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Internet of Things April 2020 Viewpoints

Technology Analyst: David Strachan-Olson

Autonomous Mobile Sensing Robots

Why is this topic significant?

Advances in autonomous robots are enabling a revolution in mobile sensing platforms, which could have a major impact on the development of the Internet of Things.

Description

"Internet of Cameras" in the August 2018 Viewpoints discusses how advances in machine learning are revolutionizing computer-vision systems and how computer-vision systems could diminish the need for physical sensors in some applications. Advances in autonomous robots are enabling engineers to combine mobile robots and sensing suites—including computer vision, which could further expand the concept of a computer-vision-centric approach to the Internet of Things (IoT).

In the field of robotics, flying-drone technology has been one of the fastest-progressing areas in recent years. The price of enterprise quadcopters continues to fall, and changes in regulations are starting to allow drones to operate without the need of a pilot within direct line of sight. Companies are using drones for numerous purposes—many of which involve equipping drones with sensing suites to collect information about the environment or specific objects. Example applications include infrastructure inspection, construction-site surveying, and agricultural monitoring.

Ground robots provide another platform on which companies can create mobile sensing systems. Wheeled robots continue to see improvements in autonomous-path planning and navigation but still struggle in unstructured environments. Companies are applying wheeled robots to indoor sensing applications such as shelf scanning in retail stores. Several companies are now developing quadruped robots that can navigate difficult terrain autonomously. Example robots include Boston Dynamics's Spot and Anybotics's Anymal C. Companies are equipping such robots with various sensors and testing them in a variety of application spaces—such as construction sites, sewers, and remote ocean platforms—for inspection applications.

Many companies are developing autonomous vehicles for personal vehicle ownership, ride sharing, and logistics applications, but autonomous vehicles could also act as urban sensing platforms. Autonomous vehicles require various sensors, including cameras, lidar units, and radar units. Potentially, companies could leverage these sensors to collect and process information about urban environments for other applications and services.

Implications

As researchers continue to improve the autonomous capabilities of robots, companies are likely to leverage robots for more data-collection tasks. Similar to stationary computer-vision systems, mobile robots could prevent the need for permanent IoT sensor installations for some applications. Many IoT applications require an IoT device attached to a piece of equipment or placed in the environment to enable constant monitoring and control. Many other IoT applications—in particular, sensing over large environments—could use mobile robots that collect data about the environment intermittently. For example, a robot that moves through an orchard to collection information about the health of trees could be much cheaper and potentially just as effective as sensors attached to each tree's base.

Impacts/Disruptions

In the future, autonomous mobile robots could provide greater roles in IoT networks. Potentially, robots could act as data dump trucks and network-communication nodes. For environment-sensing projects over large areas or for projects in remote regions, companies might find it cost-effective to use robots and ultra-low-power sensors with short-range communications. Each sensor would be disconnected from a network and wake up periodically to sample the environment. At set intervals, a robot could traverse the environment, wake each sensor, and transfer the data from the sensor to the robot. Later, the robot would upload the data to a cloud data platform.

Scale of Impact

  • Low
  • Medium
  • High
The scale of impact for this topic is: High

Time of Impact

  • Now
  • 5 Years
  • 10 Years
  • 15 Years
The time of impact for this topic is: 5 Years to 10 Years

Opportunities in the following industry areas:

Drones, mobile robots, autonomous vehicles, autonomous systems, computer vision, lidar, radar, industrial inspection, environmental sensing, smart cities

Relevant to the following Explorer Technology Areas:

Key Areas to Monitor

Why is this topic significant?

Stakeholders should monitor key issues and uncertainties that could have an outsize impact on how companies commercialize IoT technology. Important areas to monitor are below.

Digital Transformation and Disruption

Digital transformation is a fundamental change in how a business operates caused by using digital technology. With the Internet of Things (IoT), many companies that once sold physical products only are now transforming into companies that also sell digital services. Digital transformation can be a large investment for a company—in particular, investing in not only the technology necessary for achieving the transformation but also the resources necessary to recruit or retrain staff who are able to embrace the new operations. The extent to which individual industries can undergo a digital transformation varies considerably. However, companies that embrace digital transformation, where possible, can gain a significant first-mover advantage over competitors.

What to Watch For:

  • Emergence of turnkey technologies that can enable new applications in particular industries
  • Signs of competing companies' embracing digital technologies or rolling out new digital platforms, products, and services
  • Partnerships between existing industry players and tech companies
  • Competition from unexpected places—particularly new entry by tech-based companies into markets for physical products.

Advanced AI

The Internet of Things promises to increase dramatically the amount of data generated and gathered by IoT devices. Prolific data gathering and advances in artificial intelligence and machine-learning technology could work synergistically and enable unprecedented results. For example, companies are already beginning to implement predictive maintenance, whereby replacing failing components before they fail prevents potential malfunctions in equipment. With advanced AI's ability to draw insights from a diverse multitude of data, a result akin to a predictive industry could emerge, in which raw-material ordering, logistics, manufacturing, and even sales and marketing are handled by algorithms rather than people. On application for the consumer Internet of Things—advanced AI—is a critical component in realizing the ultimate IoT dream of having a pervasive technology that integrates into people's lives seamlessly, automatically adjusting the environment, ordering food, and selecting entertainment by predicting a user's needs and desires.

What to Watch For:

  • AI and machine-learning technologies embedded in IoT devices
  • Cloud-based AI tools that make it easy for nonexperts to leverage and implement AI techniques
  • Digital assistants that respond to users proactively
  • AI that builds interactions between various services and systems automatically.

Mesh Networks

Mesh networks adopt an approach to transmitting data different from that of cellular and Wi-Fi networks. Instead of solely communicating with a central hub (say, the nearest cell tower or Wi-Fi router), devices incorporate relay nodes, enabling data to travel via multiple hops. Provided sufficient relaying devices are available, this ability can increase the range of wireless telecoms greatly, creating a robust network of local nodes. Mesh networks turn devices into part of the network infrastructure, potentially enabling self-forming local mesh networks even when no centralized communication access is available. This capacity could be extremely useful in remote areas or as part of disaster recovery. Ultimately, turning devices into relay nodes could reduce dramatically the burden of providing comprehensive network coverage from hubs such as cell towers.

What to Watch For:

  • Use of mesh networks for emergency communications after natural disasters
  • Device makers' incorporating peer-to-peer and relay capabilities into smartphones and similar devices
  • Successful trials of mesh networks in urban areas as part of 5G.

Scale of Impact

  • Low
  • Medium
  • High
The scale of impact for this topic is: Medium to High

Time of Impact

  • Now
  • 5 Years
  • 10 Years
  • 15 Years
The time of impact for this topic is: Now to 10 Years

Opportunities in the following industry areas:

Digital goods and services, hardware as a service, artificial intelligence, machine learning, mobile devices, networked sensors

Relevant to the following Explorer Technology Areas: