#lidar matlab

[priyaohia]

It was greatly applied in satellite tracking. With its help, laser-based imaging and calculation of distances were made easy by measuring the time for a signal to return using appropriate sensors and data acquisition equipment.

[servotechinc78]

Autonomous Vehicle Control System: Engineering the Future of Smart Mobility

Autonomous vehicles (AVs) are no longer a futuristic concept—they are fast becoming a transformative force in transportation, logistics, and urban mobility. At the core of every autonomous vehicle lies a complex and highly intelligent Autonomous Vehicle Control System (AVCS), which governs everything from navigation and speed control to obstacle avoidance and decision-making. This system acts as the "brain" of the self-driving vehicle, enabling it to perceive its environment, process data, and take safe, accurate, real-time actions.

As technology rapidly evolves, the development and integration of reliable AV control systems have become one of the most critical aspects of engineering modern vehicles. These systems merge robotics, artificial intelligence (AI), embedded software, control theory, and real-time computing into a cohesive unit that enables true autonomy.

What Is an Autonomous Vehicle Control System?

An Autonomous Vehicle Control System is a multi-layered system composed of software and hardware components that allow a vehicle to operate without direct human intervention. It processes data from various sensors such as LiDAR, radar, cameras, GPS, and ultrasonic devices to assess the vehicle’s surroundings. It then makes driving decisions—such as acceleration, braking, lane keeping, and path planning—through real-time control algorithms.

The AVCS includes:

Perception Module: Processes raw sensor data to understand the vehicle’s environment.

Localization Module: Determines the precise location of the vehicle using GPS and sensor fusion.

Planning Module: Creates a safe and optimal path to the destination.

Decision-Making Module: Makes high-level decisions such as lane changes, obstacle avoidance, and traffic rule compliance.

Control Module: Executes vehicle motion via throttle, brake, and steering commands.

These modules work in unison, often using machine learning and predictive modeling to constantly adapt to changing road conditions, traffic, and unexpected events.

Key Technologies Behind AV Control Systems

Sensor Fusion Sensor fusion algorithms combine inputs from multiple sensors to generate a reliable understanding of the surrounding environment. This redundancy increases safety and decision accuracy, even in harsh or unpredictable driving conditions.

Advanced Driver Assistance Systems (ADAS) ADAS features such as adaptive cruise control, lane-keeping assist, and emergency braking serve as building blocks for full autonomy. They are often integrated into control systems for Level 2 or 3 autonomous driving capabilities.

Real-Time Embedded Systems The control system must process large volumes of data and respond within milliseconds. Embedded software, running on real-time operating systems (RTOS), ensures deterministic behavior and low-latency performance.

Artificial Intelligence and Machine Learning AI models help the vehicle recognize objects, predict the behavior of other road users, and learn from previous driving scenarios. Deep learning, in particular, is used in visual perception and decision-making systems.

Model-Based Design (MBD) Using platforms like MATLAB/Simulink, engineers develop, simulate, and validate control algorithms before implementing them in actual vehicles. MBD shortens development cycles and reduces errors in complex control logic.

Vehicle-to-Everything (V2X) Communication AVs can interact with infrastructure (V2I), other vehicles (V2V), and pedestrians (V2P) to increase situational awareness and optimize traffic flow.

Levels of Autonomy and Control

The Society of Automotive Engineers (SAE) defines six levels of vehicle automation:

Level 0: No automation—full driver control.

Level 1-2: Partial automation—driver assistance and adaptive systems.

Level 3: Conditional automation—the vehicle can handle certain tasks but needs human intervention.

Level 4: High automation—fully autonomous in specific scenarios.

Level 5: Full automation—no human intervention required, in all conditions.

Control systems become increasingly sophisticated as vehicles move up the levels of autonomy, requiring more robust perception, planning, and actuation systems.

Challenges in Developing AV Control Systems

Despite the significant progress in autonomous technology, several challenges persist:

Safety and Reliability: The control system must function flawlessly across all scenarios—including extreme weather, low visibility, and unexpected road conditions.

Cybersecurity: As AVs become more connected, securing the control system from hacking or malicious interference is critical.

Regulatory Compliance: Different regions have varying safety and traffic laws, which AVCS must adapt to dynamically.

Validation and Testing: Real-world testing and simulation of autonomous control systems are resource-intensive and time-consuming. Hardware-in-the-loop (HIL) and software-in-the-loop (SIL) testing environments are often used to validate performance safely and efficiently.

Applications Across Industries

While autonomous cars are a prominent example, AV control systems are also being deployed in:

Logistics and Freight: Self-driving trucks optimize long-haul transportation with fewer accidents and better fuel efficiency.

Agriculture: Autonomous tractors and harvesters follow programmed routes and perform precise operations using GPS and onboard sensors.

Construction and Mining: Heavy equipment operates autonomously in hazardous environments, improving safety and productivity.

Public Transportation: Robo-taxis and autonomous shuttles are being introduced to reduce urban congestion and offer accessible mobility options.

Future of Autonomous Control Systems

The future of autonomous vehicle control systems lies in greater intelligence, scalability, and human-machine collaboration. Emerging trends include:

Edge AI: Processing data at the vehicle level for faster decisions.

5G Connectivity: Ultra-low latency communication enabling real-time coordination between AVs and infrastructure.

Cloud-Based Fleet Management: Managing and updating control systems remotely for entire fleets.

Ethical AI: Designing control algorithms that make responsible decisions in complex, moral scenarios.

Conclusion

The Autonomous Vehicle Control System by Servotechinc is the driving force behind the mobility revolution. Its development requires a deep understanding of control theory, embedded systems, artificial intelligence, and real-time communication. As the industry continues to evolve, AV control systems will become safer, smarter, and more adaptable—paving the way for a world where transportation is more efficient, sustainable, and accessible for all.

By investing in robust control system engineering and partnering with technology experts, businesses can lead the next wave of autonomous innovation and shape the future of mobility.

[nmietbbsr]

Latest Trends in Automobile Engineering: EVs, AI & Autonomous Cars

The automobile industry is undergoing a massive transformation. With advancements in technology, the way vehicles are designed, manufactured, and operated is changing faster than ever before. From electric vehicles (EVs) to artificial intelligence (AI) and autonomous cars, innovation is driving the future of transportation. But what do these changes mean for aspiring engineers? Let's take a closer look at the latest trends shaping the industry.

Electric Vehicles (EVs) Are Taking Over

One of the most significant shifts in automobile engineering is the rise of electric vehicles. With concerns over pollution and the rising cost of fuel, EVs have become a viable alternative to traditional internal combustion engines. Companies like Tesla, Tata Motors, and Hyundai are investing heavily in EV technology, improving battery efficiency, and extending driving range.

