In order to promote the development of the global embodied AI industry, the Unitree G1 robot operation data set is open sourced, adapted to a variety of open source solutions, and continuously updated:&nbsp;Open source data collection:&nbsp;<a tabindex="0" href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqa3VhODBhSF8tQzQtdFlpaUk2blczM2t1WFdkd3xBQ3Jtc0ttTkF6bzFrSzVTbkk5elFNaXl4ZmZyQTE5VGFfbWpESlhxVS1VRklKVXozX2JrZ2ItekNReGx3Vm5QOHY2OGxER0diUUhhTWlBdmotSU9LUV9EcWV0dC00Y3pjTWpyQXlQUTU5RmZCYjhoRl9KeXhadw&q=https%3A%2F%2Fgithub.com%2Funitreerobotics%2Favp_teleoperate&v=OTWHXTu09wE" rel="nofollow" target="_blank" data-immersive-translate-walked="dc553b62-3d51-4d74-bb21-9b5c19549e6f">https://github.com/unitreerobotics/av...</a>Open source learning algorithms:&nbsp;<a tabindex="0" href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbFVfRV9DS25fNFpSMXVpaEJicUFBV29QY2NYd3xBQ3Jtc0tuejRtek9OWDF5Qk9Fc0FRNUhzWkl0SEdDSFM4UFRHLWk2MHdOUjR3LWxwN0JlUmxGRkJ5TjhlbE83S0hlZEhDY0IxQmFiOGJaM0MxRFVtdmtta3ZMMWVCOXlQMS1WR2hJa0hla0RtVHpvZWlQc3pPZw&q=https%3A%2F%2Fgithub.com%2Funitreerobotics%2Funitree_IL_lerobot&v=OTWHXTu09wE" rel="nofollow" target="_blank" data-immersive-translate-walked="dc553b62-3d51-4d74-bb21-9b5c19549e6f">https://github.com/unitreerobotics/un...</a>Open source datasets and models:&nbsp;<a tabindex="0" href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbGh0clVCMHBNbkpPWTczdTFjSGtWNGFfLVowUXxBQ3Jtc0trV2VfWFBrLUVNNS1oM3JNR2ZEU0d0MnlYZXhBNDVQVjNfYkN1aGd1b3BMc3JHUGtNTnFBN0JSMi1CQUJHSnRUTzZMTWNIZnJNQ29IUmNtT2t1blZkRXJ3ZEtfZmRKYVdqUGk1OWNZSUl4d1g5Qk16MA&q=https%3A%2F%2Fhuggingface.co%2FUnitreeRobotics&v=OTWHXTu09wE" rel="nofollow" target="_blank" data-immersive-translate-walked="dc553b62-3d51-4d74-bb21-9b5c19549e6f">https://huggingface.co/UnitreeRobotics</a>

In order to promote the development of the global embodied AI industry, the Unitree G1 robot operation data set is open sourced, adapted to a variety of open source solutions, and continuously updated.

