual glasses to immerse themselves in the latest video game or see houses equipped with this technology that solves all kinds of problems.

Robotics is a common term, current and with a broad future for IT professionals who wish to specialize in an area with great growth possibilities. This Professional master’s degree provides extensive knowledge that will allow students to acquire learning in Augmented Reality, Artificial Intelligence, aerospace or industrial technology fields. All this will allow students to access companies in different sectors or create their own Robotics projects.

In this 100% online program, in order for students to achieve their goal, TECH has brought together a team of specialized professionals with extensive experience in prestigious international projects in the Robotics field. This teaching staff provides IT professionals with a theoretical-practical approach, where they will not only learn about the latest developments in Robotics, but will also be able to learn about its application in real environments.

An excellent opportunity to progress with a qualification that, from the very beginning, provides a complete syllabus composed of video summaries, essential readings, detailed videos and self-command exercises. This way, students will acquire a global vision of Robotics in a convenient way, as they will be able to access all the content whenever they wish and distribute the teaching load according to their personal needs. This will allow students to balance academic learning at the forefront of their field of study with their personal responsibilities.

Connect whenever you want, at any time to all the content of this university program. TECH adapts to you”

 

This Professional master’s degree in Robotics 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

Enroll now and don't miss the opportunity to advance in the main visual SLAM technologies"

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.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations.

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

Develop clean and efficient PLC programming techniques with this university qualification"

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Master the most advanced Robotics thanks to this program's approach on hardware and software agents"

Syllabus

In this program, taught online, students have access to an extensive content in Robotics field of structured in 10 modules that can be accessed at any time The theoretical-practical vision of the syllabus can be acquired in a more agile way thanks to the multimedia resources and the Relearning system, based on the reiteration of content This way, IT professionals will have all the necessary knowledge at their fingertips to be able to advance in this area. 

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Enroll now in a program that brings you the latest developments in Robotics and industry 4.0”

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. Intelligent Agents. Application of Artificial Intelligence to Robots and Softbots

2.1. Intelligent Agents and Artificial Intelligence

2.1.1. Intelligent Robots. Artificial Intelligence
2.1.2. Intelligent Agents

2.1.2.1. Hardware Agents. Robots
2.1.2.2. Software Agents. Softbots

2.1.3. Robotics Applications

2.2. Brain-Algorithm Connection

2.2.1. Biological Inspiration of Artificial Intelligence
2.2.2. Reasoning Implemented in Algorithms. Typology
2.2.3. Demonstrability of Results in Artificial Intelligence Algorithms
2.2.4. Evolution of Algorithms up to Deep Learning

2.3. Search Algorithms in the Solution Space

2.3.1. Elements in Solution Space Searches
2.3.2. Solution Search Algorithms in Artificial Intelligence Problems
2.3.3. Applications of Search and Optimization Algorithms
2.3.4. Search Algorithms Applied to Machine Learning

2.4. Machine Learning

2.4.1. Machine Learning
2.4.2. Supervised Learning Algorithms
2.4.3. Unsupervised Learning Algorithms
2.4.4. Reinforcement Learning Algorithms

2.5. Supervised Learning

2.5.1. Supervised Learning Methods
2.5.2. Decision Trees for Classification
2.5.3. Support Vector Machines
2.5.4. Artificial Neural Networks
2.5.5. Applications of Supervised Learning

2.6. Unsupervised Learning

2.6.1. Unsupervised Learning
2.6.2. Kohonen Networks
2.6.3. Self-Organizing Maps
2.6.4. K-Means Algorithm

2.7. Reinforcement Learning

2.7.1. Reinforcement Learning
2.7.2. Agents Based on Markov Processes
2.7.3. Reinforcement Learning Algorithms
2.6.4. Reinforcement Learning Applied to Robotics

2.8. Artificial Neural Networks and Deep Learning

2.8.1. Artificial Neural Networks. Typology
2.8.2. Applications of Neural Networks
2.8.3. Transformation from Machine Learning to Deep Learning
2.8.4.  Deep Learning Applications

2.9. Probabilistic Inference

2.9.1. Probabilistic Inference
2.9.2. Types of Inference and Method Definition
2.9.3. Bayesian Inference as a Case Study
2.9.4. Nonparametric Inference Techniques
2.9.5. Gaussian Filters

2.10. From Theory to Practice: Developing an Intelligent Robotic Agent

2.10.1. Inclusion of Supervised Learning Modules in a Robotic Agent
2.10.2. Inclusion of Reinforcement Learning Modules in a Robotic Agent
2.10.3. Architecture of a Robotic Agent Controlled by Artificial Intelligence
2.10.4. Professional Tools for the Implementation of the Intelligent Agent
2.10.5. Phases of the Implementation of AI Algorithms in Robotic Agents

Module 3. Robotics in the Automation of Industrial Processes

3.1. Design of Automated Systems

3.1.1. Hardware Architectures
3.1.2. Programmable Logic Controllers
3.1.3. Industrial Communication Networks

3.2. Advanced Electrical Design I: Automation

3.2.1. Design of Electrical Panels and Symbology
3.2.2. Power and Control Circuits. Harmonics
3.2.3. Protection and Grounding Elements

3.3. Advanced Electrical Design II: Determinism and Safety

3.3.1. Machine Safety and Redundancy
3.3.2. Safety Relays and Triggers
3.3.3. Safety PLCs
3.3.4. Safe Networks

3.4. Electrical Actuation

3.4.1. Motors and Servomotors
3.4.2. Frequency Inverters and Controllers
3.4.3. Electrically Actuated Industrial Robotics

3.5. Hydraulic and Pneumatic Actuation

3.5.1. Hydraulic Design and Symbology
3.5.2. Pneumatic Design and Symbology
3.5.3. ATEX Environments in Automation

3.6. Transducers in Robotics and Automation

3.6.1. Position and Velocity Measurement
3.6.2. Force and Temperature Measurement
3.6.3. Presence Measurement
3.6.4. Vision Sensors

3.7. Programming and Configuration of Programmable Logic Controllers PLCs

3.7.1. PLC Programming: LD
3.7.2. PLC Programming: ST
3.7.3. PLC Programming: FBD and CFC
3.7.4. PLC Programming: SFC

3.8. Programming and Configuration of Equipment in Industrial Plants

3.8.1. Programming of Drives and Controllers
3.8.2. HMI Programming
3.8.3. Programming of Manipulator Robots

3.9. Programming and Configuration of Industrial Computer Equipment

3.9.1. Programming of Vision Systems
3.9.2. SCADA/Software Programming
3.9.3. Network Configuration

3.10. Automation Implementation

3.10.1. State Machine Design
3.10.2. Implementation of State Machines in PLCs
3.10.3. Implementation of Analog PID Control Systems in PLCs
3.10.4. Automation Maintenance and Code Hygiene
3.10.5. Automation and Plant Simulation