For engineers, this means new opportunities in battery technology, power electronics, and sustainable design. Learning about lithium-ion batteries, charging infrastructure, and energy management systems can give students an edge in the field.

AI Integration in Automobiles

Artificial intelligence is playing a crucial role in making vehicles smarter. From voice assistants to predictive maintenance, AI is improving user experience and vehicle performance. Features like adaptive cruise control, lane departure warnings, and AI-powered diagnostics are becoming common in modern cars.

Engineers working in this domain need to understand machine learning, neural networks, and sensor integration. Skills in data analysis and software development are now essential for those aiming to contribute to AI-driven automobile innovations.

The Race for Autonomous Cars

Self-driving cars are no longer a concept from science fiction. Companies like Waymo, Tesla, and Mercedes-Benz are testing autonomous vehicles that can operate without human intervention. While fully self-driving cars are still in the testing phase, semi-autonomous features like self-parking and automated lane changing are already available.

To work in this sector, engineers must develop expertise in robotics, computer vision, and LiDAR technology. Understanding how different sensors interact to create a safe driving experience is key to developing autonomous systems.

What Are the Top 5 Engineering Colleges in Orissa?

With so many changes happening, students looking to enter the automobile industry should focus on gaining practical skills. Learning software like MATLAB, SolidWorks, and Ansys can be beneficial. Hands-on experience with automotive projects, internships, and research work can also help build a strong resume.

Those studying at the best engineering colleges in Odisha have the advantage of accessing quality labs, experienced faculty, and industry connections. Institutes like NMIET provide students with the resources needed to stay updated with industry trends and develop practical expertise.

Where to Study Automobile Engineering

With the growing demand for skilled professionals in this field, many students are looking for the best engineering colleges in Odisha to build their careers. A good college should offer state-of-the-art labs, strong placement support, and industry collaborations. Some institutions even have partnerships with automotive companies, providing students with direct exposure to the latest technologies.

The future of automobile engineering is exciting, and those who keep up with these trends will have plenty of opportunities ahead. Whether it's working on EVs, AI-powered vehicles, or autonomous technology, staying ahead of the curve is crucial. If you're passionate about cars and technology, now is the perfect time to explore these innovations and prepare for an exciting career ahead.

[tech4bizsolutions]

AI-Powered Pantograph Monitoring — From Concept to MATLAB & PLC Validation!

Revolutionizing Railway Safety & Efficiency with AI and Automation

The future of railway maintenance and safety is here! At Tech4Biz Solutions, our Research & Development (R&D) team has developed a cutting-edge real-time monitoring solution to enhance railway operations. Our AI-powered system ensures seamless pantograph alignment, stagger analysis, and overhead wire height deviation detection using advanced MATLAB simulations and PLC validation.

Why Pantograph Monitoring Matters

The pantograph plays a critical role in railway electrification, ensuring a stable connection between the train and overhead wires. However, any misalignment, excessive stagger, or wire height deviation can cause power loss, arcing, or even system failures. Our AI-powered monitoring solution prevents such risks and enhances railway safety by implementing:

✅ Real-time fault detection & predictive maintenance — Detects anomalies before they escalate into failures. ✅ Power loss prevention & arcing reduction — Ensures uninterrupted electricity flow for high-speed trains. ✅ Safer & more reliable railway operations — Enhances overall infrastructure longevity and efficiency.

Our Innovative Approach

🔹 MATLAB-Based AI Simulations

We leverage MATLAB’s computational power to simulate dynamic train movements, analyze real-time deviations, and integrate UAV-assisted LiDAR scanning. This allows for accurate pantograph alignment monitoring and predictive failure analysis, ensuring optimal railway system performance.

🔹 PLC Logic Implementation

By integrating Programmable Logic Controllers (PLCs), we have developed a robust automation system that provides:

Live monitoring of pantograph alignment & wire height deviations

Automated alert systems for early fault detection

Corrective action mechanisms to prevent costly breakdowns

🔹 System Validation & Predictive Maintenance

Combining AI-driven simulations with PLC-based real-time analytics, our solution ensures:

Timely intervention before component failures occur

Automated maintenance schedules based on system health data

Significant cost savings by reducing unexpected downtimes

Enhancing Railway Safety with AI & Automation

With railway networks expanding rapidly worldwide, safety and efficiency have become paramount. Our AI-powered pantograph monitoring solution integrates:

Advanced LiDAR edge detection for real-time scanning

Encrypted blockchain communication for tamper-proof data transmission

AI-driven decision-making for predictive maintenance

This combination of AI, MATLAB simulations, and PLC automation not only improves railway safety and reliability but also reduces maintenance costs and enhances operational efficiency.

Future Prospects & Expansion

We’re just scratching the surface of AI-driven railway monitoring. Our roadmap includes:

Further automation of railway maintenance using UAV-assisted inspections

Integration of IoT sensors for enhanced real-time analytics

Expansion into global railway networks for widespread adoption

Watch Our MATLAB Simulation in Action! 🎥

Stay tuned for a detailed demonstration of our MATLAB-based AI simulation showcasing real-time pantograph analysis and PLC-driven corrective actions.

What are your thoughts on AI in railway automation? 🚄 Share your insights and let’s discuss the future of railway technology!

[fortunerobotic]

Learning Robotic Engineering in USA

From science fiction, robots have emerged as a key component of contemporary innovation. Robots are influencing a wide range of industries, including manufacturing, healthcare, entertainment, and space exploration. Aspiring engineers have a fantastic opportunity to acquire state-of-the-art information and skills in a profession that is propelling worldwide advancement by studying robotic engineering in the USA.

Why Choose the USA for Robotic Engineering?

Top-Ranking Programs: When it comes to robotics and artificial intelligence, universities like Carnegie Mellon, Stanford, and MIT routinely rank among the finest in the world.

Access to Cutting-Edge Facilities: Students can get firsthand exposure with the newest robotic technology in cutting-edge labs and research facilities.

Industry Relationships: Unmatched internship and job possibilities are guaranteed by the area's close proximity to tech hotspots like Silicon Valley and its alliances with leading businesses like Boston Dynamics, Tesla, and NASA.

Diverse Career Paths: The United States of America provides chances in a number of fields, including farm automation, healthcare robotics, and autonomous cars.

What Will You Learn?

Designing mechanical systems involves knowing how robots move and engage with their surroundings.

Programming and Software Development: Robot operation using coding languages such as Python, C++, and MATLAB.

Teaching machines to learn, adapt, and make judgments is the goal of artificial intelligence and machine learning.

Control Systems: Using sophisticated algorithms to guarantee accurate robotic motions.

Perception and Sensors: Using LiDAR, cameras, and other sensors, robots can perceive and react to their environment.

Tips for Success

Establish a Solid Base: During your pre-university coursework, concentrate on STEM courses like computer science, physics, and math.

