Dr. Jeff Smith is the Managing Partner of QuantumIOT an IoT systems integrator.
In this blog series, I will address some of the state-of-the-art technologies and tools that are enabling Industry 4.0 as it applies to Advanced Manufacturing, Production, and Supply Chain. There are many underlying components, and the focus will be on Autonomous Guided Vehicles and Cooperative Robots.
Five years ago, in early 2016, I consulted for Brain Corp. The name of the company is warranted. Going there reminded me of the quote attributed to Will Rogers, Dizzy Dean, and others. “It ain’t braggin’ if you done it.” Brain Corp. built an Autonomous Mobile Robot (AMR) that cleaned floors. The product is now commercial. That experience provided insight into deployed and emerging technologies and new business models that apply to manufacturing, supply chain, and service delivery.
The floor cleaning AMR needed to work along with people in a retail environment – think Wal-Mart. It needed to be safe and non-intrusive to retail customers and the retail employees. Additionally, the AMR was required to provide an excellent ROI and have minimal upfront risk to the customer. The AMR was designed to go up and down isles; what else could it do to increase the ROI?
The question became, can we provide autonomous robots as a floor cleaning and inventory management service at a rate lower than current wages? How can it work and adapt in a dynamic environment? What cost-effective way could it be serviced or manually controlled in an unusual situation? It was determined that offshore manual control could answer highly unpredictable environments and automatic software service updates.
In 2012 in Nevada, Google tested a Prius with self-driving capabilities on city streets. I love this feature in my Tesla Model 3. The car will drive on I-30 for 70 of the 100 miles from my farm/robotics lab in East Texas to Dallas – I mostly pay attention.
Adaptive autonomous manufacturing systems that work cooperatively in the same space as humans are in rapid adoption. Industry 4.0 is reconfigurable, adaptive, flexible, predictive, autonomous, and based in the cloud, fog, edge, and embedded. It is low-risk, regulatory compliant, standardized, open-source, and services-based.
Humans are increasingly surrounded by robots that sense a changing environment of people and things and then move and react accordingly – self-driving cars, driverless taxis, Autonomous Mobile Robots on sidewalks delivering pizza. I call these, Awareness Applications.
Awareness Applications are consumer products that incorporate new technology, are incredibly innovative, and may not be necessarily mass-marketed (but in many cases are). They are the innovations that become talk at the dinner party and water cooler, particularly people who want to be perceived as leading edge. Innovations such as drones, Ipods, Tivo, DVD, VHS, digital camera, Roomba, Palm Pilot, Smart Phone, TomTom, flat-screen, Wii, VR – Oculus, and Tesla. Underlying each of these innovations is a breakthrough technology. When the technology is exploited across many products it gets widely adopted and embedded in many products and goes unnoticed. It becomes a ‘component’ for many new innovations. Deep learning is one of the latest examples. At first, you see it in Google photos for facial recognition, then security, and now in commercial large farm agricultural weed identification and killing products. Who knew the progression would take such a wild path?
All of these technologies are driving Industry 4.0. The Awareness Applications for high-resolution, low-cost servo motors and the systems to operate and control them are 3D printing and drones. The Awareness Application for high torque density motors is electric vehicles. Combine these, and you have Autonomous Guided Vehicles and Articulated Cooperative Robots. Both Autonomous Guided Vehicles and Cooperative Robots have many common requirements and challenges such as synchronization, speed, precision, accuracy, repeatability, and safety.
Underlying these requirements are contact and non-contact sensors such as lidar and capacitive skin, edge-based and cloud-based A.I., new processing platforms for real-time embedded machine learning, and new algorithms for adaptive and predictive systems. Kalman filters are used to “fuse” the sensor data for intelligent prediction and adaptive control.
Let us take a closer look at a single actuator and then build up to a more complex 5G driven, Industrial Internet of Things (IIoT) system for Smart Manufacturing. A single actuator may have a built-in encoder, embedded microprocessor controller, and communications to a device controller, then to a workspace controller, then to a factory controller. As we get more complex, an embedded controller with an actuator and sensor may communicate with other actuators or directly to the cloud. In Autonomous Guided vehicles, a precision GPS sensor receives ephemeral satellite data from the cloud to enhance its position estimation, or an actuator controller downloads “crowdsourced” tuning parameters based on wear data from other actuators in the field.
The applications, enabling technologies, and components introduced here are the basis of Industry 4.0. Two recent developments under rapid adoption are Autonomous Guided Vehicles and Cooperative Robots. I will use these as a framework for future blog posts in this series. I will dive deeper into the details of the underlying technologies as well as the availability of tools and libraries to exploit them. I will point you to development kits and online learning to raise your awareness. I will also describe some of the soon-to-be-released and recently released products that are incorporating these technologies. I solicit your feedback and direction for my own enlightenment.
Dr. Jeff Smith Bio:
Jeff is the Managing Partner of QuantumIOT an IoT systems integrator. Previously, Jeff was the Chief Technology Officer of Catapult Health. Jeff was also the Chief Technology and Strategy Officer of Numerex, a public company acquired by Sierra Wireless. He was the Founding Chair of the Telecommunications Industry Association (TIA) IOT Committee and the Convener of the Global IOT Standardization Task Force of the ITU/United Nations. Jeff is no stranger to emerging technologies. As a scientist for Motorola, The Robotics Institute, and the SuperCollider, he has worked on wireless sensor and control systems developed for the FBI, CIA, DOD, DOE, NASA, and USPS – from autonomous unmanned aerial vehicles to particle accelerators.
In 2002, Jeff founded SensorLogic, an IoT platform provider acquired by Gemalto, Ublip, an IoT platform provider acquired by Numerex, On-Ramp, one of the first commercial ISPs later acquired by Verio and several other technology companies. Jeff has served on the boards of Numerex, Winning Habits, CreditAnswers, Vericenter, Verio, and Catapult Health, 2020 Solutions, and The IoT Industrial advisory board of Interdigital. He is an advisor to Motus Labs and other IoT, A.I., and robotics companies.
Jeff has a B.S. in Engineering Science from Trinity University and an M.S. and Ph.D. in Computer Engineering (A.I. and Robotics) from the University of Texas at Arlington (UTA). He currently serves on several boards at UTA and is an adjunct professor. He was the project manager of the winning entry in the First International Autonomous Aerial Robotics Competition in 1988. Jeff has 22 patents and several pending. In 2006 Jeff received The International “Community” award from the Ewing B. Kaufmann Foundation. This award recognized his social entrepreneurship mission in Honduras.
Jeff is currently living in East Texas “off the grid” on a high-tech 35-acre sustainable farm with a 2000 SQFT robotics lab and private maker space. He has a bride of 25 years and about 300 animals, including his Australian Shepard Harley. He continues to do research and development in IoT, Autonomous Vehicles, and Ag Tech. Jeff is an avid speaker with a fun and entertaining style and has been called on internationally as a technology evangelist and pragmatic visionary.