Module 4. Automatic Control Systems in Robotics

4.1. Analysis and Design of Nonlinear Systems

4.1.1. Analysis and Modeling of Nonlinear Systems
4.1.2. Feedback Control
4.1.3. Linearization by Feedback

4.2. Design of Control Techniques for Advanced Non-linear Systems

4.2.1. Sliding Mode control
4.2.2. Lyapunov and Backstepping Control
4.2.3. Control Based on Passivity

4.3. Control Architectures

4.3.1. The Robotics Paradigm
4.3.2. Control Architectures
4.3.3. Applications and Examples of Control Architectures

4.4. Motion Control for Robotic Arms

4.4.1. Kinematic and Dynamic Modeling
4.4.2. Control in Joint Space
4.4.3. Control in Operational Space

4.5. Actuator Force Control

4.5.1. Force Control
4.5.2. Impedance Control
4.5.3. Hybrid Control

4.6. Terrestrial Mobile Robots

4.6.1. Equations of Motion
4.6.2. Control Techniques for Terrestrial Robots
4.6.3. Mobile Manipulators

4.7. Aerial Mobile Robots

4.7.1. Equations of Motion
4.7.2. Control Techniques in Aerial Robots
4.7.3. Aerial Manipulation

4.8. Control Based on Machine Learning Techniques

4.8.1. Control Using Supervised Learning
4.8.2. Control Using Reinforced Learning
4.8.3. Control Using Non-Supervised Learning

4.9. Vision-Based Control

4.9.1. Position-Based Visual Servoing
4.9.2. Image-Based Visual Servoing
4.9.3. Hybrid Visual Servoing

4.10. Predictive Control

4.10.1. Models and State Estimation
4.10.2. MPC Applied to Mobile Robots
4.10.3. MPC Applied to UAVs

Module 5. Planning Algorithms in Robots

5.1. Classical Planning Algorithms

5.1.1. Discrete Planning: State Space
5.1.2. Planning Problems in Robotics. Robotic Systems Models
5.1.3. Classification of Planners

5.2. The Trajectory Planning Problem in Mobile Robots

5.2.1. Forms of Environment Representation: Graphs
5.2.2. Search Algorithms in Graphs
5.2.3. Introduction of Costs in Networks
5.2.4. Search Algorithms in Heavy Networks
5.2.5. Algorithms with any Angle Approach

5.3. Planning in High Dimensional Robotic Systems

5.3.1. High Dimensionality Robotics Problems: Manipulators
5.3.2. Direct/Inverse Kinematic Model
5.3.3. Sampling Planning Algorithms PRM and RRT
5.3.4. Planning Under Dynamic Constraints

5.4. Optimal Sampling Planning

5.4.1. Problem of Sampling-Based Planners
5.4.2. RRT* Probabilistic Optimality Concept
5.4.3. Reconnection Step: Dynamic Constraints
5.4.4. CForest. Parallelizing Planning

5.5. Real Implementation of a Motion Planning System

5.5.1. Global Planning Problem. Dynamic Environments
5.5.2. Cycle of Action, Sensorization. Acquisition of Information from the Environment
5.5.3. Local and Global Planning

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

5.6.1. Multirobot Coordination Problem
5.6.2. Collision Detection and Resolution: Trajectory Modification with Genetic Algorithms
5.6.3. Other Bio-Inspired Algorithms: Particle Swarm and Fireworks
5.6.4. Collision Avoidance by Choice of Maneuver Algorithm

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

5.7.1. Use of Complex Objective Functions
5.7.2. Pareto Front
5.7.3. Multi-Objective Evolutionary Algorithms

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

5.8.1. Order 1 Planning Systems
5.8.2. ORCA Algorithm
5.8.3. Addition of Kinematic and Dynamic Constraints in ORCA

5.9. Decision Planning Theory

5.9.1. Decision Theory
5.9.2. Sequential Decision Systems
5.9.3. Sensors and Information Spaces
5.9.4. Planning for Uncertainty in Sensing and Actuation

5.10. Reinforcement Learning Planning Systems

5.10.1. Obtaining the Expected Reward of a System
5.10.2. Mean Reward Learning Techniques
5.10.3. Inverse Reinforcement Learning

Module 6. Artificial Vision Techniques in Robotics: Image Processing and Analysis