Take Part in Projects: To obtain real-world experience, construct your own robot or compete in robotics contests.

Network: To stay informed, go to conferences, join robotics groups, and make connections with people in the field.

Seek internships: To improve your talents, look for chances to work with prestigious organizations or research institutes.

Keep Up: The world of robotics is always changing, so stay up to date on the newest developments.

With its combination of academic brilliance, real-world experience, and industry ties, the USA provides an unparalleled environment for learning robotic engineering. A career in robotics engineering offers creativity, excitement, and enormous growth potential as robots continue to transform the world.

Robotic engineering in the United States is your ticket to success if you have a strong desire to influence the direction of technology. Begin your path now and rise to the top of this revolutionary industry!

To know more, click here.

[stemroboedtechcompany]

Robotics and AI Curriculum

The rapid advancements in robotics and artificial intelligence (AI) are reshaping industries, economies, and our daily lives. From autonomous vehicles to smart assistants, these technologies are becoming integral parts of modern society. To keep up with this momentum, educational institutions worldwide are developing specialized curriculums in robotics and AI. These programs aim to equip students with the technical knowledge, critical thinking, and problem-solving skills required to drive innovation in these fields.

In this article, we will explore the structure, objectives, and importance of a comprehensive Robotics and AI curriculum. We will also examine how this curriculum aligns with industry needs, prepares students for future challenges, and addresses ethical considerations in AI and robotics development.

1. The Importance of Robotics and AI in Education

As automation and machine learning systems become more prevalent, there is a growing demand for professionals who understand how these technologies work. Robotics and AI are no longer limited to tech companies; they are being integrated into healthcare, manufacturing, logistics, agriculture, and even the arts. The need for a workforce proficient in these areas is critical for continued innovation and economic competitiveness.

Educational institutions are tasked with fostering this expertise by designing curriculums that cover both the theoretical foundations and practical applications of robotics and AI. An effective curriculum goes beyond coding and machine mechanics; it instills a deep understanding of how AI models function, how robots perceive their environment, and how the two can work together to create sophisticated, autonomous systems.

2. Core Components of a Robotics and AI Curriculum

A robust Robotics and AI curriculum is built on several core components, each designed to provide students with a comprehensive understanding of the field. These components include:

Introduction to Robotics and AI: This serves as the foundational course where students learn the basic concepts, history, and future trends of robotics and AI. Topics such as robot anatomy, sensors, machine learning algorithms, and the AI development cycle are introduced at this stage.

Mathematics for Robotics and AI: Mathematics is the language of robotics and AI. Courses in linear algebra, calculus, probability, and statistics are crucial for understanding how AI algorithms function and how robots interpret data from their sensors.

Programming and Software Development: Proficiency in programming languages such as Python, C++, and MATLAB is essential. This component includes courses on object-oriented programming, software architecture for AI systems, and real-time control for robotics.

Machine Learning and Deep Learning: These courses delve into the core of AI development. Students learn about supervised and unsupervised learning techniques, neural networks, reinforcement learning, and natural language processing. Deep learning frameworks like TensorFlow and PyTorch are commonly taught in this part of the curriculum.

Robot Kinematics and Dynamics: Robotics courses cover topics like motion planning, control theory, and the physics of robot movement. Students gain hands-on experience in building and programming robots that can interact with their environment, whether through autonomous navigation or manipulation tasks.

Sensors and Perception Systems: Robots rely on sensors to interact with the physical world. This component covers the various types of sensors (e.g., cameras, LIDAR, ultrasonic sensors) and how they are used in computer vision, object detection, and environmental mapping.

Control Systems: Control theory is critical in robotics for ensuring that machines behave in predictable and safe ways. This includes topics like PID controllers, state estimation, and feedback loops that allow robots to perform tasks accurately.

AI Ethics and Social Implications: As AI systems become more autonomous, the ethical implications of their use become more pronounced. Courses on AI ethics discuss topics like bias in machine learning models, data privacy, the impact of automation on jobs, and the moral considerations of developing autonomous weapons or surveillance systems.

Capstone Projects and Research: A capstone project allows students to apply what they've learned to real-world problems. These projects often involve designing a robot or AI system to solve a specific challenge, such as building a robot that can navigate through a maze or developing an AI system that can recognize emotions in speech.

3. Hands-on Learning and Lab Work

One of the distinguishing features of a robotics and AI curriculum is the emphasis on hands-on learning. In addition to theoretical knowledge, students spend a significant amount of time in labs working on projects. These labs are typically equipped with robotic kits, 3D printers, machine learning servers, and high-performance computers that allow students to experiment with real-world AI applications.

For example, students might work on building robots capable of performing complex tasks like object manipulation, obstacle avoidance, or human interaction. In the AI labs, they might create algorithms that enable autonomous decision-making, image recognition, or predictive analytics.

This practical exposure is vital for preparing students to enter the workforce, where they will be expected to build, maintain, and improve upon AI systems and robotic devices in various industries.

4. Alignment with Industry Needs

A well-rounded Robotics and AI curriculum is closely aligned with the needs of industry. Tech companies, manufacturing firms, healthcare providers, and even defense organizations are all investing heavily in AI and robotics. As a result, the skills taught in these programs must meet the demands of these sectors.

For example, the growing interest in autonomous vehicles has led to an increased focus on sensor fusion, machine vision, and decision-making algorithms in many robotics programs. Similarly, healthcare providers are looking for AI systems that can assist in diagnostics, so there is a strong emphasis on machine learning and natural language processing in the medical AI curriculum.

By collaborating with industry partners, educational institutions can ensure that their curriculum remains relevant and that students are exposed to the latest technologies and tools used by professionals in the field.

5. Career Opportunities in Robotics and AI

Graduates of a Robotics and AI curriculum are highly sought after in various sectors. The skills they acquire can be applied to roles such as:

Robotics Engineer: Design, develop, and test robots for manufacturing, healthcare, and consumer applications.

AI Specialist: Build and implement AI systems for data analysis, machine learning, and predictive modeling.

Machine Learning Engineer: Focus on developing algorithms that allow machines to learn from data and improve their performance over time.

Autonomous Systems Developer: Work on autonomous vehicles, drones, or robots that can operate without human intervention.

AI Research Scientist: Engage in cutting-edge research to develop new AI models and applications.

6. Ethical Considerations in Robotics and AI

As the capabilities of robots and AI systems continue to expand, so do the ethical challenges. A well-rounded Robotics and AI curriculum must address these concerns. For instance, AI systems are often prone to biases because they are trained on historical data that may contain social, racial, or gender biases. This can result in unfair or discriminatory outcomes in areas like hiring, lending, and law enforcement.

