New industrial robotics applications are enabled by improvements in artificial intelligence (AI), the potential of 5G wireless, the Internet of Things (IoT), and cloud computing. However, the next wave of industrial robotics also requires new skills and training for the humans who will design for and work with them.

The automotive sector has long pioneered industrial automation, but now the practice is expanding and flourishing in other areas. Improvements in enabling technologies such as computing, sensors, and machine vision—and some advances in materials—have helped collaborative and mobile robots take hold in industries such as logistics. That is where the growth opportunities exist. This subgroup of collaborative robots is changing the way people work with machines because they are designed to work alongside people. Furthermore, they tend to be much cheaper than traditional automation.

Where robots lead the way

The automotive industry is venturing into designing automation and human-assist technologies that depend on sensors and AI. Companies are spinning off centers, such as the Toyota Research Institute, to develop technologies in other areas such as human factors and self-driving cars (see “Case Study: Automating for Process”).

“Industrywide, the biggest excitement is about how self-driving car technology can be used in cars and trucks, airplanes and ships,” says Vijay Kumar, Ph.D., Nemirovsky Family Dean of Penn Engineering at the University of Pennsylvania. In the university’s GRASP Lab, Kumar and his students create autonomous ground and aerial robots and design bio-inspired algorithms for collective behaviors and robot swarms.

Kumar sees growth in data analytics systems in the logistics field, such as advances in sensing, communication, and storage technologies that enable acquiring and storing data “at unbelievable levels of resolution,” he says. He adds that advances in data production, AI system integration and availability of sensor data for input into computing systems “enable sensing, computing, storage, and communication with the ability to mine the data, understand it, and to learn from it.”

The future of the robotics space is not autonomous robots, per se, but robots which exert some degree of local autonomy while aided by a human.

While actual robots may not have changed that much in the past 10 years, Sean Murray, co-founder and director of robotics engineering, Realtime Robotics, a startup with the goal of expanding the potential of robotic automation, says there have been improvements in 3D sensors, machine learning algorithms to interpret data, as well as mobile robotic applications.

Murray adds that for anything that is difficult to deploy, there is a technology learning curve. Scale and scope matter.

“It only makes sense to invest in acquiring that expertise if you’re going to amortize that cost over a lot of factories or a lot of deployments,” Murray says. “While larger companies are early adopters, as automation technologies become easier to use, smaller and lower-volume industries can take advantage of innovations as well.”

“Industrial robotics prospers where you have the right intersection of volume and value,” says Erik Nieves, CEO and co-founder of Plus One Robotics, which develops 3D vision software and collaborative automation for the logistics industry. “And that’s why automotive is still the dominant market for industrial robots. Historically, when you have repeatability, robots win. It can’t just be a million widgets; it’s got to be a million widgets of substantive value.”

According to Nieves, the burgeoning online commerce and logistics industry is a prime area for industrial automation in the United States. “We are moving more of our commerce online,” he says. “Which means that supply chain has a growing labor problem because increasing order volumes and shorter delivery cycles require a huge amount of labor.”

The needs of the current supply chain industry harken back to the original impetus for the development of robots. “Remember the classic three D's of robotics? Work that’s dirty, dangerous, or degrading to the human spirit? In supply chain, there’s a fair amount of that,” Nieves says. “It’s not very dirty, but it is certainly dangerous from the standpoint of ergonomics. Nobody wants to move stuff from left to right, every two seconds for the next six to eight hours. That’s why the churn is so bad.”

Although robots excel at high-value tasks of low variability, a manufacturing environment usually still ends up with more people than robots. “That’s even true in automotive because you have repeatability and a constrained set of processes on the 'body in white' side of the wall,” Nieves says. “But when it crosses over to the final assembly, now you have a lot more variability in trim levels and options.”

However, once a job requires more cognitive complexity, robots can lose their edge. “Robots are great because they’ll pick up and sling 250 pounds all day long,” Nieves says. “But they’re dumber than a box of rocks. Anytime a robot needs to make a qualitative decision about its environment or circumstance, it breaks down. People are just better.”

Scope and Scale

  • The worldwide market value for robot systems in 2017 was estimated at $48 billion by the International Federation of Robotics.

  • The global collaborative robot market was valued at $420.4 million in 2017 and is projected to expand at the compound annual growth rate of 46.8% through 2025, according to a report by Research and Markets.

