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The Baltimore company remains resolute in its commitment to honor LaPere's vision as former COO and cofounder Davis steps into the top leadership role.

EcoMap Technologies names Sherrod Davis CEO following Pava LaPere’s death: ‘My focus is on ensuring that we turn her vision into a reality’

Baltimore police are hunting a suspect believed to have killed rising business star Pava LaPere, the 26-year-old CEO of EcoMap Technologies.

Baltimore police hunt suspect in slaying of rising business star Pava LaPere

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Ecobot

ecobot

Meet the Winners of Startups in North and Central America

If you're a Swift developer with a chatbot idea brewing in your mind, and you're keen on bringing it to life using your favorite programming language, then the simplest way to do so is by using Botter - an open-source, cross-platform framework. It leverages the power of Vapor in conjunction with platform-specific frameworks for swift and convenient cross-platform chatbot development. chatbot 🤓 Theory Botter enables you to receive and send text messages, media attachments, files, and commands for a high-quality user experience, all while using Swift - the native programming language for all Apple developers. Botter works in tandem with , which handles the server-side functions of your project. This powerful combination allows you to focus on what matters most - creating an engaging and effective chatbot. Vapor 🔥 Practice Next, I will demonstrate step by step how easy it is to write a simple chatbot using this framework: 👶 Setting Up Your Vapor Environment As I mentioned earlier, our project will be built on the foundation of Vapor. Therefore, we need to install it locally. You can do this by running the following command in your terminal: brew install vapor Now that we have Vapor installed let's create a new empty Vapor project. You can do this by running the following command: vapor new hello -n After running this command, a new project will be generated in the directory. The next step is to open the file in Xcode. You can do this by navigating to the project directory and opening the file with the following commands: /hello Package.swift cd ./hello
open Package.swift 🔀 Adding Dependencies To use Botter, we need to add it as an external dependency. Open your project's Swift Package Manager file and add the following line to the dependencies array: .package(url: "https://github.com/CoolONEOfficial/Botter.git", branch: "main"), Next, add Botter to the dependencies of your application by including the following line in the targets section of the same file: .product(name: "Botter", package: "Botter"), You can then try to build the project. This will ensure that Botter is correctly linked to your project and ready to use. 🏗️ Describing the Bot Logic In Botter, there are three entities that describe a chatbot: : interacts with the platform, for example, to send something, modify an existing message, etc. bot : retrieves data via webhooks. updater : processes the received data and passes it to you. dispatcher It's a good practice to wrap these entities in your own entity, like so: class EchoBot {
    public let dispatcher: Botter.Dispatcher
    public let bot: Botter.Bot
    public let updater: Botter.Updater

    public init(settings: Botter.Bot.Settings, app: Application) throws {
        self.bot = try .init(settings: settings)
        self.dispatcher = .init(bot: bot, app: app)
        self.updater = .init(bot: bot, dispatcher: dispatcher)
    }
} To subscribe to text messages from the user, you can refer to the : dispatcher dispatcher.add(handler: Botter.MessageHandler(filters: .all, callback: handleMessage)) And in the method, you can specify the return of the just-received message like this: handleMessage func handleMessage(_ update: Botter.Update, context: Botter.BotContextProtocol) throws {
    guard case let .message(message) = update.content else { return }

    guard let params = Botter.Bot.SendMessageParams(to: message, text: message.text) else { return }

    try bot.sendMessage(params, platform: message.platform.any, context: context)
} In the same way, you have the ability to receive other types of updates, such as receiving an image or clicking on a button sent earlier. 👷‍♂️ Configuring Your Bot To get your chatbot up and running, you'll need to specify platform-specific settings, such as the port and URL where the chatbot will be accessible, authorization keys, and so on. You can specify these in the file as follows: configure private func configureEchoBotter(_ app: Application) throws {
    let botterSettings = Botter.Bot.Settings(
        tg: tgSettings(app)
    )
    let bot = try EchoBot(settings: botterSettings, app: app)
}

func tgSettings(_ app: Application) -> Telegrammer.Bot.Settings {
    var tgSettings = Telegrammer.Bot.Settings(token: ””, debugMode: !app.environment.isRelease)
    tgSettings.webhooksConfig = .init(ip: "0.0.0.0", baseUrl: “”, port: 123)
    return tgSettings
} Each platform requires its own settings for chatbots to work. For instance, in the case of Telegram, you'll need a token, a URL, and a port for the chatbot. To get a token, you'll need to follow the steps in the official . You'll also need to set up your server to be accessible on the internet. This can be done using numerous services, such as . guide http://localhost.run/ Once all the parameters are specified, don't forget to call the bot configuration method in the body of the method: configure try configureEchoBotter(app) And start receiving updates from the platform: try bot.updater.startWebhooks(vkServerName: Constants.vkServerName).wait() If everything is specified correctly, upon launch, the chatbot should function and duplicate messages. For your convenience, I've uploaded the above example to a repository: https://github.com/CoolONEOfficial/echobot ⚙️ Advanced Bot Logic Examples Now that we've covered the basics let's delve into some more complex bot logic that you might find useful. 📝 Editing Sent Messages You can edit a message that has just been sent using the method as follows: editMessage try bot.sendMessage(params, platform: message.platform.any, context: context).map(\.first).optionalFlatMap { message in
    try! self.bot.editMessage(message, params: .init(message: "Other text"), app: self.app)!
} 🖼️ Attaching Images to Messages You can attach an image to a message by adding the parameter with an array of attachments. This can be media content or documents. Here's an example: attachments Botter.Bot.SendMessageParams(to: message, text: message.text, attachments: [ .init(type: .photo, content: .url("")) ]) 🔘 Adding Buttons to Messages You can add buttons to a message using the parameter: keyboard Botter.Bot.SendMessageParams(to: message, text: message.text, keyboard: [ [ Button(text: "Tap me", payload: "some data") ] ]) To handle button clicks, you should process them in the method, where the button's payload can be found in the parameter. handleMessage update ☁️ Deploying Your Bot In the previous steps, we managed to get the chatbot running locally, but where can you host it for continuous use? The good news is that the project supports both macOS and Linux, so you can take advantage of numerous PaaS services. You could start with Heroku or its alternatives, although due to port restrictions, you'll need to launch separate instances of the chatbot for each platform. However, a better solution for me was a dedicated macOS server from MacStadium, which doesn't impose such restrictions. 📚 Resources You can find even more examples of Botter in use in my , which utilizes all the capabilities of the framework. PhotoBot project If you're interested in delving deeper into the foundation of Botter-Vapor, I recommend the following resources: Official Vapor docs Server Side Swift with Vapor book Awesome Vapor 🏁 Conclusion We've walked through the steps of setting up and configuring a chatbot using the Botter framework and even touched on some advanced features. But this is just the beginning. The real magic happens when you start to experiment, innovate, and build something that's uniquely yours. Remember, every great chatbot started with a single line of code. And with Botter, you have a powerful tool at your fingertips that can make your chatbot dreams a reality. Whether you're looking to build a simple bot for fun or a sophisticated one for your business, Botter provides the flexibility and functionality you need. So why wait? The world of chatbots is at your fingertips. Dive in, start coding, and see where your creativity takes you. Your first chatbot could be just a few lines of code away. Your Chatbot Journey Begins Now! Happy coding, and remember - the only limit is your imagination! 🌈 Also published . here