Moreover, the rise of autonomous robots, particularly in military and surveillance applications, raises questions about accountability. Who is responsible when a robot makes a mistake or when an AI system is used in a harmful way? These ethical dilemmas require careful consideration and must be integrated into the curriculum to ensure that students are not only technically proficient but also ethically aware.

A comprehensive Robotics and AI curriculum is essential for preparing the next generation of innovators and leaders in technology. By providing students with a strong foundation in both the theoretical and practical aspects of robotics and AI, these programs help bridge the gap between academic knowledge and industry needs. As robots and AI systems become more integrated into society, the importance of a well-educated workforce that understands how to develop and apply these technologies cannot be overstated.

Educational institutions must continue to adapt their curriculums to keep pace with technological advances, ensuring that their graduates are not only skilled engineers and scientists but also responsible innovators who understand the broader societal impact of their work.

More Details visit : https://www.stemrobo.com/solution/

[digitalmbn]

The Greatest MATLAB Introduction to Automated Driving Toolbox

With the introduction of autonomous vehicles, the automobile industry is undergoing a dramatic transition in today's quickly evolving technology landscape. These cars have the power to completely transform transportation, making them safer, more effective, and more convenient because of their cutting-edge sensors, computers, and algorithms. The creation, testing, and implementation of autonomous driving systems are made easier by the Automated Driving Toolbox provided by MATLAB, a robust computational software platform that is utilised in many different sectors.

 Understanding Automated Driving Toolbox

MATLAB's Automated Driving Toolbox provides a comprehensive set of tools for designing and simulating autonomous driving algorithms. Whether you're a researcher, engineer, or student, this toolbox offers a streamlined workflow for developing and testing perception, planning, and control algorithms in a simulated environment.

Perception

Perception is crucial for an autonomous vehicle to understand its surroundings accurately. The toolbox offers algorithms for sensor fusion, object detection, and tracking, allowing the vehicle to detect and recognize pedestrians, vehicles, signs, and other relevant objects in its environment.

Planning and Control

Planning and control algorithms enable the vehicle to make intelligent decisions and navigate safely through various scenarios. The toolbox provides tools for path planning, trajectory generation, and vehicle control, ensuring smooth and efficient motion planning while adhering to traffic rules and safety constraints.

Simulation and Validation

Simulation is a key component in developing and testing autonomous driving systems. MATLAB's Automated Driving Toolbox includes a high-fidelity simulation environment that enables users to create realistic scenarios, simulate sensor data, and evaluate the performance of their algorithms under various conditions.

Key Features and Capabilities

1. Sensor Simulation

The toolbox allows users to simulate various sensors such as cameras, lidar, and radar, enabling realistic sensor data generation for algorithm development and testing.

2. Scenario Generation 

Users can create complex driving scenarios including urban, highway, and off-road environments, allowing for thorough testing of autonomous driving algorithms in diverse conditions.

3. Deep Learning Integration

MATLAB's deep learning capabilities seamlessly integrate with the Automated Driving Toolbox, enabling the development of advanced perception algorithms using convolutional neural networks (CNNs) and other deep learning techniques.

4. Hardware-in-the-Loop (HIL) Simulation

The toolbox supports HIL simulation, allowing users to test their algorithms in real-time with hardware components such as vehicle dynamics models and electronic control units (ECUs).

5. Data Labeling and Annotation

 Efficient tools for data labelling and annotation are provided, facilitating the creation of labelled datasets for training perception algorithms.

 Getting Started with Automated Driving Toolbox

Getting started with MATLAB's Automated Driving Toolbox is straightforward, thanks to its user-friendly interface and extensive documentation. Whether you're a beginner or an experienced developer, MATLAB offers resources such as tutorials, examples, and online forums to support your learning journey.

1. Installation

Ensure you have MATLAB installed on your system, along with the Automated Driving Toolbox.

2. Explore Examples 

MATLAB provides numerous examples covering various autonomous driving tasks, from simple lane following to complex intersection navigation. Explore these examples to gain insights into the capabilities of the toolbox.

3. Experiment and Iterate

 Start experimenting with the toolbox by designing and testing your autonomous driving algorithms. Iterate your designs based on the results obtained from simulation and validation.

4. Engage with the Community

 Join online forums and communities dedicated to MATLAB and autonomous driving to connect with experts and enthusiasts, share ideas, and seek assistance when needed.

 Conclusion

MATLAB's Automated Driving Toolbox empowers developers to accelerate the development and deployment of autonomous driving systems through its comprehensive set of tools and intuitive workflow. By leveraging this toolbox, researchers, engineers, and students can contribute to the advancement of autonomous vehicle technology, paving the way for a safer, more efficient, and more sustainable future of transportation. Whether you're exploring the possibilities of autonomous driving or working on cutting-edge research projects, MATLAB provides the tools you need to navigate the road ahead.

[techrobot1235]

Basics of AR: SLAM – Simultaneous Localization and Mapping

What does SLAM mean?

Through the collection of diverse data and their subsequent transformation into multiple formats so that they may be readily interpreted, simultaneous localization and mapping (SLAM) technology aids in understanding and creating maps. SLAM has been able to understand the egg and chicken paradox by gathering data and obtaining the Ariel signals from the environment through the map, utilizing designated places. The environment would be tracked immediately, and the map would be shown as 3D objects and scenarios using SLAM.

Uses for SLAM include parking a self-driving car in an open place or using a drone to deliver a package in an unknown area. A fleet of mobile robots might also be used to organize the shelves in a warehouse using guidance systems. SLAM algorithms, functions, and analysis tools are available in the MATLAB program for the development of a variety of applications.  

Functions of SLAM

The first type is sensor signal processing, which also involves front-end processing and significantly relies on the sensors being used. The second kind is pose-graph optimization, which also includes sensor-independent back-end processing. There are two types of SLAMs:

Visual SLAM: Cameras and other image sensors are used for visual SLAM, also known as vSLAM. For visual SLAM (depth and ToF cameras), simple cameras (wide angle, fish-eye, and spherical cameras), complex eyes (stereo and multi cameras), and RGB-D cameras can all be used. Utilizing cheap cameras allows for visual SLAM at a minimal cost. Also, because cameras provide a lot of information, they may be utilized to recognize landmarks (previously measured positions). Second, landmark detection and graph-based optimization can increase SLAM implementation flexibility. 

Slam lidar: The technique known as light detection and ranging (lidar) typically utilizes a laser sensor (or distance sensor). Lasers are employed in applications involving high-speed moving vehicles like self-driving cars and drones because they are substantially more accurate than cameras, ToF, and other sensors. When you create SLAM maps, the laser sensor point cloud offers highly accurate distance measurements. In general, movement is calculated by matching the point clouds in a proper sequence.

Basics of AR: SLAM

AR using markers

The device’s camera must be pointed clearly at visuals to use AR technology.  The gadget could understand the superimposed material thanks to specified visuals. The one limitation of marker-based technology was that it required a physical object (in this example, the image) for use. As a result, businesses had to advertise both the tangible product and the software.

Technology for databases

According to developers, the smooth operation of SLAM AR technology requires a thorough database. Tech behemoths understand the value of having a strong database, but it is up to them how they use this database in this industry. Since SLAM and AR will likely become billion-dollar industries over the next 10 years, all IT behemoths are vying to develop a proper visual understanding of the real world. Nobody wants to fall behind the competition.

Sensors for Observing the Environment

Data from multiple sources, such as the camera, is processed to create a map of the surrounding area and find the gadget in the area. The device uses information from essential sensors, such as the gyroscope and accelerometer, to reduce errors. But  GPS falls short of expectations indoors and lacks the simplicity of well-known beacons.

The automobile business and the guiding sector both benefit from SLAM. It is a guiding system in automobiles, autonomous vehicles, laptops, headsets, etc. For companies and clients in sectors like navigation, gaming, advertising, etc., it may also be crucial. Hence, SLAM has a wide range of uses and will continue to remain on the market.

[greysmaster]

Webots tutorial python

Webots tutorial python how to#

Webots tutorial python install#

Webots tutorial python code#

Refer to the language chapter to setup a controller using a different programming language.

Webots tutorial python code#

In this tutorial, we are going to use C as a reference language but all the code snippets are also available in C++, Java, Python and MATLAB. We use e-puck robot, and simulated it on Webots Robot Simulator. Python and MATLAB controllers are interpreted languages so they will run without being compiled. Here we want to implement a simple avoidance algorithm to robot. It is the most efficient solution to quickly get professional results. Webots has been codeveloped by the Swiss Federal Institute of Technology in Lausanne, thoroughly tested, well documented and continuously maintained since 1996.

Webots tutorial python how to#

Recompile the Python wrapper using its Makefile (see details inside to understand how to call it with different.

Webots tutorial python install#

Thousands of institutions worldwide use it for R&D and teaching. The general idea is to walk through the following steps: Install a new Python version and add the path to the new python binary in your PATH environment variable, so that you. dynamic Scenic scenarios following the instructions in the dynamics tutorial. Webots is an open source robot simulator that provides a complete development environment to model, program and simulate robots. Install Scenic in your Python virtual environment as instructed in Getting. On how to create a ROS 2 Python package please visit the official tutorial. The following example assumes you have a ROS 2 Python package created. See the Creating a Custom C++ Plugin tutorial for C++ plugins. Note that the coordinate system representations of. Since webotsros2 1.1.1 one can create Python plugins with webotsros2driver package. The next figure shows the robot from a top view. The robot will consist of a body, four wheels, and two distance sensors. The webots controller code for lidar is written in Python TIMINGS 0. Tutorial 6: 4-Wheeled Robot (60 Minutes) This tutorial aims at creating your first robot from scratch. “Do you want to learn robot, but you don’t have money to buy the robot?” In this Webots tutorial, we will learn how to use lidars in Webots. PSO is used in real life for a scenario like Search and Rescue Operations. Later we will discuss about how to implement Particle Swarm Optimization (PSO) in Webots. Move Your Robot to Specific Coordinates.Create and Play Your First Robot Program Easily using Webots Robot Simulator.This story is the second part of Webots tutorial series. When a simulation starts, Webots launches the specified controllers, and it associates the controller processes. This story is the second part of Webots tutorial series. Simple Avoidance Algorithm, Implemented on e-puck Robot and Simulated on Webots Robot Simulator. by Webots: C, C++, Java, Python or Matlab.

[chauhellingojz721]

Latest MATLAB and Simulink Release Adds New Tools for Wireless Communication

Latest MATLAB and Simulink Release Adds New Tools for Wireless Communication MathWorks has introduced  Release 2021b (R2021b) of the MATLAB and Simulink product families. Release 2021b offers hundreds of new and updated features and functions in MATLAB® and Simulink®, along with two new products and five major updates. New capabilities in MATLAB include code refactoring and block editing, as well as the ability to run Python commands and scripts from MATLAB. Simulink updates enable users to run multiple simulations for different scenarios from the Simulink Editor and to create custom tabs in the Simulink Toolstrip.

R2021b also introduces new products supporting wireless communications:

RF PCB Toolbox RF PCB Toolbox enables the design, analysis, and visualization of high-speed and RF multi-layer printed circuit boards (PCBs). RF engineers can design components with parameterized or arbitrary geometry, including distributed passive structures such as traces, bends, and vias. Using the frequency-domain method of moments and other EM techniques, coupling, dispersion, and parasitic effects can be modeled. Toolbox support for ODB++ and databases from Cadence® Allegro®, Mentor Expedition, Altium®, and Zuken enables signal integrity engineers to analyze the high-speed portions of the PCB layout.

Signal Integrity Toolbox Signal Integrity Toolbox provides functions and apps for designing high-speed serial and parallel links. Users can generate experiments covering multiple parameters, extract design metrics, and visualize waveforms and results. The toolbox supports standard-compliant IBIS-AMI models for statistical and time-domain simulation to analyze equalization and clock recovery.

In addition to the new products, R2021b includes major updates to Symbolic Math Toolbox, Lidar Toolbox, and Simulink Control Design, and other products in the areas of Deep Learning, Reinforcement Learning, Predictive Maintenance, and Statistics and Machine Learning. R2021b is available immediately worldwide.

[emploimaroc30]

Recrutement de Profils en Electronique, Automatisme et Informatique industrielle chez Madrex Engineering

New Post has been published on https://emploimaroc.net/recrutement-de-profils-en-electronique-automatisme-et-informatique-industrielle-chez-madrex-engineering/

Recrutement de Profils en Electronique, Automatisme et Informatique industrielle chez Madrex Engineering

Missions : – Réalisation de validations fonctions ADAS (Radar, Caméra, UltraSon, Lidar) – Réalisation de campagne d’essais sur route, sur banc – Coordination de l’inter systèmes – Rédaction des rapports de tests – Analyse des anomalies détectées – Reporting au Responsable de Validation – Réalignement de véhicule

Compétences techniques : – Métier de la validation EE / architecture EE électrique électronique – Suite CAN & DIAG (CANalizer, DDT2000) – Codage sur MATLAB – Simulink – Réseaux et protocole communication CAN – LIN – BUS – CAPL

Profil recherché : – Bac +5 en électronique/ Système embarqué. – Avoir 1 an d’expérience min dans l’intégration ou la validation de systèmes électroniques dans le secteur automobile. – Bonnes capacités de communication en français et en anglais.