Unitree G1 Open Source Dataset

<h3 id="isPasted">4 Reasons to Create a Smart Tank Monitoring and Pump Control System</h3>#1. A smart solution allows operators to remotely manage tank levels and pump operations from anywhere, preventing common issues such as tank overflows, pump dry-running, and treatment process disruptions. The ability to monitor systems in real-time reduces the risk of costly emergencies by enabling early detection of potential problems, providing timely data for decision-making.#2.&nbsp;In terms of maintenance and usage, access to real-time data allows operators to optimize system performance. Data on pump operations and operating hours can be leveraged to support predictive maintenance, reducing downtime and extending the lifespan of equipment. This also ensures consistent water quality by making informed decisions based on continuous monitoring.#3.&nbsp;When based on versatile products like Arduino’s, inspired by open-source principles, a smart tank monitoring system is highly adaptable and scalable, allowing it to fit various industrial needs. It can be integrated with existing equipment and third-party systems, ensuring seamless incorporation into a facility’s operations. This flexibility makes it ideal for a range of industrial applications, from small-scale plants to complex systems.#4.&nbsp;By automating the monitoring process, the solution minimizes the need for frequent manual checks, reducing human error and cutting operational costs. Automated alerts and precise monitoring help maintain optimal chemical dosing and flow rates, while reducing any costs associated with equipment damage, process disruptions, and regulatory non-compliance.<h3>Technical Overview of the Solution</h3>A flexible and cost-effective solution for tank monitoring and pump control can be based on an easy-to-use micro PLC such as the <a href="https://www.arduino.cc/pro/hardware-arduino-opta/" target="_blank">Arduino Opta</a>. The <a href="https://docs.arduino.cc/tutorials/opta/tank-level-app-note/" target="_blank">complete tutorial is available on Arduino Docs</a>.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzI2ODQ0ODQzNTk5LWVsZWN0cmljYWxfY29ubmVjdGlvbnMucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">In this case, the system employs a variety of sensors – such as float switches, ultrasonic transducers, and pressure transducers – to continuously track liquid levels. Data from flow meters is used to measure pump discharge rates and overall system flow. This information is then transmitted to a cloud-based SCADA&nbsp;(Supervisory Control and Data Acquisition) system for real-time access, allowing operators to visualize and manage tank levels, pump status, and system flow rates through an intuitive dashboard.Additionally, the system includes configurable alarms that send alerts for critical scenarios such as abnormal flow rates or high/low tank levels. These alarms can be customized and sent via multiple channels, including email and SMS, preventing unnecessary notifications and ensuring only critical alerts are triggered.The solution is compatible with a variety of sensors and supports industry-standard protocols, such as Modbus RTU over RS485 and Modbus TCP/IP.&nbsp;<h3>Adding Vision-Based Liquid Monitoring</h3>In situations where conventional sensors are not practical, a vision-based monitoring system can be deployed. A module such as the Arduino <a href="https://www.arduino.cc/pro/hardware-product-nicla-vision/" target="_blank">Nicla Vision</a>, which is equipped with a 2 MP color camera, has the intelligence to&nbsp;visually assess the current liquid level in a tank: applying so-called “blob detection”, the liquid’s surface can be identified and the tank’s fill level calculated. This data can be relayed to the control system to trigger appropriate actions, such as opening or closing valves.This vision-based approach offers a non-invasive method to monitor tank levels, making it suitable for systems where physical sensor installation is difficult or impossible. It can also be retrofitted to almost any existing system without significant modifications.<h3>Unlocking Smart Industrial Solutions with the Arduino Ecosystem</h3>The flexibility and adaptability of the Arduino platform make it an excellent choice for creating smart industrial systems, such as tank level monitoring and pump control solutions. Whether you're integrating traditional sensors or implementing innovative vision-based methods, Arduino’s open-source tools and broad compatibility with industry standards offer a versatile foundation for any project. Explore the Arduino ecosystem to find the right tools for your next industrial challenge, and see how it can accelerate your path to smarter, more efficient operations.

The need for advanced tank monitoring and pump control solutions is growing across industries – from water treatment to aquaculture to industrial utilities. Meanwhile, smart technology is transforming tank management for a variety of liquids, ensuring a streamlined process and minimizing risks.

Build Your Own Smart Industrial Tank Level Monitoring and Pump Control Solution

AMB Vapor Monitoring demonstrate how innovation and the right technology can drive efficiency, accuracy, and cost savings while ensuring a safer environment for all

A foundational challenge to construction is ensuring that the earth below a building is stable enough to support it: a challenge AMB Vapor Monitoring met by creating a custom Arduino solution – going from idea to installation in a matter of months: “And it works. It just literally works.” 

AMB Vapor Monitoring Leverages Faster, Cloud-connected Reporting

<a href="https://r2-labs.io/" target="_blank">R2 Labs</a>’ goal is to make modern automation equipment systems more accessible by providing today’s software engineers with a tool that makes interaction and control easier, regardless of the specific application.&nbsp;Successful projects – such as the development of an AI-powered agricultural robot for Padma AgRobotics with Arduino products – encouraged the team to pursue a combination of versatility and customization, making the range of applications they can tackle almost limitless.&nbsp; Clear Requirements to Lower BarriersWanting to create a “general-purpose controller”, R2 Labs knew it had to be programmable with modern languages, extensible, and have the computational power to manage motor control, I/O control, machine vision, and more. They decided to keep exploring with Arduino Pro hardware to find an innovative solution that would be open-source, extensible without vendor lock-in, and capable of providing a host of common functionalities customers need in almost any autonomy application.With these requirements in mind and previous positive experience using Arduino microcontrollers, R2 Labs decided to develop their project with the <a href="https://www.arduino.cc/pro/portenta-machine-control" target="_blank">Portenta Machine Control</a>. This allowed them to cut down on time-to-market and quickly integrate robust, industrial-grade features – from the dual-core processor that allows for the segmentation of critical sections, to CAN bus support. Unlocking Opportunities for All, with Open SourceInstead of spending huge internal resources on developing their own control unit or going through complex sales processes, by partnering with Arduino R2 Labs immediately gained access to support, documentation, and high-quality products tested by a community of millions.“We achieved exactly what we were aiming for: a solution well-suited for both modern robotics and industrial automation applications. Thanks to the Portenta Machine Control’s advanced features and Arduino’s prompt support, we can now enable any software engineer to tackle a broad range of applications and solve challenges that are common to most control systems,” says CEO Roby Lynn, who co-founded R2 Labs with Sulisay Phonekeo and Rebecca Peterson.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1711267873603-controller+1.png" style="width: 100%px;" class="fr-fic fr-dib">Meet RAC: A Paradigm Shift in Control SystemsR2 Labs’ vision led to the launch of the RAC, a fully integrated ROS 2-native autonomy control solution that supports multi-axis motion control and is able to easily interface with a wide range of sensors and actuators. The device simplifies development with an array of digital and analog I/O, advanced vision processing, and standard connectivity (HDMI, Ethernet, USB). It allows users to effortlessly program with Python® and C++ and control/manage the project with the included interactive web HMI.&nbsp;All in all, the RAC is a game-changing product for control and monitoring – both in robotics and industrial automation applications. The Arduino Portenta Machine Control is the brain of this brilliant solution to make Industry 4.0 and 5.0 accessible to software engineers, ready to tackle any specific application they may need. Creating New Solutions for Modern Robotics and ControlWith a clear vision of the challenges in their field, R2 Labs is committed to creating modern robotics and control system solutions that champion flexibility and ease of use.Pairing professional expertise with outside-the-box thinking, the company is now ready to tackle bigger deployments, new customers, and different industries. Choosing open source is key to this growth path, allowing them to expand their portfolio by focusing on the work they do without constraints.

R2 Labs: democratizing robotics and industrial automation with Arduino

Manufacturing, agriculture, healthcare, and more: each industry needs specific robots and specific solutions to run and control them. But “Robotics for the Real World” provider R2 Labs, based in the United States, has set out to change that.

R2 Labs Presents RAC: An Industry-Agnostic Controller

In this article, we explore how open source is not just for makers anymore, and actually makes great business sense. Business Benefits of Open Source: The Pragmatic Side of a PhilosophyChoosing open source is not just a philosophical stance; it is a strategic decision with tangible benefits for businesses across a variety of sectors: manufacturing, building automation, smart cities, e-mobility, logistics, safety – and any industry where technology is key.Here are 7 reasons to consider open-source solutions:<ol><li dir="ltr">License Included: Escape the burden of hefty license fees, with an open-source model that completely eliminates the need for these costs.</li><li dir="ltr">Limit NREs: Non-recurring engineering costs decrease when you use an open and accessible platform designed for seamless integration.</li><li dir="ltr">No Vendor Lock-in: The growing Arduino ecosystem is not a trap; it’s a versatile offering that allows exploration and evolution, without imposing switching costs.</li><li dir="ltr">Extensive Documentation: Over the years, Arduino has built a treasure trove of documentation, tutorials, and tech specs to support users at every stage of development.</li><li dir="ltr">Community-Sourced Resources: Our vibrant community of over 33 million users ensures a constant flow of libraries, examples, and shared experiences.</li><li dir="ltr">Effortless Integration: Arduino products seamlessly integrate with third-party solutions, enabling cost-effective retrofitting and interoperability.</li><li dir="ltr">Lower Labor Costs: Arduino’s accessibility and user-friendly design mean lower labor costs, with a large pool of engineers familiar with the platform.</li></ol> Shared Commitment, Shared AchievementsArduino was founded on principles of open source since its inception in 2005, and continues to be at the forefront of the movement. Our recently released <a href="https://blog.arduino.cc/2024/01/11/the-2023-arduino-open-source-report-is-out/" target="_blank">Open Source Report for 2023</a> unveils a range of achievements that exemplify in how many directions this core value translates into new projects, products, resources and more.Key highlights include joining the Zephyr® Project, releasing five new open-source hardware components (Arduino UNO R4 Minima and R4 WiFi, Arduino GIGA R1 WiFi and GIGA Display Shield, Arduino Nano ESP32), and a plethora of updates to the Arduino IDE and command line tools. The pivotal role of the vibrant Arduino community is highlighted in the report too, with over 1,000 new libraries and 205 open-source tutorials shared on Project Hub in 2023.&nbsp;Another initiative fueled by the community’s contributions is <a href="https://docs.arduino.cc/" target="_blank">Arduino Docs</a>, a collaborative repository launched in 2021 as a testament to the brand’s commitment to sharing knowledge. The archive has grown into a comprehensive resource hub, and the Arduino Team’s efforts to keep a sea of information updated, organized, and easy to navigate for all recently led to a complete overhaul of the platform’s design and architecture, unveiled in January 2024.&nbsp; Unlocking Innovation with the Open-Source ParadigmIn a world where innovation is the lifeblood of success, adopting open-source technology platforms like Arduino can be a savvy move for entrepreneurs and engineers worldwide. The tangible business benefits, coupled with an outstanding track record of community collaboration and continuous improvement, position open source as not just a philosophy but a pragmatic pathway to sustained success in the ever-evolving landscape of digital transformation, leading to cost-effectiveness, innovation, and a future-proof tech ecosystem.

Open source makes business sense. Photo: Alberto Morici for Arduino

In the ever-evolving landscape of technology and innovation, businesses are constantly seeking ways to stay ahead of the curve, optimize processes, and drive innovation. One powerful approach gaining widespread recognition is the adoption of open-source technology platforms – and for good reason.