Swiftly Starting Your Chatbot Journey: Building a Chatbot with Botter

More than 600 developers compete for $500k in prizes and business incubation and the potential to tap into $200m in Neo’s EcoBoost grant resources. Neo recently kicked off their Polaris Hackathon with a goal to help developers succeed with Web3 and foster the ongoing expansion of a thriving ecosystem built on the Neo N3, the newest and most powerful version of the Neo blockchain development platform. Along with the launch of N3 in 2021, Neo’s first hackathon was also introduced calling upon teams to spearhead projects and take advantage of Neo's powerful features. More than 700 participants and 95 teams responded, resulting in 11 winning projects–all of which received post-hackathon incubation support and are now building on Neo N3. The event incubated up-and-coming DeFi, crowdfunding, NFT-centric, and other innovative projects such as Humswap, ToTheMoon, Rentfuse, Lyrebird Finance, and many more. This year, Neo returned with the Polaris Launchpad as the next opportunity for developers to join the growing Neo Ecosystem. The hackathon series was back with an expanded prize pool of US $500, 000 and opportunities for post-hackathon business incubation support through Neo’s $200m EcoBoost program. The judges evaluated more than 600 qualifying entries and awarded five Grand Prizes worth US$35,000 each in NEO tokens. The five Grand Prizes were given across five competition categories. Grand Prize-winning projects that received a multiplier award in the Planathon phase will have their prizes multiplied by a factor ranging from 1.1 to 1.5x. Each winning project team also received a custom-tailored set of post-hackathon incubation opportunities through participation in the Neo Global Development (NGD) EcoBoost program. Fifteen additional project entries have been selected as Polaris Launchpad award winners: 14 Excellence Awards and 1 Special Mention Award. Each winning project will receive US $8,000 in NEO tokens. Award-winning projects that receive a multiplier designation in the Planathon phase will also have their prizes multiplied by a factor ranging from 1.1 to 1.5x. Along with the prizes, the winning project teams will receive customized consultation time with the NGD EcoGrowth team regarding their projects. Speaking on the occasion, John Wang, Head of Neo Eco-Growth said, “The high quality and innovative nature of these award-winning projects point to a bright future for Neo, the blockchain industry, and Web3, I commend the efforts of the many skilled participants in this event. I expect to see more achievements from them in the coming events in the Neo Launchpad hackathon series.” Best Middleware Project Incorporating NeoFS/OracleProject was awarded to , a decentralized data storage solution on Neo N3 and NeoFS. Greenfinch will bring a range of capabilities such as granular permissions, privacy, and more to decentralized storage on Neo N3. Greenfinch “We are ecstatic about winning the grand prize for the best middleware project incorporating NeoFS/Oracle. We set out to build a product that will ultimately benefit the community, but also show the world the incredible functionality of Neo and NeoFS,” said Alex Walker, a member of the Greenfinch team. The best DAO project was awarded to , a smart contract and a client-side Javascript library that allows a developer with only typical website development skills to create robust DAOs on the Neo N3 blockchain. Neodao was awarded the best NFT / Metaverse Project. It is a GameFi built on Neo. Players use their NFTs as playable characters to earn tokens that can be used in-game to improve a player’s gameplay or be traded for profit. Players use their NEO wallets as game accounts to log in to the game and can use NFTs as playable characters and items. Dogerift The best DeFi Project went to , a decentralized crypto trading protocol with swap, staking, and liquidity pool features. Sky, a member of the Pumpkin Swap team commented, “I chose the Neo N3 network as the easiest and most complete solution for my ideas. The Neo Polaris Launchpad hackathon has been a perfect opportunity to show my idea. My passions are coding and bringing my ideas to life. This has given me a chance to share my work and passion with the Neo community,” Pumpkin Swap , a social platform on Neo N3 that natively allows users to explore the integration of Web2 and Web3, was awarded the Best General Category Project. Bamboo is a new Web3 social platform that generates exclusive NFTs by linking users’ existing centralized and decentralized social information. Users can use this exclusive NFT to participate in personal information upgrades, play social games, and add value to their own data and behaviors. Bamboo On June 8th, Polaris Launchpad will move into the final phase of the hackathon, Polaris Plus. As this final round kicks off, the Neo community will vote for their favorite projects using their Neo tokens. Neo will then match the community votes through quadratic means, amplifying the voice of the community. Find the details about all Polaris Launchpad Hackathon winners here.

Neo Announces the Winners of Polaris Launchpad Hackathon

Dogecoin: Everything You Wanted To Know About This Memecoin Making A Mockery of 'Serious' Crypto

This is to open a discussion about how companies can make money using blockchain technology which is, in essence, open source software and/or protocols. As an analogy, think going back to the early 1990s to discuss how companies could make money from TCP/IP. Blockchain Instead of discussing what the blockchain technology is, let’s focus on what this tech enables, on its key functionality: the ability to record cryptographic proof of any piece of information (a document, a transaction, an account balance) into an immutable, shared database. This is only useful in the context of more than one entity needing the information; when there’s an interaction between two or more parties. In such context, the blockchain technology enables verification of information that does not need a trusted third party. There is no need for a notary, an official (land, vehicle, ship, plane, vaccination…) registry or a bank. In a sense, it’s the next logical step from the shared / cloud model, because now the trusted operator of the system doesn’t exist, so it can’t change the rules the service operates under. Also, the users do not need a trusted operator to validate their own and other user’s information. This makes things possible that were impossible without blockchain, including: Having instant, verified proof of the current and past states of a business (or any other) process. Having instant, verified proof of fulfilling a contractual obligation. Holding and transferring value in a decentralized way, without the need of a trusted party and without the legal structure of a national state. Creating governance and socio-economic structures independent of a national state or a corporation. Sharing costs and benefits of a business network or an ecosystem. Designing incentives for independent actors of an ecosystem. Combining the ownership, the distribution of the value and the incentive in a single asset (a crypto-token). Combining information like identity, legal status, accounting transaction, payment and probably more (like the physical status of goods) into a single transaction. Business models vs Technology The revolution that the internet brought was caused by new business models being enabled by technology, not by the technology itself. Similar will be true about the blockchain tech. Still, someone has to develop and build the technology: the specific solutions, standards and protocols that will be invisible to the end user and will make new business models seamless and obvious. A few examples of solutions needed in the technology realm include the basic ones: - Handling online identity and everything that comes with it, including authentication and privacy. - Safe, preferably decentralized, integration/interaction of crypto with fiat (all crypto hacks happen on exchanges and other centralized services). - Integration (enforceability) of smart contracts into existing legal systems. - Verifying if what is on the blockchain is true (does it match reality). And more complex solutions that need the basic ones to work: - Reputation protocols, with staking mechanisms. - Tokenization of assets, starting with the virtual (invoices, claims, music, movies, brands, website addresses) and expanding to the real world. - Stable coins backed by real world assets, like money or anything else (including asset tokens). There are more examples, but as things get more complex, the line between the technology and the business model starts to blur. Here are a few more examples of possible technologies / business models: - Invoice financing for supply chains. - Tamper proof product provenance system. - Tamper proof document control system. - Loyalty program on a blockchain, with public, tradable perpetual discount tokens. - Reversed advertising ‘pay-to-pitch-me’ model, where people can create their own ‘ad profile’ sharing verified information about themselves, at their own discretion, and getting paid for reading/watching promotional material. - Investing directly into startup projects and companies without any financial intermediaries like VCs, stock exchange, mutual funds etc., by buying digital coins. Let’s call this idea an ICO (or STO). - Community-owned, self-driving cars that work in tandem with public transport. - Driver and community owned ride-share platform that uses discount tokens and verified reputation mechanics. Market reality So, how do you make money in this space? And how do you go about it? What’s the next logical step? Let’s start with some observations about the state of the market. It seems there’s a big divide between the crypto world and the real, business world. The crypto world is revolving around exchanges and converting crypto to fiat and vice versa. The business world (with notable exceptions like Foxconn) thinks about private blockchains and shared data models (courtesy of IBM). Both worlds and the people living in them don’t understand the other world and the needs, drives and opportunities there. The real world is obviously much greater than the crypto world, so startup opportunities (or at least the potentials) are greater. But a reality facing or business oriented startup needs to understand 2 key things: By definition, if it’s doing anything with public blockchain technology (like raising capital through a token sale) it is a part of the crypto works, whether it wants it or not. It needs to respect this world, its rules, customs and ways of doing business. Secondly, any real-world facing project will be initially an educational endeavor; a research project on the way to understanding blockchain application in solving business problems. Ideally, this should be conducted together with potential or actual clients/users. The ‘Build it and they will come’ strategy might now work in such a complex mix of technology, business models and product definition. Token as a product Going back to the idea of understanding and embracing the crypto space: selling tokens / coins is one obvious way of making money. ICO funding in 2017 was already higher than VC startup/seed funding ( ). The amount of money raised in 2018 is even higher ( ) despite the supposedly cold market. https://www.cnbc.com/2017/08/09/initial-coin-offerings-surpass-early-stage-venture-capital-funding.html https://www.coindesk.com/6-3-billion-2018-ico-funding-already-outpaced-2017/ The example of EOS proves that an outrageously successful ICO is possible, as long as it’s done by the crypto world rules, and the token is treated as a product in its own rights. A product needs to have its unique selling point or competitive advantage, clearly defined target market, price point, etc. One of the key factors that attracts (even serious) investors to ICO is the promise of token liquidity: tokens listed on exchanges are much more liquid than shares in a private company. This should be one of the key features of any token offered. Otherwise, if there is no marketplace for the token, why not offer equity in a private company? Listing a token on an exchange creates a situation where the price is volatile and might fall below the original ICO price. This happened to a lot of tokens that have no use case or no working product or platform, so there is no demand for the token other than being speculative, or purely sentiment driven. One of the reasons this happens is because projects want to avoid creating security tokens at all costs and want to have a utility token instead. So, in the interim between the ICO and launching the platform/product that uses the utility token, there’s no utility, hence no utility value. A few utility tokens that actually have a use case don’t have this problem ( ). https://coinmarketcap.com/currencies/binance-coin/ Security token One way of dealing with this issue (low token price) is market manipulation by creating an artificial demand for the token (= spending money raised in the ICO to buy your own tokens on the exchanges). Another (maybe radical) idea would be to get over the fear of security tokens. Most probably, any ICO style capital raised for a completely new idea/project/product/company will be considered a security offering by the SEC anyway. So, instead of designing a utility token that needs a use case to work, new projects could focus on designing their tokens as security tokens that make sense. Tokenizing shares or creating tokens that resemble stock with dividends and voting rights doesn’t make sense, because stock exchanges and the entire legal infrastructure already exist. But there are at least 2 interesting security token designs: 1. Let’s call it a buyback token: A new project issues tokens to raise capital, to create a business that will be generating revenue. It can be a SaaS platform, a coffee shop, a solar power plant. Once the business is operational, part of the revenue is used to buy back tokens from the market and burn them. Just like with stock buyback. This increases liquidity of the token and limits the supply in circulation. Both factors increase the price of the token. Any holder has the option to exit now or hold longer and wait for further price increases. 2. Let’s call it a virtual taxi medallion or virtual mall token: A new project is working on a platform that will allow others to conduct business and offer their services. For example: a network of independent operators, who use specific protocols to offer liquidity and payment solutions for supply chains. The platform will require from any operator to buy and hold in escrow (as stake) a specific amount of the medallion/mall token. There will be only a limited supply of the tokens, so as the market grows and becomes more attractive, the demand for the tokens increases and so does the price of the token. Just like with a real taxi medallion or mall space. There can also be an entity that applies these two models and offers ICO rails for impactful, real life projects, something like an Impact Kickstarter on the blockchain. Early stage, risky, but important and powerful ideas, like water desalination technology or ocean plastic recycling could now raise seed/early capital easier. The ICO rails company can offer due diligence, KYC, smart contract development, and access to professional funds, for a fee plus initial token supply. Part of this business would also be a responsible ICO model where a project would only be able to raise funds based on a proposed budget and the money would be released in milestone payments, based on the evaluation of the progress vs the budget. I think this idea would be interesting for Binance (https://techcrunch.com/2018/08/23/binance-labs-incubation-program/) or EOS (https://www.econotimes.com/Blockchain-startup-Blockone-announces-1B-venture-fund-EOS-VC-1330074 ). Adoption and time to revenue Moving on to the real world applications, there has to be a product or a service that generates revenue somewhere along the way. Selling tokens is not generating revenue. It’s raising capital in order to create something that will generate revenue and eventually the return on the capital raised. The key notion here is that the capital raised needs to last long enough for the startup to generate revenue. The longer it takes or generate revenue, the higher the amount of initial capital is needed. Another question is the level of adoption required to make a project/business profitable. This is very important especially for a startup that wants to build value based on a network effect. A lot of times a network based business is only profitable at a late stage of maturity and adoption. This creates a situation where the profit is generated during the last mile of a 1,000 mile journey, further increasing the capital requirements and the risk of a project. The market situation for a real world facing blockchain startup looks difficult: business clients don’t understand the technology or its implications, there is a lot of money needed to develop technology and applications that don’t exist today. Getting market adoption can be long and expensive. Research Is there a better way for a blockchain startup other than raising as much money as possible (preferably at least $100M) and hoping it will hit a fast moving market target in 1, 2 or maybe 3 years? Is it possible to start generating revenues faster, while developing the world changing product? One solution would be to have part of the team offering consulting services focused on business model transformation. Another would be to offer joint blockchain research/discovery projects, preferably using an agile approach with sprints, constant feedback loops etc. However, any blockchain startup approaching a potential enterprise client needs to understand that a Fortune 100 company has the brand power and the financial strength to develop and introduce blockchain solutions (or any other innovation) to their entire supply chain on their own. Just like Foxconn did ( ). Why would they decide to work with a blockchain startup that doesn’t have a working product/platform or a significant customer base? They could take any interesting ideas presented by the startup and go to IBM or Accenture to develop a proprietary blockchain platform or solutions. https://www.coindesk.com/foxconn-wants-take-global-supply-chain-blockchain An enterprise would work with a startup for 2 reasons: If there is a team of people there who came up with great ideas (better than anything IBM or Accenture or their internal team can come up with) and can now start working on designing and building products and solutions. People who understand both blockchain/crypto and the real world and can deliver quality software. If the startup has real-life, front-line trenches experience in areas like ICO, KYC, compliance, utility vs security tokens, talking to the SEC, minting tokens, tax implication, and many more. Hiring a startup would save them a lot of time and money. They could leverage a good team and its experience instead of starting from scratch. This would be an interesting idea as long as the enterprise could partner/hire/form a JV with the startup to develop solutions tailored specifically for them and not necessarily the original world changing product that the startup is working on. In this model, the startup is working as a consultant/think tank/research company for the enterprise instead. Such a relationship can be very beneficial for the startup and can be applied to 1) Increase the prestige of the startup, 2) gain more high profile clients, 3) use the work done for enterprises to develop own products, platform and services. Here is an example of how this could work for a specific business model: The idea of a discount token that is not spent, but gives perpetual discounts to holders is very powerful. A loyalty program built on this idea is a killer loyalty program. However, despite the idea of ‘the single ultimate loyalty token to rule them all’ being very tempting, it’s not realistic where there is no working platform and without large marketing budgets. Approaching any company or organization with the offer: “pay me so I can develop my platform on own token “ seems naive, because they can develop their own platform, with their own token, keeping all the benefits. So maybe there is a business case for a company that starts by developing discount token platforms for third parties, based on white label software solutions that are customized for a specific case. This entity can work for companies like airlines, local communities of merchants, municipalities, energy companies (with a focus more on specific incentives than loyalty). The offer would include KYC, ICO (I might be wrong, but I think having an existing use case makes it an actual utility token, not a security), a mobile and a web wallet for customers, a dashboard for the program owner. Having this in place, it’s possible to have one wallet that holds any compatible branded tokens, part of different loyalty programs, so the number of different tokens is not an issue from the user experience perspective. Such a startup can charge a fee and keep part of the new branded discount tokens (say 5%). It can even launch its own branded loyalty token program later on based on its own white label development kit. Education Taking the idea of doing paid research even a step further, is there a better way of doing business development? Getting paid for talking to businesses interested in blockchain instead of spending money on conferences? Maybe it would be possible to organize a high profile, C-level event (a dinner, a retreat) with a limited number of participants who pay to attend. They would have direct access to the brightest minds in the blockchain space and where no one is trying to sell them anything. There are already blockchain projects in existence that make money educating enterprises, BRI ( ) being the best example. https://www.blockchainresearchinstitute.org/ Decentralized and open-source As a side note, any blockchain startup would be smart trying to partner with other blockchain startups in the true spirit of decentralization, instead of trying to do everything in-house. Also, as a lot of the software is open-source, using it, even if it needs adapting, might be a cheaper, more efficient way of doing software developing. Amazon approach Supposedly “after reading a report about the future of the Internet that projected annual web commerce growth at 2,300%, Bezos created a list of 20 products that could be marketed online. He narrowed the list to what he felt were the five most promising products, which included: compact discs, computer hardware, computer software, videos, and books. Bezos finally decided that his new business would sell books online, due to the large worldwide demand for literature, the low price points for books, along with the huge number of titles available in print.” ( ) https://en.wikipedia.org/wiki/Amazon_(company) My personal opinion is that ‘the amazon approach’ today would be to get involved in the ‘security tokens ICO on rails’ project. This could later evolve into a Security ICO Exchange, something like a Nasdaq equivalent in the crypto space: self-regulated, with mature, structured process including project vetting and due diligence. Feel free to get in touch with me if you want to discuss token economics, strategy or applications: https://www.linkedin.com/in/michalbacia/ I’m able to explain very complicated subjects in a comprehensible way while making people laugh and engage.

Making Money on a Blockchain

, a professor at Benedictine College, Atchison, KS, earned his Ph.D. in mechanical engineering in 2016. He has also designed numerous tech devices. On September 29th, he and two of his guest-colleagues were speakers at the “Artificial Intelligence and the Human Person” lecture held on the Benedictine campus. It was part of a series of lectures which comprised the annual meeting of the . Dr. Charles Sprouse Fellowship of Catholic Scholars As the speaker who delivered the first talk, Sprouse discussed artificial intelligence in relation to its current and futuristic implications in the industry of transportation and what it would mean for passengers and pedestrians. _Welcome to the very first episode of the wHat iF Studios Podcast, an episodic audio program exploring the past…_www.podomatic.com Episode #1: The Fellowship of Catholc Scholars Convention - "Artificial Intel... Sprouse had decided he would lecture on the uses of AI in this particular field due to its current inundation of AI-minded developers. He informed his peers and the other listeners that at an AI conference earlier in the year the nonprofit “recognized 580 new startup companies in the area of smart transportation.” Thus, it is a relevant topic accumulating a growing interest among fulfillment organizations and potential users alike. Ecomotion “This is a huge field,” said Sprouse, who proceeded to go over a few of the nuts and bolts of the use of AI in our vehicles. With vehicles in the 21st century being “on demand,” for functions such commuting back and forth to work, an autonomous car could serve several helpful purposes. Sprouse poses that if a self-driving, AI-oriented car were able to hold multiple people and offer the same usability as an Uber vehicle, an individual who just needs a car for work commute could end up “sharing that car with about 20 people.” A number of people would own parts of a single car, so to speak. Thus, each of the joint owners would not have to pay as much for their segment of the vehicle as they would for an entire car of their own. Dr. Sprouse said this implication would likely result in “fewer cars” overall and that it would be economically beneficial to the car owners. So this function of AI in transportation would not only keep people’s transportation bills lower, but it could also have the potential to do less damage to the climate than our present transport situation. Currently, there is a bit of (what the scientific community believes to be) a global climate crisis due primarily to mankind’s greenhouse gas emissions. The average car generates about annually. six tons of carbon dioxide Using an AI system in a vehicle would save time. With its own artificial intelligence, the car would take the quickest route and would not spend as much time as you and I do in searching for an open spot in the parking lot. So it would be able to deliver more people to their destinations in a more timely fashion than any human taxi service. If Sprouse’s proposed Uber-like utility of vehicles is employed in the future, it could lower the threatening amounts of carbon dioxide being produced. And this, inevitably, would start to decrease the risk of extreme climate change.