Merci d’envoyer vos candidatures à l’adresse email suivante en mentionnant dans l’objet le poste souhaité:

rh.recrutement@madrex.ma

Voir la source

Ingénieurs Validation ADAS

Missions : – Analyser le plan de validation suivant les spécifications techniques – Architecture et modélisation des fonctions étudiées. – Réaliser des validations organiques et système suivant le plan de validation rédigé (MoyenHIL, table d’intégration ou véhicule avec Matlab – Simulink – Canalyzer). – Rédiger des fiches d’incidents techniques et d’autres livrables. – Analyser / Résoudre des incidents techniques. – Améliorer et réécrire le plan de validation si besoin. – Simulation et validation des modèles – Identifier les incidents techniques sur le périmètre. – Rédiger la documentation technique liée aux modèles

Compétences techniques : – Maitrise des architectures EE Auto, sur des système innovant tel que Stop 1 Start, ADAS..

Profil: – De formation Bac+5 en électronique embarquée / informatique industrielle et/ou automatique & lois de commande – Une expérience dans le développement de systèmes embarqués. – Des connaissances en langage C et développement de systèmes dans l’environnement Matlab Simulink. – Un bon niveau d’anglais.

Merci d’envoyer vos candidatures à l’adresse email suivante en mentionnant dans l’objet le poste souhaité:

rh.recrutement@madrex.ma

Voir la source

Dreamjob.ma

[allahabadphoto]

Automotive engineers use MATLAB® and Simulink® to design automated driving system functionality including sensing, path planning, and sensor fusion and controls. With MATLAB and Simulink, you can: Develop perception systems using prebuilt algorithms, sensor models, and apps for computer vision, lidar and radar processing, and sensor fusion. Design control systems and model vehicle dynamics in a 3D environment using fully assembled reference applications. Test and verify systems by authoring driving scenarios using synthetic sensor models. Use automated driving-specific visualizations. Plan driving paths by designing and using vehicle costmaps, and motion-planning algorithms. Reduce the engineering effort needed to comply with ISO 26262. Automatically generate C code for rapid prototyping and HIL testing using code generation products.http://automotivedesigndevelopment.blogspot.com/ #mysore #karnataka #kanpur #madikeri #bangalore #kannada #mandya #mysuru #sandalwood #coorg #mangalore #hassan #allahabad #prayagraj #allahabadi #allahabaddiaries #prayagrajcity #allahabaduniversity #uttarpradesh #sangam #prayagrajkumbh #up #kumbh #instagram #prayag #allahabadphotography #prayagrajallahabad #streetsofprayagraj #instaallahabad #kanpurdairies (at Mysore, Karnataka) https://www.instagram.com/p/CA990Q0huA_/?igshid=z07t1p0655hb

[deservico]

#5 Inteligência artificial & Machine Learning 09/04/2019

Inteligência artificial (+-1950) > Machine Learning (+-1980) > Deep Learning (+-2010) Todas elas usam algoritmos.

Algoritmos são conjuntos de regras que são usadas para resolver problemas.

Em Machine Learning, os algoritmos puxam dados e fazem cálculos para encontrar uma resposta, podendo ser simples ou complexos. Existem vários algoritmos que produzem diferentes resultados, não existe um algoritmo certo para todas as questões e eles podem variar de acordo com cada problema que cada um quer resolver. Então você deve encontrar o algoritmo mais eficiente que entregue uma resposta mais correta na maioria das vezes. Afinal se um algoritmo demorar mais que um humano para fazer as escolhas, de que adianta. Como uma ferramenta, vc pode usar para fazer o que você quiser, mas há uma ferramenta especifica para isso.

Um algoritmo não trabalha por si só, ele precisa ser treinado para que aprenda a classificar e processar informação. A qualidade do algoritmo depende de quão bem ele foi treinado.

Usar um algoritmo para analisar dados e fazer uma previsão não necessáriamente significa que ele faz ML ou AI.

Agora quando ele tem um resultado e utiliza ele para aprimorar sua previsão no futuro, aí  sim estamos falando de AI ou ML.

Inteligência artificial é um conceito mais abrangente que ML, que aborda o uso de computadores para imitar as funções cognitivas dos seres humanos. Quando máquinas executam tarefas baseadas em algoritmos de uma maneira “inteligente”, isto é, Inteligência Artificial.

Machine Learning é um subcategoria da inteligência artificial e concentra-se na capacidade das máquinas de receber um conjunto de dados e aprenderem sozinhos, alterando os algoritmos conforme eles aprendem mais sobre as informações.

Para um computador “pensar como seres humanos, ele se utiliza de Redes neurais. Redes neurais são uma série de algoritmos modelados a partir do cérebro humano. Assim como o cérebro pode reconhecer padrões e nos ajudar a categorizar e classificar informações, as redes neurais fazem o mesmo com os computadores.

Benefícios das redes neurais:

Extrair o significado de dados complexos

Detectar tendências e identificar padrões complexos demais para os humanos perceberem

Aprendizado por exemplos

Velocidade

Deep Learning, pode ser considerado uma subcategoria de Machine Learning.

O conceito de Deep Learning às vezes é chamado de "Redes Neurais Profundas", referindo-se às muitas camadas envolvidas.

Uma rede neural pode ter apenas uma única camada de dados, enquanto uma rede neural profunda tem dois ou mais. As camadas podem ser vistas como uma hierarquia aninhada de conceitos relacionados ou árvores de decisão.

A resposta a uma questão leva a um conjunto de questões mais profundas que aquelas que ele resolveu.

Redes de Deep Learning precisam ver grandes quantidades de itens para serem treinadas. Em vez de serem programados com as bordas que definem itens, os sistemas aprendem com a exposição a milhões de pontos de dados. Um dos primeiros exemplos disso é o Google Brain aprendendo a reconhecer gatos depois de serem exibidos mais de dez milhões de imagens. Redes de aprendizagem profunda não precisam ser programadas com os critérios que definem itens; eles são capazes de identificar bordas ao serem expostos a grandes quantidades de dados.

Dados são o principal. Se você estiver usando um algoritmo, inteligência artificial ou aprendizado de máquina, uma coisa é certa: se os dados em uso forem falhos, os insights e informações extraídos serão defeituosos. O que é limpeza de dados?

“O processo de detecção e correção (ou remoção) de registros corrompidos ou imprecisos de um conjunto de registros, tabelas ou banco de dados e refere-se à identificação de partes incompletas, incorretas ou irrelevantes dos dados e a substituição, modificação ou exclusão de dados sujos ou grosseiros. "

E de acordo com o relatório da CrowdFlower Data Science, os cientistas de dados gastam a maior parte do tempo limpando dados - e, surpreendentemente, essa também é a parte mais chata do trabalho deles.

Apesar disso, também é a parte mais importante, pois não se pode confiar nos resultados se os dados não tiverem sido limpos.

Para que a IA e o aprendizado de máquina continuem avançando, os dados que conduzem os algoritmos e as decisões precisam ser de alta qualidade. Se os dados não forem confiáveis, como as informações dos dados serão confiáveis?

Aplicações

Lidar com aplicações repetitivas. Para que possamos lidar com aspectos mais criativos nas tomadas de decisão. Ajudar a clientes a encontrar produtos mais facilmente. Carros que se dirigem sozinhos Reconhecimento de imagem Previsões financeiras O que você imaginar...

Exemplos de Software e linguagem: Python Tensorflow Matlab Mathematica… Olhem na Wikipedia e no ai.google

Referências e Links: https://www.datasciencecentral.com/profiles/blogs/artificial-intelligence-vs-machine-learning-vs-deep-learning Google Primer https://ai.google

[thpintocardoso]

O que é a linguagem Python e como ela está presente na engenharia?

É bem provável que, se você perguntar para alguém que programa qual linguagem de programação você deve aprender (se não houver um objetivo específico), a resposta será Python. A linguagem Python é uma das queridinhas na programação e é cheia de recursos para você explorar. Por isso, vamos entender um pouco mais sobre ela, saber suas vantagens e usos na Engenharia e conhecer alguns cursos gratuitos.

+ O que é a linguagem Python?

Python é uma linguagem de programação de alto nível (que usa instruções mais abstratas, mais “humanas”) criada por Guido van Rossum e que foi lançada em 1991. Para traduzir, uma linguagem de programação é um conjunto de instruções para uma máquina realizar alguma tarefa. Escrever um algoritmo é como escrever um manual de instruções para o computador.

Imagem: younggates.com

Há quem diga que o nome, Python, é devido à serpente, mas a verdade é que ele se deve ao clássico programa humorístico britânico chamado Monty Python. Desde seu lançamento, Python foi se popularizando no meio da programação.

+ Quais as vantagens de usar Python?

Além de ser uma linguagem simples, dinâmica, robusta, multiplataforma, fácil de aprender e expressiva (é fácil gerar um algoritmo a partir do raciocínio), Python suporta vários paradigmas de programação, como orientada a objetos. Assim, é possível fazer desde programas simples e rápidos (como o bom e velho “Hello World”) a estruturas mais complexas. A biblioteca do Python é muito ampla e diversificada, com funções que permitem realizar muitas tarefas.

Outras duas vantagens da linguagem Python são a sua interface gráfica, que permite visualizar grafos, fazer animações da evolução ao longo do tempo e mais, e a capacidade de lidar com uma grande quantidade de dados científicos. Há muitos bancos de dados que são trabalhos em Python.

Se você está com algum problema que não consegue resolver, não é difícil tirar uma dúvida sobre Python. Há uma grande comunidade espalhada na internet que dá dicas, ensina tutoriais, disponibiliza códigos e sempre tira aquelas dúvidas complicadas.

+ Python na Engenharia

Saber programar é um diferencial para engenheiros de qualquer área. No caso do Python, ele é um recurso que pode ser muito explorado na Engenharia. Isso acontece porque, como nós já sabemos, a Engenharia é muito ampla e a programação está presente em inúmeras áreas e tarefas cotidianas.

Imagem: gaunte.com

No caso do Python na Engenharia, é possível usar para fazer desde simulações simples até busca de banco de dados na internet, aprendizado de máquinas, automação de tarefas, etc. É até difícil especificar os usos, porque eles são vários, mas é possível afirmar que um(a) engenheiro(a) que sabe Python está bem “armado” para guerrear no mercado de trabalho.

Porém, nós sabemos que, para cada tarefa, há uma linguagem de programação que é mais indicada. Então, não vale a pena entrar em discussão sobre qual é a melhor de todas. Cada linguagem é mais adequada para uma situação e melhor em determinado caso. Não há uma número um.

A linguagem Python possui várias aplicações e um uso muito amplo, o que a faz ser uma das queridinhas. Porém, é possível que, em várias aplicações na Engenharia, Python não seja o ideal. Às vezes, Matlab, Java, C, C++, R ou qualquer outra pode ser mais útil. Por isso, é importante que você sempre pesquise antes de escolher uma linguagem para desenvolver.

Imagem: realpython.com

A parte boa é que, uma vez que você aprende a lógica de programação (que é algo um tanto intuitivo para quem faz Engenharia), você consegue aprender uma nova linguagem sem muito esforço. Se você quer aprender a programar sem algum objetivo específico, o Python é sim uma boa opção para começar. Abaixo, nós separamos alguns cursos gratuitos para quem quer se aventurar no universo da linguagem Python.

+ Cursos gratuitos de Python

Introdução à Ciência da Computação com Python – USP

Dividido em duas partes (parte 1 e parte 2), o curso ensina a programar em Python e passa conceitos básicos da Ciência da Computação.

Python Fundamentos para Análise de Dados

Ensina os conceitos básicos de Python e tem foco na análise de dados.

Python para Zumbis

Apesar do nome curioso, o curso é para iniciantes (zumbis) em Python.

Python for Absolute Beginners (em inglês)

Como o nome já diz, é um curso de Python para iniciantes.

Google’s Python Class (em inglês)

É uma aula para quem conhece um pouco sobre programação e quer aprender Python.

Referências: Python.org; Pyscience; Ibpad.

The post O que é a linguagem Python e como ela está presente na engenharia? appeared first on Engenharia 360.

O que é a linguagem Python e como ela está presente na engenharia? Publicado primeiro em https://blogdaengenharia.com

[chapterxxv]

Cover v2

As drafted on the flight from AZ to IL:

I need to imagine who you are. I am great at forming genuine connections with people but find this so challenging because I don’t know who I’m talking to. So I’m going to imagine you are the boss I want. 

Remove this section. Think it to yourself. Define that boss. Then you can consider a single line at the end about what this boss would be like.

One of the most significant lessons I learned from graduate school is that I am a people-oriented person, differentiated from a sea of goal-oriented engineers at the University of Illinois. Like my peers, I enjoy learning and engaging with interesting or hard problems, and I dream of making a bigger impact on the world. Perhaps the goal-oriented people find that these pursuits sufficiently motivate their technical experiences. I, on the other hand, am driven by collaboration with passionate minds, camaraderie with my peers or coworkers, and meaningful work that visibly impacts our community. With that, I bring a suite of both hard- and soft-skills (e.g. communication, leadership, mentorship) 

[Insert some statement of clear objectives here.]