  • Worldwide shipments for warehousing and logistics robots will grow rapidly from 194,000 units in 2018 to 938,000 units annually by 2022, with the rate of growth slowing after 2021 as many major players will have adopted robotic systems. By 2022, there will be nearly 1 million total warehouse and logistic robots, according to Tractica.

  • Global revenue for cloud robotics, which combines cloud computing and robotics technologies, will increase from $5.3 billion in 2018 to $170.4 billion in 2025. The growth of cloud technology and integration with the IoT and AI, as well as the introduction of 5G connectivity, are expected to lead to strong growth in the cloud robotics market, according to Tractica.

  • Construction robots market revenue will increase from $22.7 million in 2018 to $226 million annually by 2025. The largest market in terms of unit shipments will be for robot assistants used on construction sites, followed by infrastructure robots, structure robots, and finishing robots, according to Tractica.

  • AI technology software, hardware, and services in smart manufacturing sector investment will increase from $2.9 billion in 2018 to $13.2 billion by 2025, according to Tractica.

  • 3D imaging sensors and hardware subsystems global market will grow from $8.2 billion in 2017 to $57.9 billion by 2025. Mobile and automotive application markets were the two largest segments in 2017 and are expected to continue to be the leading markets in 2025, according to Tractica.

The latest improvements

“Systems that have moved around warehouses have been around for a long time,” says Thomas Ryden, executive director of Mass Robotics, a nonprofit supporting the Massachusetts robotics community. Now, startups are developing flexible, intelligent systems that can quickly plan on the fly and gather data as they do, as well as fleets of robots that can navigate and communicate with each other to optimize performance.

“Instead of the robot going from point A to point B to point C, a system of collaborative robots can communicate and select the closest robot who has the capacity, who can get back to the pick stations fastest,” Ryden says. “And then, use cloud-based IoT and AI to optimize for mobile manipulation. So, think of adding traditional factory automation arms—but now on a mobile platform. You would want to avoid collisions with other robots and people in terms of the trajectory of the arm or arms.”

These collaborative robots can work with humans in the same workspace because the robots can detect human movement and then plan a dynamic path and execute tasks in close to real time by using field programmable gate arrays, a hardware-based solution. “And so, this whole idea is of computing at the edge—putting the processing right next to the arm with a combination of sensors and sensor fusion to be able to accurately position and reposition the arm so the path that it takes avoids all obstacles in the area,” Ryden says.

He also points out the new trend toward leveraging AI and other sensing capabilities to ease the programming load. In traditional factory automation, an engineer has to program a robot to carry out a specific task, but now this can be accomplished more flexibly through software.

Change Checklist

When incorporating a new robotics system, one should consider the payload of the robot, the work envelope of the robot, and the accuracy of the robot versus the task required. However, that is not all. The following questions provide a guide:

  • What stakeholders should be consulted in the planning process?
  • What technologies can be leveraged to make a better working environment?
  • Is the supply chain evolved enough to handle automation?
  • What is the potential return on investment?
  • How can different parts of the organization work together to integrate new robotics systems?
  • How can production lines be designed so humans can safely and comfortably work with robots?
  • What knowledge exists within the company to maintain robots, or what systems integrator expertise is available?
  • What new skills do employees need to design for or work alongside robots?
  • How can engineering, maintenance, and training departments work with the safety department to ensure the safe integration of robots?

Finally, learn about tools and templates that will help your organization assess whether you should leverage industrial automation with robotics.

Automated guided vehicle (AGV) technology has been around for about 25–30 years, according to Melonee Wise, CEO of Fetch Robotics, which develops robotics for logistics. The reason autonomous mobile robots have become so popular is that they offer flexibility and the ability to deploy on demand in a dynamic environment. Wise believes that technologies such as the cloud and potentially 5G—as well as new, low-cost sensors—will influence industrial robotics development. Expect to see autonomous mobile robots with arms on them, as the industry is already starting to prepare for this by developing standards.

Meanwhile, Nieves speculates that the future of the robotics space is not autonomous robots, per se, but robots which exert some degree of local autonomy while aided by a human. That human assistance is necessary for the edge cases—the unexpected events which fall outside of a robot’s programed capabilities. In these situations, a human must step into the process to make the decision.

“With this work model, edge computing happens at the work cell, but there’s still a human in the loop,” Nieves says. “And that human can be anywhere there is internet access.” Connectivity to the robot in the field has now become important, and while the tools and infrastructures exist that can connect these robots using cloud computing platforms, cybersecurity remains an issue.

Nieves believes that future industrial robots will work autonomously 80% of the time—a minimum number he believes is required to justify the return on investment. While one may not hear from a specific robot for hours, when it requires decision assistance, it will message a human to provide direction on the next course of action.

The logistics field needs further development of technologies in perception and grasping, as well as bilateral and mobile manipulation, according to Nieves. While manipulating a robotic arm is a straightforward process, other technologies under development include scene segmentation, 3D manipulation, decision-making, and the ability to iterate quickly on designs with additive manufacturing.

There is also a need for better simulation tools to train robots. “It used to be in the old, line-building days for automotive, the best you could do is actually get a physical robot, put your tool on it, program it, and see what cycle time you’ve achieved,” Nieves says. “We’ve gotten smarter about how to replicate these environments digitally. While many robot manufacturers have proprietary simulation tools, there are also third-party robot-agnostic simulation tools available. These third-party tools mean you can program any robot, but these simulation packages are often more expensive."

Case Study: Automating for Process

Over the past 10 years, robots in Toyota assembly plants have primarily performed tasks considered dangerous, such as welding, manipulating heavy parts, and handling sharp objects. But the company is increasingly leveraging collaborative robots, automated guided vehicles (AGVs) to move materials, and other robots to pick and position smaller parts. Collaborative robots are relatively low-powered, so they can work together with humans without compromising safety.

The bulk of the robots Toyota uses in the body, paint, and machining areas have powerful motors that move heavy items and are separated from workers. Collaborative robots are used in the final assembly area where there are more sensory-specific types of applications, such as installing hole plugs or clip-in plastic parts, particularly for tasks that are ergonomically strenuous for human workers.

According to Brian Eggleston, general manager of Toyota production engineering, the main technologies enabling faster, more precise and cost-effective robotics in the automotive sector in the last decade have been machine vision and computing. Robots have gotten faster and more accurate.

Toyota is now putting vision systems on the end of robots to validate part presence, dimension, and specifications as vehicles are built down the line. “Sensors have improved, and computers are faster, but the ability to process data has really been what’s kicked the door open and allowed us to use that type of sensing equipment,” Eggleston says. “Much of the same technology progress that the industry in general has made towards automatic driving cars we’re beginning to try to implement in the factory—probably not as fast as we would like.”

Toyota’s production engineers execute the major model change activity and refurbishment for its factories throughout the Americas. When a model change is occurring, they must design the new process. The first step is working with product designers to ensure the car they draw can be built, accounting for the factory that will build that vehicle and ensuring safety, quality, and productivity. Then it is time to execute, procuring new equipment and preparing the factory for mass production.

In Toyota’s case, innovative technologies in self-driving cars are now filtering back to the manufacturing line. The company uses AGVs in its factories to move material from receiving areas to the installation point on the line. “The advantage we have is the fact that it is a much more controlled environment than roads,” Eggleston says.

In the past, humans spent days teaching the machines when transitioning from one car model to the next year’s version. Now, the machine can teach itself based on what it knew it did on the previous version of that car. The machine can check if the vehicle has the right color, key, and door handle based on the build specification for that specific vehicle.

“Our quality goal is built to be robust enough so we can catch issues inside the factory and not have them make it to our final customer,” Eggleston says. “We can put vision on the end of a robot that is performing another task, such as applying an adhesive. And we can get very detailed with the quantity and shape of the adhesive application to ensure quality. Really, all of these things are just improving our ability to provide consistency.”

For engineers, problem-solving skills are always in demand. “The advances in vision technology are allowing robots to be inserted into some of these areas of higher variability of tasks,” Eggleston says. “Our decision to use robots are based on business factors, the balance of labor and automation, on the pace of the factory; how much time that robot is going to be in use versus not.”

As Toyota works globally, the company needs to consider the skill sets available to maintain certain levels of technology in each country, such as the expertise available for maintenance of nuanced and sophisticated robotic systems, according to the region where a project is deployed.

Working relationships with robots

According to Murray, in deploying industrial automation, the first challenge is to allay workers’ concerns that they must deal with maintaining a piece of equipment that breaks frequently. “After six months, they uninstall the factory automation, and people are doing the job again,” he says. “And then on the management side, same thing. The challenge also is convincing them that you’re going to get them a return on their investment.”

Fetch Robotics emphasizes the user experience to help ease concerns, too, Wise says. “We started to mimic a lot of the interfaces that you see on the internet and web browsers, so that people feel very comfortable with using and configuring the robots,” she says.