6.1. Computer Vision

6.1.1. Computer Vision
6.1.2. Elements of a Computer Vision System
6.1.3. Mathematical Tools

6.2. Optical Sensors for Robotics

6.2.1. Passive Optical Sensors
6.2.2. Active Optical Sensors
6.2.3. Non-Optical Sensors

6.3. Image Acquisition

6.3.1. Image Representation
6.3.2. Color Space
6.3.3. Digitizing Process

6.4. Image Geometry

6.4.1. Lens Models
6.4.2. Camera Models
6.4.3. Camera Calibration

6.5. Mathematical Tools

6.5.1. Histogram of an Image
6.5.2. Convolution
6.5.3. Fourier Transform

6.6. Image Preprocessing

6.6.1. Noise Analysis
6.6.2. Image Smoothing
6.6.3. Image Enhancement

6.7. Image Segmentation

6.7.1. Contour-Based Techniques
6.7.3. Histogram-Based Techniques
6.7.4. Morphological Operations

6.8. Image Feature Detection

6.8.1. Point of Interest Detection
6.8.2. Feature Descriptors
6.8.3. Feature Matching

6.9. 3D Vision Systems

6.9.1. 3D Perception
6.9.2. Feature Matching between Images
6.9.3. Multiple View Geometry

6.10. Computer Vision based Localization

6.10.1. The Robot Localization Problem
6.10.2. Visual Odometry
6.10.3. Sensory Fusion

Module 7. Robot Visual Perception Systems with Automatic Learning

7.1. Unsupervised Learning Methods applied to Computer Vision

7.1.1. Clustering
7.1.2. PCA
7.1.3. Nearest Neighbors
7.1.4. Similarity and Matrix Decomposition

7.2. Supervised Learning Methods Applied to Computer Vision

7.2.1. “Bag of words” Concept
7.2.2. Support Vector Machine
7.2.3. Latent Dirichlet Allocation
7.2.4. Neural Networks

7.3. Deep Neural Networks:: Structures, Backbones and Transfer Learning

7.3.1. Feature Generating Layers

7.3.3.1. VGG
7.3.3.2. Densenet
7.3.3.3. ResNet
7.3.3.4. Inception
7.3.3.5. GoogleNet

7.3.2. Transfer Learning
7.3.3. The Data Preparation for Training

7.4. Computer Vision with Deep Learning I: Detection and Segmentation

7.4.1. YOLO and SSD Differences and Similarities
7.4.2. Unet
7.4.3. Other Structures

7.5. Computer Vision with Deep Learning II: Generative Adversarial Networks

7.5.1. Image Super-Resolution Using GAN
7.5.2. Creation of Realistic Images
7.5.3. Scene Understanding

7.6. Learning Techniques for Localization and Mapping in Mobile Robotics

7.6.1. Loop Closure Detection and Relocation
7.6.2. Magic Leap. Super Point and Super Glue
7.6.3. Depth from Monocular

7.7. Bayesian Inference and 3D Modeling

7.7.1. Bayesian Models and “Classical” Learning
7.7.2. Implicit Surfaces with Gaussian Processes (GPIS)
7.7.3. 3D Segmentation Using GPIS
7.7.4. Neural Networks for 3D Surface Modeling

7.8. End-to-End Applications of Deep Neural Networks

7.8.1. End-to-End System. Example of Person Identification
7.8.2. Object Manipulation with Visual Sensors
7.8.3. Motion Generation and Planning with Visual Sensors

7.9. Cloud Technologies to Accelerate the Development of Deep Learning Algorithms

7.9.1. Use of GPUs for Deep Learning
7.9.2. Agile Development with Google IColab
7.9.3. Remote GPUs, Google Cloud and AWS

7.10. Deployment of Neural Networks in Real Applications

7.10.1. Embedded Systems
7.10.2. Deployment of Neural Networks. Use
7.10.3. Network Optimizations in Deployment, Example with TensorRT

Module 8. Visual SLAM. Robot Localization and Simultaneous Mapping by Computer Vision Techniques

8.1. Simultaneous Localization and Mapping (SLAM)

8.1.1. Simultaneous Localization and Mapping. SLAM
8.1.2. SLAM Applications
8.1.3. SLAM Operation

8.2. Projective Geometry

8.2.1. Pin-Hole Model
8.2.2. Estimation of Intrinsic Parameters of a Chamber
8.2.3. Homography, Basic Principles and Estimation
8.2.4. Fundamental Matrix, Principles and Estimation

8.3. Gaussian Filters

8.3.1. Kalman Filter
8.3.2. Information Filter
8.3.3. Adjustment and Parameterization of Gaussian Filters

8.4. Stereo EKF-SLAM

8.4.1. Stereo Camera Geometry
8.4.2. Feature Extraction and Search
8.4.3. Kalman Filter for Stereo SLAM
8.4.4. Stereo EKF-SLAM Parameter Setting

8.5. Monocular EKF-SLAM

8.5.1. EKF-SLAM Landmark Parameterization
8.5.2. Kalman Filter for Monocular SLAM
8.5.3. Monocular EKF-SLAM Parameter Tuning

8.6. Loop Closure Detection

8.6.1. Brute Force Algorithm
8.6.2. FABMAP
8.6.3. Abstraction Using GIST and HOG
8.6.4. Deep Learning Detection

8.7. Graph-SLAM

8.7.1. Graph-SLAM
8.7.2. RGBD-SLAM
8.7.3. ORB-SLAM

8.8. Direct Visual SLAM

8.8.1. Analysis of the Direct Visual SLAM Algorithm
8.8.2. LSD-SLAM
8.8.3. SVO

8.9. Visual Inertial SLAM

8.9.1. Integration of Inertial Measurements
8.9.2. Low Coupling: SOFT-SLAM
8.9.3. High Coupling: Vins-Mono

8.10. Other SLAM Technologies

8.10.1. Applications Beyond Visual SLAM
8.10.2. Lidar-SLAM
8.10.2. Range-only SLAM

Module 9. Application of Virtual and Augmented Reality Technologies to Robotics

9.1. Immersive Technologies in Robotics

9.1.1. Virtual Reality in Robotics
9.1.2. Augmented Reality in Robotics
9.1.3. Mixed Reality in Robotics
9.1.4. Difference between Realities

9.2. Construction of Virtual Environments

9.2.1. Materials and Textures
9.2.2. Lighting
9.2.3. Virtual Sound and Smell

9.3. Robot Modeling in Virtual Environments

9.3.1. Geometric Modeling
9.3.2. Physical Modeling
9.3.3. Model Standardization

9.4. Modeling of Robot Dynamics and Kinematics Virtual Physical Engines

9.4.1. Physical Motors. Typology
9.4.2. Configuration of a Physical Engine
9.4.3. Physical Motors in the Industry

9.5. Platforms, Peripherals and Tools Most Commonly Used in Virtual Reality

9.5.1. Virtual Reality Viewers
9.5.2. Interaction Peripherals
9.5.3. Virtual Sensors

9.6. Augmented Reality Systems

9.6.1. Insertion of Virtual Elements into Reality
9.6.2. Types of Visual Markers
9.6.3. Augmented Reality Technologies

9.7. Metaverse: Virtual Environments of Intelligent Agents and People

9.7.1. Avatar Creation
9.7.2. Intelligent Agents in Virtual Environments
9.7.3. Construction of Multi-User Environments for VR/AR

9.8. Creation of Virtual Reality Projects for Robotics

9.8.1. Phases of Development of a Virtual Reality Project
9.8.2. Deployment of Virtual Reality Systems
9.8.3. Virtual Reality Resources

9.9. Creating Augmented Reality Projects for Robotics

9.9.1. Phases of Development of an Augmented Reality Project
9.9.2. Deployment of Augmented Reality Projects
9.9.3. Augmented Reality Resources

9.10. Robot Teleoperation with Mobile Devices

9.10.1. Mixed Reality on Mobile Devices
9.10.2. Immersive Systems using Mobile Device Sensors
9.10.3. Examples of Mobile Projects