On the technical end: I have extensive coursework within control theory and embedded systems. I served as a co-lead on a distributed robotics research project (CyPhyHouse) for the last two years, where I was responsible for developing an autonomous ground vehicle testbed (using a 1/10th scale formula hobby RC car) that runs ROS and custom Java-based software. I collaborated with two other graduate students and directly mentored five undergraduates. To date, we have three of these platforms assembled; each set up with a stereo camera, LIDAR, IMU and ready to interface with Vicon and an alternate in-house positioning system. We have implemented some “simple” applications demonstrating basic functionality: waypoint-following using external positioning data (i.e. Vicon), wall-following with LIDAR, SLAM with LIDAR, object-following with camera, and remote motion control. We are in the process of fine-tuning motion control with feedback from the IMU, obtaining visual odometry estimation, and tying it all back together under a waypoint-following scenario for a group of heterogeneous robots (cars and quadcopters.)

Within the robotics realm, I have worked with other similar projects but with varied setups (e.g. programming a dsPIC to control two servos to balance/roll a ball on a touchscreen “table”).

Technical promise: I am very interested in gaining deeper understanding and applied skills within robotics. My current personal project involves designing a neural network to control a drone in the Microsoft AirSim environment. If all goes well, the goal is to achieve Intel’s drone light shows in simulation by extending to control of swarm robots. This project excites me because it ultimately puts me back in touch with wanting to do applied work and creating deliverables; it demonstrates my commitment and resourceful approach to learning as I have been intrigued by AI, control of distributed systems, and Python--all areas I have never had formal training or formal opportunities to pursue. 

Learning capabilities. Where I want to be. How we can help each other.

To summarize how I fulfill your ideal criteria: All my significant programming endeavours have been in C, C++, and MATLAB. I have handled but not significantly developed Python and Java projects, aside from my ongoing Python project (i.e. controlling drones in AirSim). I have worked on simulators in MATLAB for a SUV retrofitted for autonomous driving (i.e. CAT Vehicle REU in 2013) and for a benchmark satellite rendezvous mission (i.e. AFRL Space Scholar internship in 2016). The theme of my past work has been in developing safe (think, constrained) controllers--necessary for collision avoidance--and there is some overlap with optimal performance, such as with model predictive control. Oftentimes, controls is associated with low-level tasks (e.g. motor actuation), but the view I employ and enjoy most is modeling high-level system dynamics and applying control theory to achieve motion or task planning. With this experience (and foundational coursework involving mechanical engineering, physics, and math), I can quickly jump into working with optimal control, trajectory planning, gripper dynamics, visual servoing, and writing physics packages. I have also touched on Gazebo at some point and I recall the trickiest aspect was working with multiple frames of reference, which I have recently reviewed with learning quaternions in Field Robotics class.

So much more: communication, leadership, mentorship, problem-solving, organization, vision/drive for the “right” things (--> our visions align). I am driven to grow, challenge myself. get in touch with your vision, create/build/debug.

Organization extends to time and resource management but also clean, commented code. As a leader and person who loves efficiency and working with people, I know how important communication, clarity, and organization are within the technical work. I think I do decently well managing these aspects, but definitely hope to grow and learn from the great experiences of the leadership and technical teams at HDS.

[jobswzayef]

Mechatronics and Robotics Design Engineer

Mechatronics and Robotics Design Engineer Saudi Aramco is seeking an experienced Mechanical Engineer Mechatronics and Robotics Design Engineer to work as an expert in mechatronics and robot design in the Intelligent Systems Team under the Oil Gas Network Integrity Division O GNID within the Research and Development R D Department. O GNID focuses on rapid technology development aimed at providing solutions for integrity related challenges. O GNID aims to decrease the annual cost of corrosion and its related activities including inspection monitoring and maintenance. You will join the Intelligent Systems IS team where you will contribute to their research and development portfolio that includes intelligent systems and robotics solutions for integrity monitoring reliability enhancement and inspection technologies. Intelligent Systems is a multidisciplinary team which will require you to contribute to the overall creation of various robotic solutions based on your specialty and assignment. You will design new electromechanical systems to meet project objectives related to the prototyping testing and deployment of intelligent systems or robotic technologies. You must demonstrate an ability to develop and mentor young engineers in specialized areas of mechatronic systems robotic system design control systems artificial intelligence system architecture and electromechanical design. You must exhibit skills in developing innovative robust and reliable robotic solutions to novel applications including challenging manipulation locomotion perception and inspection tasks. You will also have the ability to lead development tasks projects to address challenges experienced by Saudi Aramco operations. This will include coordinating technical team members performing techno economic analyses and delivering technical presentations.Minimum Requirements As the successful candidate you will hold a bachelor’s degree in mechatronics engineering with a focus related to robotics. A master’s degree or Ph.D. in the mechatronics robotics field is desirable. You must have a substantial hands on experience in robotic platforms or automation in an academic commercial or industrial setting. This includes having a strong electromechanical design background for the purpose of developing intelligent technologies. You must have excellent command of oral and written English to demonstrate active participation in technology development technical seminars and or original industrial technology deployment. You will have experience in the design of robotic systems control systems dynamics instrumentation artificial intelligence and computer vision. You should have experience in using Computer Aided Design CAD Platforms such as SolidWorks AutoCAD ProE or CATIA SolidWorks is ideal . You should have experience in designing electrical systems and boards and in the usage of PCB design software. The ideal candidate will be knowledgeable in Altium. You must have knowledge in working with at least one programming language C C MatLab Python etc. and one analysis and simulation software program MatLab MathCAD etc. . A background in programing control systems dynamics instrumentation mechanics and design is also required along with familiarity with robotic sensors such as video cameras LIDAR IMU force torque ultrasonic and SLAM. Experience with motion control system components such as motors encoders drive train bearings analysis and application is also essential.Duties Responsibilities You will be required to perform the following Design and prepare manufacturing packages and assemble new mechanical systems to meet project objectives related to the prototyping testing and deployment of intelligent systems or robotic technologies. Research and develop innovative robust and reliable robotic solutions to novel applications including challenging manipulation locomotion perception and inspection tasks. Mentor and provide advice in a specialized area of technology such as product design robotics magnetic based movements statics and dynamics various locomotion systems integrity and condition monitoring and mechanical control systems. File patents and present technical findings in published journal articles and at intracompany meetings. Auto req ID 16398BR En savoir plusTout afficher * راتب مجزي جداً. * مكافأت و حوافز متنوعة. * توفير سكن مؤثث أو بدل سكن. * أنتقالات أو توفير بدل عنها. * توفير تذاكر السفر لمن يشغل الوظيفة و عائلته. * نسبة من الأرباح الربع سنوية. * أجازات سنوية مدفوعة الراتب بالكامل. * مسار وظيفي واضح للترقيات. * بيئة عمل محفزة و مناسبة لحالة الموظف. * تأمين طبي للموظيف و عائلته. * تأمينات أجتماعية. التقدم و التواصل مباشرة دون و سطاء عند توافر الألتزام و الجدية التامة و المؤهلات المطلوبة علي: jobswzayef@gmail.com