Robots are great because they’ll pick up and sling 250 pounds all day long. But they’re dumber than a box of rocks. Anytime a robot needs to make a qualitative decision about its environment or circumstance, it breaks down. People are just better.
Erik Nieves Plus One Robotics

Her company also developed guidance, called “robot etiquette,” to help users interact with robots safely. “You should treat the robot like another co-worker,” Wise says. “You wouldn’t step out in front of a co-worker, jump in front of a co-worker, push a co-worker, or drag co-worker around.”

The other key is training the workforce at all levels, and getting employees to a place where they can operate these robots comfortably.

“Typically, the fear that workers have is not that they’re going to lose their job; it’s that they they’re not capable or competent to work with the robots.” Wise says. “You have to remember that many of the people that are working with our robots don’t have a lot of formal secondary education, and so they are constantly worried that they can’t keep up. That’s why we’ve invested so much effort into making the robots easy to use. A lot of what we built into the robot, like process engineering a warehouse, is really like visual programming. So, it’s a pretty easy-to-use way to configure what the robot has to do and how it’s supposed to do its job. But that depends on the person that’s using it.”

For engineers and developers, robotics is as much or more software development than pure-play engineering anymore, according to Nieves. He advises younger engineers to add software and programming language classes such as Python, C++, or Robot Operating System (an open source robotics framework) to their curriculum because so much of their future depends on being able to code.

However, Nieves also wants potential hires to have a good background in human factors. “It’s not enough to be a whiz-bang software developer and great at controls,” he says. “And you know, maybe you can even engineer the mechanicals. But if you don’t understand what the person who does this task experiences each and every day, then you’re not going to have sufficient empathy to engineer the best solution for the worker. And that’s the one thing I ask interview candidates here at Plus One: Can you really relate to the people who will be helped by our robots?”

Untapped Potential: Robots in Construction

The University of Pennsylvania’s Vijay Kumar projects that robotics will next transform the construction industry. Even though it is a labor-intensive industry, productivity and technology adoption have lagged in recent decades. “Construction is still stuck in the 20th century,” he says.

According to Kumar, there are two parts of information handling involved in automation. “One, you can loosely call data acquisition and going from data to information,” he says. “The second is really about manipulation and assembly. In the construction industry, a lot of that has already started to happen.”

He cites Komatsu Ltd., a Japanese multinational equipment manufacturer, as an example. “You should look at the way they plan out construction projects,” Kumar says. “They use, for example, overhead drones to get high-resolution imagery in order to build models for what needs to be done and monitor construction sites. They know exactly how many backhoes or excavators or cranes they need to dispatch.”

According to a 2017 report by the McKinsey Global Institute, productivity in construction, a $10 trillion global industry, has fallen since the 1960s. Labor shortages and waste are contributing factors. A 2018 survey conducted by Autodesk and the Associated General Contractors of America found that 80% of construction firms are having trouble finding skilled craft workers.

While current robotics applications are in their early pilot stages, some construction firms use autonomous software with preexisting machinery. Others are using manufacturing robotics and small self-driving vehicles for construction work.

Aside from cutting labor costs, robotics could make construction more efficient by shortening time between subtasks. Barriers to adoption include limitations on the capability of robots to adjust to complex sites and changing plans. There needs to be enough contractors and production-system players in this risk-averse industry to generate standardized products at lower price points. Companies can have a few construction projects a year, compared to automating a fixed location that is involved in large, high-value industrial output.

Several companies besides Komatsu are using drones to map sites, as well as self-driving robots to bring materials to sites. Built Robotics is a California-based startup that is developing software for earthmoving equipment, enabling equipment such as bulldozers and construction vehicles to operate themselves. New York-based Construction Robotics is deploying the robots SAM (semi-automated mason), a brick-laying robot, and MULE (material unit lift enhancer), a lift assist device designed for handling and placing material weighing up to 135 pounds on a construction site.

Written by Wendy Wolfson

Want to see the rest?

Sign up to see our Essential Guides

Here's what you get

ASME's Decision-Makers' Guides on Additive Manufacturing, Robotics, Bioengineering, and Clean Energy. These include:

  • Critical background and details on industry-changing technologies
  • Change Checklists that cover what your company should consider before adopting these technologies
  • Case studies that illustrate successful implementation
  • Stats and figures on each industry and what engineers can do to drive them forward into new areas

ASME Essential Guides