Written by
Greg Nichols, Contributor
Greg Nichols
Contributor
Greg Nichols
Full Bio
Posted in Robotics
on January 10, 2022
| Topic: Robotics
Pixabay
Our own brains use all three of these techniques in concert to give us a rich understanding of the world around us that goes beyond simply building a 3D model.
GN: Why is there a better technological case for machine vision over LiDAR for many robotics applications?
Rand Voorhies: LiDAR is well suited for outdoor applications where there are a lot of unknowns and inconsistencies in terrain. That’s why it’s the best technology for self-driving cars. In indoor environments, machine vision makes the better technological case. As light photons are bouncing off objects within a warehouse, robots can easily get confused under the direction of LiDAR. They have a difficult time differentiating, for example, a box of inventory from a rack of inventory — both are just objects to them. When the robots are deep in the aisles of large warehouses, they often get lost because they can’t differentiate their landmarks. Then they have to be re-mapped.
By using machine vision combined with fiducial markers, our inVia Picker robots know exactly where they are at any point in time. They can “see” and differentiate their landmarks. Nearly all LiDAR-based warehouse/industrial robots require some fiducial markers to operate. Machine vision-based robots require more markers. The latter requires additional time and cost to deploy long rolls of stickers vs fewer individual stickers, but when you factor in the time and cost to perform regular LiDAR mapping, the balance swings far in the favor of pure vision. At the end of the day, 2D machine vision in warehouse settings is cheaper, easier, and more reliable than LiDAR.
If your use of robots does not require very high precision and reliability, then LiDAR may be sufficient. However, for systems that cannot afford any loss in accuracy or uptime, machine vision systems can really show their strengths. Fiducial-based machine vision systems allow operators to put markers exactly where precision is required. With inVia’s system that is picking and placing totes off of racking, placing those markers on the totes and the racking provides millimeter level accuracy to ensure that every tote is placed exactly where it’s supposed to go without fail. Trying to achieve this with a pure LiDAR system would be cost and time prohibitive for commercial use.
GN: Why is there a better business case?
Rand Voorhies: On the business side, the case is simple as well. Machine vision saves money and time. While LiDAR technology has decreased in cost over the years, it’s still expensive. We’re committed to finding the most cost-effective technologies and components for our robots in order to make automation accessible to businesses of any size. At inVia we’re driven by an ethos of making complex technology simple.
The difference in the time it takes to fulfill orders with machine vision versus with LiDAR and all of its re-mapping requirements is critical. It can mean the difference in getting an order to a customer on time or a day late. Every robot that gets lost due to LiDAR re-mapping reduces that system’s ROI.
The hardware itself is also cheaper when using machine vision. Cameras are cheaper than LiDAR, and most LiDAR systems need cameras with fiducials anyway. With machine vision, there’s an additional one-time labor cost to apply fiducials. However, applying fiducials one time to totes/racking is extremely cheap labour-wise and results in a more robust system with less downtime and errors.
GN: How will machine vision change the landscape with regards to robotics adoption in sectors such as logistics and fulfillment?
Rand Voorhies: Machine vision is already making an impact in logistics and fulfillment centers by automating rote tasks to increase the productivity of labor. Warehouses that use robots to fulfill orders can supplement a scarce workforce and let their people manage the higher-order tasks that involve decision-making and problem-solving. Machine vision enables fleets of mobile robots to navigate the warehouse, performing key tasks like picking, replenishing, inventory moves, and inventory management. They do this without disruption and with machine-precision accuracy.
Using robotics systems driven by machine vision is also removing barriers to adoption because of their affordability. Small and medium-sized businesses that used to be priced out of the market for traditional automation are able to reap the same benefits of automating repetitive tasks and, therefore, grow their businesses.
GN: How should warehouses go about surveying the landscape of robotics technologies as they look to adopt new systems?
Rand Voorhies: There are a lot of robotic solutions on the market now, and each of them uses very advanced technology to solve a specific problem warehouse operators are facing. So, the most important step is to identify your biggest challenge and find the solution that solves it.
For example, at inVia we have created a solution that specifically tackles a problem that is unique to e-commerce fulfillment. Fulfilling e-commerce orders requires random access to a high number of different SKUs in individual counts. That’s very different from retail fulfillment, where you’re retrieving bulk quantities of SKUs and shipping them out in cases and/ or pallets. The two operations require very different storage and retrieval setups and plans. We’ve created proprietary algorithms that specifically create faster paths and processes to retrieve randomly accessed SKUs.
E-commerce is also much more labor-dependent and time-consuming, and, therefore, costly. So, those warehouses want to adopt robotics technologies that can help them reduce the cost of their labor, as well as the time it takes to get orders out the door to customers. They have SLAs (service level agreements) that dictate when orders need to be picked, packed, and shipped. They need to ask vendors how their technology can help them eliminate blocks to meet those SLAs.
Robotics
This Labrador “Retriever” robot assists those with chronic pain
The best robot vacuums available now
Could this be the year of the pizza-making robot?
How to stop worrying and welcome the robots in 2022
Hardware
|
CXO
|
Innovation
|
Artificial Intelligence