It’s the stuff of science fiction – robots that can build things, drive our cars for us, talk to us, and work longer, harder and smarter than humans. But it’s been the reality for several decades in industries such as oil and gas, mining, manufacturing, and logistics. While the robots and autonomous applications in these industries aren’t as outwardly thrilling as science fiction’s sentient androids, they are highly effective at reducing human costs by taking over mundane, physically taxing or dangerous tasks; streamlining business operations; improving efficiency; and ensuring extreme precision.
A report from the International Federation of Robotics estimates that by 2018, there will be 1.3 million industrial robots around the world. Greater use of autonomous vehicles and robots also will increase dramatically as the Internet of Things expands. According to an A.T. Kearney report titled “Divergence, Disruption and Innovation: Global Trends 2015-2025,” the more smart technology we add to everything around us, the faster machines will ascend – “a self-propelling phenomenon.” The report calls out several key factors that will play into greater use of robots, including “cheaper, faster and more capable sensors … more dependable, flexible and higher-bandwidth wireless networks … [and] greater safety and more efficient software.”
The need for ironclad network infrastructure especially is critical to ensuring autonomous vehicles and industrial robots operate flawlessly.
What Robots Need
Autonomous and robotic applications allow vehicles and equipment to operate independently of humans, using sensors, software, computers, and network interconnectivity to give the machine a more immersive existence in its environment.
Some applications may require a remote operator, such as those involving heavy industrial machinery, while others are completely human-free. But for any autonomous application, a robust network is required to ensure the applications and tasks run safely and seamlessly. A delay in transmitting data between autonomous vehicles or robots and a command center, or an unexpected interruption in communications, can disrupt operations. This is true even when the interruption is measured in seconds.
The final decision point is finding a network that has low latency (the time it takes data to get from point A to point B); high throughput ( the quantity of data going from point A to point B); and continuous connectivity (no disruption in data delivery). Operations leaders across multiple industries are increasingly implementing kinetic mesh wireless networks in the field to facilitate autonomous applications because the networks ensure these three requirements.
Why It Works
In a kinetic mesh wireless network, each node serves as singular infrastructure, which enables all devices and the network itself to be mobile. It employs multiple radio frequencies and any-node-to-any-node capabilities to continuously and instantly route data via the best available traffic path and frequency, using hundreds of nodes.
If a certain path becomes unavailable for any reason – due to antenna failure or power loss to a piece of equipment, for example – nodes on the network use an alternate route to deliver the data, eliminating any downtime.
The network never fails as a whole; data is sent and received simultaneously, in real time. Multiple hops do not add measurably to transport time. The networks operate with the same level of reliability even in the harshest conditions. There are no single points of failure, and the network can be redeployed in multiple ways simply and easily.
Because there is no central control node, routes are built automatically, and are evaluated for quality and performance 200 times per second. This artificial intelligence allows the network to adapt to node location, local interference, and congestion dynamically, despite conditions that would cripple other networks.
Kinetic mesh has been successfully used in multiple industry segments for mission-critical autonomous applications.
Oil & Gas
Kinetic mesh was deployed in a Texas oilfield to increase production and decrease failures of semi-autonomous down-hole pumps.
After an oil well is drilled, a down-hole pump brings the oil to the surface to be collected and processed. The well heads have data loggers, which workers use to set the pump speed. Setting a speed that is too fast risks the hole running dry and the pump burning up, so workers naturally operate wells on the conservative side.
A burned-up pump may cost thousands of dollars to replace, but the total repair and removal price can skyrocket into the hundreds of thousands of dollars.
Workers only visit the pumps every week or so, so pump speed is determined based on data that is outdated as soon as the worker leaves the field after each visit, creating production loss.
The oil company then set up a kinetic mesh network to connect the pumps and send all production data to a central office in real time, eliminating any need for a technician to go into the field to pull data from each individual well. Now, technicians in the central office are alerted immediately if there is a drop in production on any well. This allows the technicians to fine tune the pump speeds for optimal oil field production.
The pumps are not fully autonomous, as technicians still manually adjust speed as needed, but the kinetic mesh network has enabled remote operations of equipment as well as allowed the company to run the pumps at exactly the right speed based on the oil conditions of a given well, increasing profits and providing a positive return on investment within a matter of months.
For mining operations in remote locations where skilled drivers are a rare commodity, both autonomous and distant remote-controlled trucks are being deployed to mitigate labor shortages.
Huge driverless trucks are used to transport material around mining sites. They can run 24 hours a day and replace high-risk jobs in harsh working conditions and extreme temperatures. Safety is paramount for mining companies, and using these types of vehicles eliminates the possibility of a fatigued driver at the wheel of a massive truck.
Remotely-controlled trucks and equipment are operated by employees thousands of miles away. Reliable communications between the operator and machines must be maintained to ensure the vehicle safely navigates the mine and safeguards are not tripped, causing equipment stoppage.
Many automobile manufacturers have deployed autonomous vehicles for durability testing. In the past, actual humans would drive cars to the point of destruction. This type of testing is performed to accelerate the wear and tear on the car to determine reliability and parts failure timeframes. An unintended consequence was the wear and tear on the vehicles’ drivers.
Now, autonomous kits are placed on the car, allowing it to drive itself. The autonomous system must keep the car moving despite the extreme conditions in which the car is operating, so many manufacturers use kinetic mesh wireless networks to maintain continuous connectivity between the car and the researchers conducting the testing.
Virtually all large manufacturing facilities and materials processing utilize some form of control and automation functions. Kinetic mesh benefits industrial manufacturing by not only providing more reliable and consistent communications in the plant, but also by delivering many times the bandwidth available via legacy SCADA networks. This additional bandwidth is necessary to run applications such as video surveillance, access control, and even basic Wi-Fi connectivity for workers.
Plant operators require systems that quickly and reliably deliver control inputs to the machines they are running. If equipment does not stop in time, it could create colossal expenses for the plant due to expensive material being processed incorrectly, or it could even cost an employee his or her life.
Autonomous applications have a place in large-scale supply chains, such as large shipping ports that receive cargo ships. These ports move tens of thousands of shipping containers each day and much of their equipment is autonomous, including the cranes that move and stack the containers. It is critical to maintain reliable communication systems for this equipment to run efficiently and reliably.
When autonomous cranes are moving shipping containers around massive yards, they send data to a central office with the exact location where a specific container is being placed. If a crane attempts to send data on a container’s location and communication is lost for even a second, the message won’t be received, and there will be no record of the location. This effectively loses the containers within the yards – costing the freight company, the yard and the shipper time, effort and money.
While some think that robots are going to take jobs away from humans, there is data that actually shows the opposite may be true. The IFR found that one million industrial robots currently in operation have been directly responsible for the creation of about three million jobs in industries such as consumer electronics, food, solar and wind power, and advanced battery manufacturing.
Robots and autonomous applications are able to work in areas unsafe for humans, accomplish tasks that are impossible for humans, and perform jobs that are not economically viable in a high-wage economy. They also allow greater precision, let companies to get the most out of their equipment, and ensure efficient business operations. The ultimate goal is straight out of science fiction: letting robots do all the hard and mundane jobs, freeing humans up for higher-level work and more leisure time.
Industrial robots and autonomous applications are ushering in a new age of smart, connected machines, but they require highly reliable, mobile, resilient communications networks that run continuously and ensure data is never delayed or not received. Kinetic mesh networks have demonstrated their capabilities in multiple sectors, including autonomous use cases, and can be part of a future that improves human lives by putting robots to work.
Todd Rigby is director of business development for Rajant Corp. (www.rajant.com).
Edited by Ken Briodagh