Dr. Charles Sprouse Talks AI in Transportation: How AI Could Help Safeguard the Climate

This article covers the introduction of the very basic formulation of the major sensor-servo problem and then it presents its most common approaches like touch-based, vision-based, distance-based, and audio-based control. Cobot control is an established field that has already witnessed significant industry commercialization. The techniques needed to control human-cobot interaction and collaboration, nevertheless, are still being developed. Research on these topics is being performed in the disciplines of collaborative robotics and physical human-cobot interaction (pHRI). Three stacked levels of consistent behaviors have been explained by the authors of De Luca and Flacco's model for the cobot to attain safe pHRI: a.) Safety, b.) Co-existence, and c.) Collaboration a.) Safety The first and most significant characteristic of Cobots is safety. Although recent efforts have been made to standardize cobot safety, we are still in the early phases. Collision avoidance is a common method for addressing safety. This feature calls for high responsiveness, i.e., high bandwidth, along with robustness at both the control and perception layers. collaborative b.) Co-existence The ability of a cobot to coexist with people in the workplace is known as co-existence. This covers situations where a cobot and a human cooperate on the same activity without physical touch or coordination, such as in medical procedures where the cobot intervenes in the patient's body. c.) Collaboration Collaboration is the capacity to use direct human interaction and Collaboration to complete cobot’s tasks. There are two ways to do this: and , which involves explicit and intentional physical touch between humans and Cobots (where all the actions are exclusively guided by the exchange of information, e.g., in the form of voice commands, body gestures, or any other modalities). contactless cooperation physical collaboration Establishing tools for intuitive control by human operators, who may be non-expert users, is essential, especially for the second mode. In order to engage more organically from a human perspective, the cobot must be proactive in carrying out the desired activities and be able to deduce the user's intentions. I n the field of robotics literature, there are two major approaches for task execution that have emerged successfully sensor-based control and path/motion planning. Since sensor-based control closes the perception-to-action loop at a much lower level as compared to path/motion planning. It is more effective and adaptable for pHRI. It should be emphasized that control techniques have their roots in the servomechanism problem and have strong similarities to the functions of our central nervous system. The best-known example is image-based visual sensing, which uses visual feedback alone to directly control the cobot’s motion without the need for a cognitive layer or a detailed environment model. sensor-based Sensing the different types of Modalities for Control Recent advances in bio-inspired measuring technologies have reduced the cost and weight of sensors, thereby facilitating their usage on Cobots. These sensors include force/moment transducers, tactile skins, RGB-D cameras, and others. Depending on the task at hand, the works reviewed here use various combinations of sensory modalities. Overall there exist 4 types of cobot sensors, as mentioned previously: a.) Touch-based This inculcates techniques for analyzing and comprehending visual data to generate numerical or symbolic information that mimics human vision. Despite the complexity and high computing cost of picture processing, this sense's richness is unmatched. is essential for comprehending the surroundings and human intentions so that it can respond appropriately. Robotic vision b.) Vision-based Proprioceptive force & tact are both considered forms of touch, with the latter involving actual physical contact with an outside object. The perception of proprioceptive force is comparable to that of muscle force. Cobots can determine it by measuring joint position errors or by using torque sensors built into the joints. By depending on force control, it may then employ both techniques to deduce and adapt to human goals. On the other hand, human touch or somatosensation is caused by the activation of neural receptors, usually in the skin. These have served as design inspiration for tactile artificial skins that are extensively employed in human-cobot Collaboration. c.) Distance-based The only sense out of the other three that humans are unable to measure directly is this one. However, there are several instances of echolocation in the animal kingdom (such as in bats and whales). Cobots use capacitive, optical, ultrasonic, or ultrasonic sensors to measure distance. The importance of this particular "sense" in human-cobot Collaboration is driven by the correlation between safety and the separation of hazards. d.) Audio-based control Binaural audition is used to conduct sound localization in humans. We can establish the source's elevation and horizontal position by taking advantage of auditory clues in the form of phase/level/time changes between the left and right ears. Cobots can "blindly" locate sound sources. Thanks to microphones, which artificially imitate this perception. Even while two microphones mounted on a motorized head are the norm for robotic hearing, other non-biological designs like a head equipped with a single microphone or an array of multiple omnidirectional microphones exist. Conclusion and Future Perception Although touch and vision emerge nowadays as one of the most popular senses for collaborative robots, the advent of precise, cheap, and easy-to-integrate tactile, distance and audio sensors present some of the most astonishing opportunities for the future. Typically, we believe that the robot skins (e.g., on hands and arms) will simplify interaction, thereby amplifying the opportunities for human-robot Collaboration over the upcoming years.

Sensor-based Control in Cobots: Its Opportunities and Challenges

Robots, or as some are better called Cobots, that respond to the commands of operators and participate interactively in collaboration sessions on the Webex Teams platform. They manage production, critical issues, and performance improvements are becoming a bigger part of the industrial ecosystem. The industry has entered one of the most important challenges of its path: changing the perspective in the innovation of the man-machine relationship. From a long and rich phase in which innovation has tried to make the "language of machines" more and more effective, the prospect of increasingly seamless integration between robots and operators have begun to "tear down" the walls that still separate the language of man from the language of machines. We are currently faced with the creation of Cobots and by implication scenarios that improve man-machine collaborations with due respect for Natural language. These articles give more context to Cobots and how they’re being used in different varying environments: and . You’ve Heard Of Robots; What Are Cobots (Forbes) COBOTS IN THE CLASSROOM: HOW THE UR ACADEMY UPSKILLS STUDENTS AND INDUSTRY PROFESSIONAL (Universal Robots) This is an innovation plan that has played a key role in the ecosystem, of multidisciplinary innovation, the ability to manage all the levels that contribute to the development of solutions that are capable of being both innovative and stable at the same time. The opportunity is provided by Cisco's ecosystem work for Industry 4.0 and Enterprise 4.0. In this case, this path sees the involvement of one of the most important manufacturers of robotics and automation systems such as Fanuc and the system integrator Alascom, who added a commitment to technical consultancy services in the ICT sector and in the networks of TLC and IP technologies. In the development of solutions on the Internet, predictive maintenance associated with the IoT. Industry4.0 and collaborative design: is your company traditional, modern, or super smart? In this regard, I suggest reading the . It combines cloud, network intelligence, and advanced learning to manage predictive maintenance, reduce energy consumption and extend the average life of machinery. article on the collaboration between Cisco and Fanuc Realization of this collaborative logic is real and concrete (and has also been the subject of demonstrations within the Digital District of the latest edition of Sps Italia in Parma) and is based on three major components: the collaborative robot Fanuc CR-7iA / L the Alascom application of natural language processing based on ML neurolinguistic programming a series of Cisco solutions 3a) Webex Teams collaboration platform (evolution of Cisco Spark) 3b) cybersecurity solutions 3c) industrial security solutions 3d) Industry network management solutions 3e) solutions for data management Natural collaboration between machines, robots, and people The ecosystem is the term that best identifies the value of these projects. The goal of this partnership is the possibility of creating a collaboration that can be brought to production plants, to companies that make Industry 4.0. natural "between machines, robots, and people.” “The Cisco Webex Teams collaboration platform - he explains - also opens up to machines with the same collaboration method similar to the one that applies to people. Specifically, the collaborative robot Fanuc CR-7iA / L communicates with the operators, understanding the natural language of man and responding and providing valuable information, on its functioning, on its "state of health", on workloads, and on the quality itself.always in the natural language of the operator". "The Fanuc collaborative robot acquires a new level of cooperation with operators thanks to the development of applications that run on the Cisco Webex Teams infrastructure." In this way, the technological evolution of production environments takes advantage of a special acceleration, enabling value-added services and reducing commissioning times. Image Credits https://dribbble.com/shots/4675483-Cobot-Operation-with-VR-Headset Digitization and IoT reduce production downtime Articles from this series: Manufacturing Robotization: Drivers and Barriers Robotic Welding Process: Welding Applications, Systems and Techniques

About Cisco, Alascom and Fanuc's Progress on Building Collaborative Cobots 

Illustration created with NightCafe As we’ve followed the fascinating twists and turns of numerous works of fiction, whose plots are in one way or another connected with robots, how many of us thought about the fact that we are already living in the very "robotic future" that is often presented in science fiction books, movies, and TV series? It's not as if someone drew a line and said: "By crossing this line, we are stepping into a new era." It's pretty obvious that even the fastest adoption of technology is still gradual. And that's fortunate, as it gives us the opportunity to adapt to the major socio-economic changes that are associated with technological advances. And there is no doubt that robotization entails these major changes. Let's take a look at the current level of robotics development and implementation. Statistics, as usual, will most accurately present the current state of affairs. And the most eloquent statistics of all are financial statistics. And it says that in 2022, the volume of the robotics market amounted to $34 billion. A rather modest, in our opinion, forecast says that by 2028, the market will grow to $45 billion a year. Interestingly, the service robot market is developing most actively. In 2022, it will account for 76% of total market share. This, of course, does not mean that industrial robots have ceased to be of interest. It's just that businesses realized their benefits and the opportunities they provide much earlier. In addition, the previous generation of robotics technology was simply unable to bring even marginally satisfactory models of service robots to market. Today, the state of affairs has changed. Several service and collaborative robots (cobots) have emerged from their diapers and begun to perform their duties in a wide variety of spheres. We are sure that among the readers of this text, there are those who, at home or in the office, use a robot vacuum cleaner and/or robotic carts to deliver various items. But these are by no means the most interesting examples of robotics technology. They are already being replaced by more advanced examples. An example of the next iteration is the Aeo service robot, which is capable of performing various actions using its manipulators. Another is Optimus, a series of humanoid robots from Tesla. It is expected that in the very near future - by 2025 - the market for cobots alone (i.e., robots that interact with people in the workspace) will exceed $12 billion. The current generation of service robots and cobots are already capable of taking over a number of routine, low-skill tasks. For example, they can perform the functions of nurses, security personnel, cleaners, porters, packers, waiters, pickers, "serve and fetch" assistants, and so on and so forth. As a rule, they are equipped with AI that is smart enough to execute voice commands that are given in any form. A separate topic is robotic transportation. According to forecasts, by 2030, 10% of all vehicles on the planet will be self-driving. And this figure is also rather modest in our opinion, and is not because of a lack of technical capability, but instead will result from a combination of price and psychological factors. As far as industrial robots are concerned, today, the creation of fully automated plants and factories is in full swing. In developed countries like South Korea, about 1,000 robots per 10,000 workers are being used in the manufacturing sector. This is a very approximate figure, but in this case, it is not the exact ratio of people to robots that matters. The salient issue is the complexity of these industrial robots. The idea of the factory of the future as an analog of traditional  "human-powered" production facilities in which human beings have been replaced by humanoid robots is fundamentally wrong. Anyone who has been to a robotized production facility knows that an ordinary industrial robot is, in fact, an automatic machine tool. Or an entire assembly line. You will agree that this kind of production unit cannot be considered simply as an improved (from the point of view of factory production) analog of a human being. Each such robot replaces entire teams and workshops. Illustration created with NightCafe The logical development of this concept is the creation of fully automated production facilities. In other words, plants, factories, and other production facilities will each, in fact, be a large and incredibly complex robot. By the way, there is a place for humanoid robots in these production environments, too. However, they should not be considered industrial robots; instead, they should be identified as a new level of service and collaborative robots designed to interact with other robots and maintain their workspaces. What will it look like? Note that we consider most predictions for robotics adoption and development that are statistically based to be too conservative. Of course, solid researchers simply must proceed with caution. But we will allow ourselves to draw on a history in which technological progress has often raced ahead of predictions based on statistical analysis. Think of the rapid evolution of cell phones into smartphones or the first personal computers into light, sleek laptops. So, let's try to imagine a robotic life ten years from now based on a similar rapidity in development. Industrial and factory production will be fully roboticized. Mass-produced goods will be produced by robotic factories. Service robots and cobots will maintain and repair these production facilities; self-directed transportation will deliver raw materials to them and export their finished products. Agriculture will also become extremely automated. This is particularly true with the development of technologies for growing meat products. Traditional livestock farms will mostly disappear, which, it should be noted, will have a positive impact on the environment. Service robots will take over most of the work in commerce and the service industry, which hasn’t transitioned into virtual reality. They will also take care of household chores, repairs, construction, cleaning, street beautification, and the like. Freight transportation and public transport will be completely taken over by self-driving trains, planes, airships, copters, buses, cabs, air cabs, and so on. The share of self-driven private transportation will gradually increase. This trend will be accelerated under the pressure of increasingly stringent safety requirements for drivers. All of the above, combined with the development of artificial intelligence and augmented reality, will rid people almost completely of the work being performed currently that is strenuous, boring, monotonous, or poses a risk to health (or is simply dangerous). In other words, people are not going to have to do most of the work that people have to do now. But what will mankind do? At least that part of it that will enjoy civilization’s benefits? This is the final chapter of our future fantasy. Robert McCall – Metropolis 2050 #4 The article was created in collaboration with Andriy Tkachenko Illustration created with NightCafe Illustration created with NightCafe Illustration created with NightCafe As we’ve followed the fascinating twists and turns of numerous works of fiction, whose plots are in one way or another connected with robots, how many of us thought about the fact that we are already living in the very "robotic future" that is often presented in science fiction books, movies, and TV series? It's not as if someone drew a line and said: "By crossing this line, we are stepping into a new era." It's pretty obvious that even the fastest adoption of technology is still gradual. And that's fortunate, as it gives us the opportunity to adapt to the major socio-economic changes that are associated with technological advances. And there is no doubt that robotization entails these major changes. Let's take a look at the current level of robotics development and implementation. Statistics, as usual, will most accurately present the current state of affairs. And the most eloquent statistics of all are financial statistics. And it says that in 2022, the volume of the robotics market amounted to $34 billion . A rather modest, in our opinion, forecast says that by 2028, the market will grow to $45 billion a year. $34 billion $34 billion Interestingly, the service robot market is developing most actively. In 2022, it will account for 76% of total market share . This, of course, does not mean that industrial robots have ceased to be of interest. It's just that businesses realized their benefits and the opportunities they provide much earlier. In addition, the previous generation of robotics technology was simply unable to bring even marginally satisfactory models of service robots to market. 76% of total market share 76% of total market share Today, the state of affairs has changed. Several service and collaborative robots (cobots) have emerged from their diapers and begun to perform their duties in a wide variety of spheres. We are sure that among the readers of this text, there are those who, at home or in the office, use a robot vacuum cleaner and/or robotic carts to deliver various items. But these are by no means the most interesting examples of robotics technology. They are already being replaced by more advanced examples. An example of the next iteration is the Aeo service robot, which is capable of performing various actions using its manipulators. Another is Optimus, a series of humanoid robots from Tesla . It is expected that in the very near future - by 2025 - the market for cobots alone (i.e., robots that interact with people in the workspace) will exceed $12 billion . Aeo Aeo Tesla Tesla $12 billion $12 billion The current generation of service robots and cobots are already capable of taking over a number of routine, low-skill tasks. For example, they can perform the functions of nurses, security personnel, cleaners, porters, packers, waiters, pickers, "serve and fetch" assistants, and so on and so forth. As a rule, they are equipped with AI that is smart enough to execute voice commands that are given in any form. A separate topic is robotic transportation. According to forecasts, by 2030, 10% of all vehicles on the planet will be self-driving. And this figure is also rather modest in our opinion, and is not because of a lack of technical capability, but instead will result from a combination of price and psychological factors. 10% of all vehicles 10% of all vehicles As far as industrial robots are concerned, today, the creation of fully automated plants and factories is in full swing. In developed countries like South Korea, about 1,000 robots per 10,000 workers are being used in the manufacturing sector. This is a very approximate figure, but in this case, it is not the exact ratio of people to robots that matters. The salient issue is the complexity of these industrial robots. The idea of the factory of the future as an analog of traditional  "human-powered" production facilities in which human beings have been replaced by humanoid robots is fundamentally wrong. Anyone who has been to a robotized production facility knows that an ordinary industrial robot is, in fact, an automatic machine tool. Or an entire assembly line. You will agree that this kind of production unit cannot be considered simply as an improved (from the point of view of factory production) analog of a human being. Each such robot replaces entire teams and workshops. about 1,000 robots per 10,000 workers about 1,000 robots per 10,000 workers Illustration created with NightCafe Illustration created with NightCafe The logical development of this concept is the creation of fully automated production facilities. In other words, plants, factories, and other production facilities will each, in fact, be a large and incredibly complex robot. By the way, there is a place for humanoid robots in these production environments, too. However, they should not be considered industrial robots; instead, they should be identified as a new level of service and collaborative robots designed to interact with other robots and maintain their workspaces. What will it look like? Note that we consider most predictions for robotics adoption and development that are statistically based to be too conservative. Of course, solid researchers simply must proceed with caution. But we will allow ourselves to draw on a history in which technological progress has often raced ahead of predictions based on statistical analysis. Think of the rapid evolution of cell phones into smartphones or the first personal computers into light, sleek laptops. So, let's try to imagine a robotic life ten years from now based on a similar rapidity in development. Industrial and factory production will be fully roboticized. Mass-produced goods will be produced by robotic factories. Service robots and cobots will maintain and repair these production facilities; self-directed transportation will deliver raw materials to them and export their finished products. Agriculture will also become extremely automated. This is particularly true with the development of technologies for growing meat products. Traditional livestock farms will mostly disappear, which, it should be noted, will have a positive impact on the environment. Service robots will take over most of the work in commerce and the service industry, which hasn’t transitioned into virtual reality. They will also take care of household chores, repairs, construction, cleaning, street beautification, and the like. Freight transportation and public transport will be completely taken over by self-driving trains, planes, airships, copters, buses, cabs, air cabs, and so on. The share of self-driven private transportation will gradually increase. This trend will be accelerated under the pressure of increasingly stringent safety requirements for drivers. All of the above, combined with the development of artificial intelligence and augmented reality, will rid people almost completely of the work being performed currently that is strenuous, boring, monotonous, or poses a risk to health (or is simply dangerous). In other words, people are not going to have to do most of the work that people have to do now. But what will mankind do? At least that part of it that will enjoy civilization’s benefits? This is the final chapter of our future fantasy. Robert McCall – Metropolis 2050 #4 Robert McCall – Metropolis 2050 #4 The article was created in collaboration with Andriy Tkachenko The article was created in collaboration with Andriy Tkachenko

What Will the Spring of Technology Bring? Part 3: Robotic Life

Industrial robots have been helping manufacture products and facilitate logistics for more than 60 years. Traditionally, these robots are very good at a specific function — for instance, going from A to B. Throw an obstacle into their work environment however — be it a physical object or a human collaborator — and severe safety hazards arise. Technology is progressing such that today’s industrial robots are being designed to be “adaptive,” meaning that they’re ​​capable of adapting to and learning from their environment, including human interactions. The big vision is that humans and automated robots will be able to interact with one another on a machine shop floor, reducing the risk involved with having humans work side by side with giant machines. In Europe, industrial robots are becoming more commonplace. Data published in 2021 from The International Federation of Robotics showed European industrial robot installations up 24% compared to 2020. And while Europe still trails the United States and China in terms of industrial robot use, the growth of EU industrial production — up 8% in 2021 and another 5% in 2022 — could drive robot adoption in factories. In order to better ensure that the industrial robots of the future can readily adapt to changing environments, while maintaining safe and trustworthy collaboration with humans, a group of universities, tech accelerators and private research labs from the European Union (EU) have established RoboSAPIENS. The project, which is funded by the European Union’s Horizon Europe 2021-2027 research and innovation programme, will develop and implement safety mechanisms that will empower robots to — at a high level — safely adapt to unexpected changes. In an interview with TheNextWeb’s Ioanna Lykiardopoulou, consortium partner Ana Cavalcanti, a professor at the University of York, said that the project aims to include a trustworthiness checker in the current Monitor-Analyze-Plan-Execute-Knowledge (MAPE-K) framework for self adaptive systems. “What is missing here is the inclusion in the loop of a step that verifies whether certified guarantees, of safety or trustworthiness, are kept when plans change,” Cavalcanti told TheNextWeb. By adding this step, researchers hope that if a robot encounters an obstacle, it will consult if its originally programmed plan is still valid with the introduction of the obstacle. If it is not, the system will self update and a “trustworthiness checker” will validate the changes for safety issues. The researchers will use a combination of Deep Learning (DL) and Digital Twin (DT) — or a virtual representation of a physical object — techniques to generate the new control settings. “As the EU advances its industrial robotics capabilities, it’s imperative that measures be taken to ensure the safety and trustworthiness of the next generation of robots working alongside humans in a collaborative environment,” said Peter Gorm Larsen, a Professor in the Department of Electrical and Computer Engineering at Aarhus University, and the coordinator of the RoboSAPIENS project. “With RoboSAPIENS, we will prepare Europe’s industrial robotics industry for rapid and unforeseen adaptability in system structure and environment, assuring trustworthy collaboration with humans by changing behavior while maintaining and even enhancing performance and safety,” he added. According to a statement from the consortium, the project is already working on industrial use studies. The first empowers robots to disassemble electronic consumer waste, another aims to improve navigation predictability of autonomous water vessels. A third looks to help Autonomous Mobile Robots (AMRs) adapt to anomalies on machine shop floors, and a fourth will automatically assess risks in human-robot collaboration (cobot) situations. Consortium partners include Denmark’s Aarhus University; Aristotle University of Thessaloniki, AUTH (Greece); Danish Technological Institute, DTI (Denmark); Fraunhofer IFF, Fraunhofer (Germany); University of York, UoY (United Kingdom); University of Antwerp, UA (Belgium); Norwegian University of Science and Technology, NTNU (Norway); PAL Robotics, PAL (Spain); ISDI Accelerator, ISDI (Spain); Simula Research Lab, SRL (Norway). The RoboSAPIENS consortium isn’t the only entity working on marketing self adaptive robots safer and more efficient. Earlier this year, Google’s Deep Mind announced a series of releases related to industrial robots, including Self-Adaptive Robust Attention for Robotics Transformers (SARA-RT) which helps robots learn and perform new tasks at more efficient speeds. And last year, a team of Japanese researchers developed a learning framework to train robots to “sense” their surrounding environment, making it safer for human-robot collaboration. Industrial robots have been helping manufacture products and facilitate logistics for more than 60 years. Traditionally, these robots are very good at a specific function — for instance, going from A to B. Throw an obstacle into their work environment however — be it a physical object or a human collaborator — and severe safety hazards arise. Technology is progressing such that today’s industrial robots are being designed to be “adaptive,” meaning that they’re ​​capable of adapting to and learning from their environment, including human interactions. The big vision is that humans and automated robots will be able to interact with one another on a machine shop floor, reducing the risk involved with having humans work side by side with giant machines. In Europe, industrial robots are becoming more commonplace. Data published in 2021 from The International Federation of Robotics showed European industrial robot installations up 24% compared to 2020. And while Europe still trails the United States and China in terms of industrial robot use, the growth of EU industrial production — up 8% in 2021 and another 5% in 2022 — could drive robot adoption in factories. published growth of EU industrial production In order to better ensure that the industrial robots of the future can readily adapt to changing environments, while maintaining safe and trustworthy collaboration with humans, a group of universities, tech accelerators and private research labs from the European Union (EU) have established RoboSAPIENS . RoboSAPIENS The project, which is funded by the European Union’s Horizon Europe 2021-2027 research and innovation programme, will develop and implement safety mechanisms that will empower robots to — at a high level — safely adapt to unexpected changes. In an interview with TheNextWeb ’s Ioanna Lykiardopoulou, consortium partner Ana Cavalcanti, a professor at the University of York, said that the project aims to include a trustworthiness checker in the current Monitor-Analyze-Plan-Execute-Knowledge (MAPE-K) framework for self adaptive systems. interview TheNextWeb “What is missing here is the inclusion in the loop of a step that verifies whether certified guarantees, of safety or trustworthiness, are kept when plans change,” Cavalcanti told TheNextWeb . TheNextWeb By adding this step, researchers hope that if a robot encounters an obstacle, it will consult if its originally programmed plan is still valid with the introduction of the obstacle. If it is not, the system will self update and a “trustworthiness checker” will validate the changes for safety issues. The researchers will use a combination of Deep Learning (DL) and Digital Twin (DT) — or a virtual representation of a physical object — techniques to generate the new control settings. “As the EU advances its industrial robotics capabilities, it’s imperative that measures be taken to ensure the safety and trustworthiness of the next generation of robots working alongside humans in a collaborative environment,” said Peter Gorm Larsen, a Professor in the Department of Electrical and Computer Engineering at Aarhus University, and the coordinator of the RoboSAPIENS project. “With RoboSAPIENS, we will prepare Europe’s industrial robotics industry for rapid and unforeseen adaptability in system structure and environment, assuring trustworthy collaboration with humans by changing behavior while maintaining and even enhancing performance and safety,” he added. According to a statement from the consortium, the project is already working on industrial use studies. The first empowers robots to disassemble electronic consumer waste, another aims to improve navigation predictability of autonomous water vessels. A third looks to help Autonomous Mobile Robots (AMRs) adapt to anomalies on machine shop floors, and a fourth will automatically assess risks in human-robot collaboration (cobot) situations. Consortium partners include Denmark’s Aarhus University; Aristotle University of Thessaloniki, AUTH (Greece); Danish Technological Institute, DTI (Denmark); Fraunhofer IFF, Fraunhofer (Germany); University of York, UoY (United Kingdom); University of Antwerp, UA (Belgium); Norwegian University of Science and Technology, NTNU (Norway); PAL Robotics, PAL (Spain); ISDI Accelerator, ISDI (Spain); Simula Research Lab, SRL (Norway). The RoboSAPIENS consortium isn’t the only entity working on marketing self adaptive robots safer and more efficient. Earlier this year, Google’s Deep Mind announced a series of releases related to industrial robots, including Self-Adaptive Robust Attention for Robotics Transformers (SARA-RT) which helps robots learn and perform new tasks at more efficient speeds. And last year, a team of Japanese researchers developed a learning framework to train robots to “sense” their surrounding environment, making it safer for human-robot collaboration. announced developed

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