Unleashing Business Potential with the Advantages of an Open-Source Approach

Capping lines in modern factories are machines of incredible speed and precision, capable of installing thousands of caps on bottles and containers every hour. Based in Canelli, Italy, <a href="https://www.arol.com/" target="_blank" rel="noopener noreferrer">AROL</a> is a leading provider of innovative and custom capping and closure system machines, with over 25,000 pieces of equipment currently installed around the world. With production lines processing up to 100,000 bottles per hour, maintaining their machines’ speed and ongoing reliability is critical to operations. In other words, any outage or unplanned downtime would be detrimental to meeting production quotas and costly to their clients.<h3 dir="ltr">Adding Brains to Speed for Improved Productivity</h3>To support diagnostic, prognostic and predictive maintenance, they decided to draw on their experience to develop a smart solution able to gather data from industrial equipment and the surrounding environment, creating a machine monitoring system based on remote wireless sensors that could be powered by battery or 24 V.&nbsp;The project faced specific challenges in the fact sensor nodes had to be mounted in rotating capping turrets, fitting in a small space and yet always remaining connected to a communication unit at the base of the line. What’s more, they had to perform part of the data processing on the edge, in order to not overload the central system. And of course, the upgraded capping machine had to be made smart without costing sensibly more.<h3 dir="ltr">An Innovative System for Predictive Maintenance</h3>To accelerate the process, AROL relied on a cost-effective, open-source system based on a combination of Arduino Pro’s Nicla Sense ME and Portenta Machine Control. This allowed their engineers to work with advanced sensors and high computational power, while still leveraging the ease of off-the-shelf components. With the support of a problem-solving and forward-thinking partner, the company quickly developed and tested a working solution: “The Arduino team was very proactive in understanding our requirements and providing a business-oriented solution with a customer-focus approach,” commented Denis Ruffino, R&amp;D technical director at AROL.AROL fitted each of the 15 capping turrets in one of their machines with a compact <a href="https://www.arduino.cc/pro/hardware-nicla-sense-me/" target="_blank">Nicla Sense ME</a>, providing vibration and temperature detection thanks to state-of-the-art, onboard Bosch Sensortec sensors – as well as the&nbsp;computational power to process part of the data directly at the level of the sensing nodes. At the base of the machine, one <a href="https://www.arduino.cc/pro/portenta-machine-control" target="_blank">Portenta Machine Control</a> was easily mounted on DIN rail to concentrate communication with all the sensors via Bluetooth®.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA4NDQ4MzMxNTIxLVBNQ19sb3cuanBlZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 300px;" class="fr-fic fr-dib">Having chosen an open-source solution, AROL is poised to scale up the idea with limited refactoring – and further upgrade their advanced capping machines, raising the bar of innovation for their industry.

Seeking a way to make their high-speed production equipment even more reliable and productive, AROL recently decided to develop an innovative solution to enable data monitoring and predictive maintenance, upgrading their offering with an Industry 4.0 approach.

AROL Tops Capping Machines with Smart Maintenance

In this interview, Samet Kütük, a board member and Marketing/Go-To-Market working group chair of the Autoware Foundation and CTO and co-founder of Leo Drive, shares insights with Ian Dickson into his eight-year journey developing level four autonomous vehicles. He discusses the crucial role of the Autoware Project, the world’s leading open-source initiative for autonomous driving. Autoware is the partner of the 2023 Autonomous Vehicle Report. Download the report to read more interviews.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzAxMTE0NjE1NTg3LUFWIGRvd25sb2FkIGJhbm5lci5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib"><iframe width="540" height="450" src="https://c8056756.sibforms.com/serve/MUIFAMBWtqlAziu5EwFUIjLCWmRdzCiwOJRx3R3LyYkfG1dfYIvOKLn1dfBY8M58jYUZY8sQm7DnIOmrBjZ93ycsAv4ICzMbRD95I3S4FIWFM6EU4i3C7yzFNYn-Kfm0vJN96NgNO2_w4BQcrnOEwEVShkElbpBLjOT3ARKFBFADGHd-PDjMwJZI5avI3TUfr-xTC_fVunqO5MKo" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true">&nbsp;</iframe>&nbsp;<h3>Can you give us an introduction to what Autoware is?&nbsp;</h3>Samet Kütük: The Autoware Project originated in 2015 at Nagoya University in Japan, initiated by Professor Shinpei Kato. Following its foundation the next year, Autoware has grown into being the world’s leading open-source project for autonomous driving, boasting more than 70 members, including many prominent companies working on autonomous vehicle technology, as well as 23 university partners.&nbsp;<h3>How does one become a member of Autoware?&nbsp;</h3>Samet Kütük:&nbsp;Autoware is an open-source project hosted on GitHub. Individuals or companies interested in joining can explore the codebase on their own. If they wish to engage further, they can reach out to us. The board evaluates the potential value of new members, offering different membership packages, including premium memberships and industry memberships, with varying fee structures for annual participation.<h3>&nbsp;Why is the Autoware project open source?&nbsp;</h3>Samet Kütük: The Autoware project adopts an open-source approach to address issues related to proprietary solutions. Many players in the industry follow proprietary models, which lack transparency and can create barriers for vehicle manufacturers (OEMs) and any potential autonomy users. Autoware’s open-source nature simplifies onboarding for OEMs, making the process more straightforward and cost-effective. This approach also allows for concept validation without heavy initial investments, providing a flexible and accessible platform.<h3>Does Autoware accelerate the development of autonomous vehicles?&nbsp;</h3>Samet Kütük:&nbsp;Absolutely. Autoware not only accelerates development but also aligns with the software-defined vehicle paradigm, emphasizing openness and transparency. Collaborative frameworks like Eclipse SDV and SOAFEE indicate a move towards more open approaches in the industry. Autoware’s open ecosystem facilitates collaboration between applications and hardware, enabling a smoother development process. Can you tell us more about the Robot Operating System (ROS)?Samet Kütük:&nbsp;Autoware is built on the Robot Operating System (ROS), a middleware with a wide audience. ROS simplifies system understanding and offers a direct path to product development. The flexibility extends to operating systems, with partnerships with major companies like Arm, providing custom builds to support the architecture. ROS plays a crucial role in data exchange, connecting vehicle components, sensors, and the operating system.&nbsp;<h3>What feedback have you received from people and members who’ve used the operating system?</h3>Samet Kütük:&nbsp;Member companies, including Leo Drive, develop autonomous solutions using generic Linux distributions for operating systems. While generic Linux distributions are suitable for initial development, some companies choose more specialized OSs, for production-ready systems once prototyping for concept validation is complete. Autoware project also has a direct path to use safety-capable operating systems of which we are discussing with our alliance partners. Where do you think we are with autonomous vehicles at the moment?&nbsp;Samet Kütük: Assessing the current state of autonomous vehicles is challenging. Even well-funded companies, like Argo.AI and Cruise, face difficulties, leading to operational restrictions, or closing shop. Regulatory uncertainties, especially for autonomous vehicles operating in densely populated urban areas, present significant hurdles. However, there are already significant success stories in geo-fenced applications in controlled environments, such as ports and warehouses.&nbsp;What’s your vision for the future of autonomous vehicles? Do you think geo-fenced areas will be the first application?&nbsp;Samet Kütük:&nbsp;Personally, I see good adoption in geo-fenced and controlled environments. While robo-taxis are not excluded from the future, regulatory compliance is a priority. Beyond regulation, technology development poses challenges, especially in diverse environmental conditions. Success stories are emerging in controlled environments, addressing transportation gaps and contributing to a greener planet.<h3>How do you believe autonomous vehicles will benefit society?&nbsp;</h3>Samet Kütük: Autonomous vehicles offer numerous societal benefits, including reduced road accidents caused by human factors. Shared mobility can fill transportation gaps, leading to fewer vehicles on the road, decreased traffic congestion, and lower greenhouse emissions. The positive impact extends to creating a more sustainable and environmentally friendly planet.&nbsp;<h3>What else do we need to know about Autoware?</h3>Samet Kütük:&nbsp;When considering Autoware, it’s crucial to understand that it is a full-stack software for autonomous driving, covering everything from vehicle and sensors to perception, localization, planning, and control. Autoware replaces human senses in a vehicle with sensors, transforming a traditional vehicle into a self-driving platform. Additionally, our commitment to the software-defined vehicle paradigm, through collaborations with external alliances like SOAFEE, Eclipse SDV, AVCC , COVESA, and AUTOSAR is integral to our future endeavors.<hr><h3 dir="ltr">Click through to read each of the report's chapters.</h3><main><h3><a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report" target="_blank" rel="noopener noreferrer">Introduction</a> <a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report?chapter=1" target="_blank" rel="noopener noreferrer">I: Sensing Technologies</a> <a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report?chapter=2" target="_blank" rel="noopener noreferrer">II: Thinking and Learning</a> <a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report?chapter=3" target="_blank" rel="noopener noreferrer">III: EDGE and RTOS</a> <a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report?chapter=4" target="_blank" rel="noopener noreferrer">IV: Communication and Connectivity</a> <a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report?chapter=5" target="_blank" rel="noopener noreferrer">V: Security</a> <a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report?chapter=6" target="_blank" rel="noopener noreferrer">VI: Tech Stack</a> </h3></main><h2>Leadership Interviews</h2><h3 dir="ltr"><a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report-interview-accelerating-av-development-through-customer-collaboration" target="_blank" rel="noopener noreferrer">Interview with Mouser </a><a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report-interview-building-trust-in-autonomous-driving-navigating-future-reliability-and-milestone-achievements" target="_blank" rel="noopener noreferrer">Interview with Murata&nbsp;</a><a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report-interview-the-role-of-pcb-in-shaping-autonomous-vehicle-development/draft" target="_blank"></a> <a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report-interview-the-role-of-pcb-in-shaping-autonomous-vehicle-development" target="_blank" rel="noopener noreferrer">Interview with MacroFab</a><a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report-interview-exploring-the-future-of-high-performance-computing-in-autonomous-vehicles/draft" target="_blank"></a> <a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report-interview-exploring-the-future-of-high-performance-computing-in-autonomous-vehicles" target="_blank" rel="noopener noreferrer">Interview with Nexperia</a><a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report-interview-driving-global-standards/draft" target="_blank"></a> <a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report-interview-driving-global-standards" target="_blank" rel="noopener noreferrer">Interview with SAE International</a><a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report-interview-advancing-open-source-development-in-the-automotive-industry/draft" target="_blank"></a> Interview with Autoware Foundation<a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report-interview-insights-from-nvidia-about-artifical-intelligence-in-avs/draft" target="_blank"></a> <a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report-interview-insights-from-nvidia-about-artifical-intelligence-in-avs" target="_blank" rel="noopener noreferrer">Interview with NVIDIA</a></h3>

In this interview, Samet Kütük, a board member of the Autoware Foundation -  the partner of this report - shares the crucial role of open-source initiatives for autonomous driving

2023 Autonomous Vehicle Report Interview: Advancing Open-Source Development in the Automotive Industry

<h2 dir="ltr" id="isPasted">Introduction</h2>Apache Kafka is a popular open-source distributed streaming platform that was developed by the Apache Software Foundation (Kafka, 2023). It is used to build real-time data pipelines and streaming applications that handle large volumes of data. Apache Kafka is designed to provide scalable, reliable, and fast data services making it a popular choice for data engineers and software developers. In this blog post, we will discuss what Apache Kafka is, how it works, and its pros and cons especially for the autonomous vehicle industry.<h2 dir="ltr">What is Apache Kafka?</h2>Apache Kafka is a distributed streaming platform that was first released in 2011. It is built on top of the Apache ZooKeeper, which is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. It is designed to handle high volumes of data and provide real-time access to that data.Apache Kafka is a publish-subscribe-based messaging system, which means that producers publish messages to topics, and consumers subscribe to those topics to receive messages. It also provides support for multiple consumer groups, which allows consumers to read messages in parallel. This makes it a highly scalable messaging system that can handle large volumes of data with low latency.<h2 dir="ltr">Advantages of Apache Kafka</h2>Apache Kafka has several advantages over traditional messaging systems.&nbsp;Scalability:&nbsp;Apache Kafka is highly scalable and can handle large volumes of data. Kafka can reliably process and store petabytes of data, making it a popular choice for large-scale data processing.&nbsp;Real-time Access to data:&nbsp;It provides real-time access to data, which means that consumers can receive data as soon as it is produced. This makes it ideal for real-time data processing applications, such as fraud detection, real-time analytics, and monitoring.&nbsp;Fault-tolerance:&nbsp;Apache Kafka is designed to handle failures and continue to operate without interruptions. Kafka provides a high level of durability, ensuring that data is never lost.&nbsp;Multiple consumer groups:&nbsp;This feature allows consumers from multiple groups to read messages in parallel. This makes it possible to process large volumes of data in real-time.&nbsp;<h2 dir="ltr">Disadvantages of Apache Kafka</h2>While Apache Kafka has several advantages, there are also many challenges to its successful integration with existing systems.&nbsp;Resource requirement:&nbsp;Apache Kafka requires a significant amount of resources, including memory and processing power. This can make it challenging to deploy on low-end hardware or in resource-constrained environments.&nbsp;Complexity:&nbsp;Apache Kafka presents a steep learning curve, and it may be difficult for beginners to learn and set up. Kafka requires a good understanding of distributed systems and messaging architectures, which can be challenging for new users.&nbsp;Scalability:&nbsp;It may not be suitable for small-scale applications or applications that do not require real-time data access. The overhead of setting up a Kafka cluster may outweigh the benefits for small-scale applications.<h2 dir="ltr">Apache Kafka and Uncrewed Vehicles</h2>Apache Kafka can provide numerous data streamlining applications to the uncrewed vehicles industry. Unmanned and autonomous vehicles are becoming widely used in a number of applications such as delivery, disaster relief, monitoring and mapping etc. They are routinely deployed to collect huge amounts of data through connected sensors and cameras. This data is then communicated to base stations and other vehicles. When operating in a remote or shared environment, unmanned vehicles need to share data with other entities as well. For example, in a disaster relief scenario, unmanned drones can be used to make 3D maps of the affected area, detect survivors and provide critical supplies. Huge amount of data is collected by swarms of unmanned vehicles and relief workers organize the best response based on this data. This is possible only when a robust data communication infrastructure exists in which the autonomous vehicles, their base stations, and the police and emergency departments of the area are involved.&nbsp;Apache Kafka can help in processing this data in real-time from uncrewed vehicles, such as drones, self-driving cars, and autonomous robots (Bear, 2017). The data can be processed in real-time, and the results can be used for further analysis and to make decisions that can help the vehicles navigate safely and avoid collisions. Apache Kafka was utilized to demonstrate a connected automotive infrastructure of 100,000 cars in 2020 (Waehner, 2020), (Waehner, 2020). Such an infrastructure can be extended to self-driving cars and other autonomous vehicles to streamline data.&nbsp;<h2 dir="ltr">Conclusion</h2>Apache Kafka is a distributed streaming platform that is used for building real-time data pipelines and streaming applications. It is highly scalable, fault-tolerant, and provides real-time access to data. However, it is a complex system of data streamlining hence, it presents a steep learning curve for beginners. It is ideal for large scale systems where resources such as memory and processing are not an issue. Unmanned vehicle applications where large amount of data is being collected and communicated in real time, offer perfect opportunities for the deployment of Apache Kafka.<hr><h2 dir="ltr">References</h2>Bear, J. W., 2017. IoT and the Autonomous Vehicle in the Clouds: SLAM with Kafka and Spark Streaming. Inside Machine Learning.Kafka, 2023. Apache Kafka. [Online]&nbsp; Available at: https://kafka.apache.org/Waehner, K., 2020. Apache Kafka in the Automotive Industry. [Online]&nbsp; Available at: https://www.kai-waehner.de/blog/2019/11/22/apache-kafka-automotive-industry-industrial-iot-iiot/Waehner, k., 2020. Streaming Machine Learning at Scale from 100000 IoT Devices with HiveMQ, Apache Kafka and TensorFLow. [Online]&nbsp; Available at: https://github.com/kaiwaehner/hivemq-mqtt-tensorflow-kafka-realtime-iot-machine-learning-training-inference

Apache Kafka is a popular open-source distributed streaming platform that was developed by the Apache Software Foundation (Kafka, 2023). It is used to build real-time data pipelines and streaming applications that handle large volumes of data.

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