Module 10. Robot Communication and Interaction Systems

10.1. Speech Recognition: Stochastic Systems

10.1.1. Acoustic Speech Modeling
10.1.2. Hidden Markov Models
10.1.3. Linguistic Speech Modeling: N-Grams, BNF Grammars

10.2. Speech Recognition Deep Learning

10.2.1. Deep Neural Networks
10.2.2. Recurrent Neural Networks
10.2.3. LSTM Cells

10.3. Speech Recognition: Prosody and Environmental Effects

10.3.1. Ambient Noise
10.3.2. Multi-Speaker Recognition
10.3.3. Speech Pathologies

10.4. Natural Language Understanding: Heuristic and Probabilistic Systems

10.4.1. Syntactic-Semantic Analysis: Linguistic Rules
10.4.2. Comprehension Based on Heuristic Rules
10.4.3. Probabilistic Systems: Logistic Regression and SVM
10.4.4. Understanding Based on Neural Networks

10.5. Dialog Management: Heuristic/Probabilistic Strategies

10.5.1. Interlocutor’s Intention
10.5.2. Template-Based Dialog
10.5.3. Stochastic Dialog Management: Bayesian Networks

10.6. Dialog Management: Advanced Strategies

10.6.1. Reinforcement-Based Learning Systems
10.6.2. Neural Network-Based Systems
10.6.3. From Speech to Intention in a Single Network

10.7. Response Generation and Speech Synthesis

10.7.1. Response Generation: From Idea to Coherent Text
10.7.2. Speech Synthesis by Concatenation
10.7.3. Stochastic Speech Synthesis

10.8. Dialog Adaptation and Contextualization

10.8.1. Dialog Initiative
10.8.2. Adaptation to the Speaker
10.8.3. Adaptation to the Context of the Dialogue

10.9. Robots and Social Interactions: Emotion Recognition, Synthesis and Expression

10.9.1. Artificial Voice Paradigms: Robotic Voice and Natural Voice
10.9.2. Emotion Recognition and Sentiment Analysis
10.9.3. Emotional Voice Synthesis

10.10. Robots and Social Interactions: Advanced Multimodal Interfaces

10.10.1. Combination of Vocal and Tactile Interfaces
10.10.2. Sign Language Recognition and Translation
10.10.3. Visual Avatars: Voice to Sign Language Translation 

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View all the contents of this Professional master’s degree from the very first day and quickly advance in a technological area with a wide range of professional opportunities"

Professional Master's Degree in Robotics

Robotics is a branch of technology that deals with the design, construction, operation and use of robots. A robot is a programmable machine capable of performing complex tasks autonomously or semi-autonomously.

In robotics, sensors, actuators and control systems are used to enable robots to interact with the environment intelligently and perform specific tasks.

Robotics is applied in various fields, such as manufacturing, space exploration, medicine, agriculture, construction, among others. Robots are used to replace repetitive and dangerous tasks that are difficult for humans to perform.

A typical robot is composed of a mechanical body, a hardware and software control system, sensors and actuators. Sensors allow the robot to perceive its environment and gather information about it. The actuators allow the robot to perform actions in response to the information it receives from the sensors.

Programming is crucial in robotics, as it allows the robot to receive instructions from programmers to carry out specific tasks. Programming can be in specialized programming languages, such as robot programming language (RPL) or block programming language.

Expert robotics is a specialized field of study that combines technical and creative skills in mechanical engineering, electronics, computer science and programming to design, build and program customized and sophisticated robots. The field requires advanced knowledge in areas such as mechanics, electronics, artificial intelligence, machine learning and computer vision.

The Professional Master's Degree in Robotics is a specialized program where students acquire advanced technical and practical knowledge in fields such as mechanical engineering, electronics, computer science and programming. The main objective is to design and create customized, sophisticated and functional robots and robotic systems for use in different applications, such as service robotics, medical robotics, military robotics, exploration robotics and collaborative robotics.