Boston Dynamics commercially launched Stretch, our autonomous robot for the warehouse, at MODEX 2022 in Atlanta in late March. At demos in the booth each day, crowds watched as the robots demonstrated one of the most common and difficult to solve challenges in logistics today: truck unloading.
In addition to Spot’s revolutionary mobility, one of its most important attributes is that the robot is designed as a scalable platform that is constantly evolving and expanding on the number of dull, dirty and dangerous jobs it can handle for our customers.
Although we are all familiar with the five senses humans use to perceive our surroundings, for robots, these senses look a little different. Robots are rapidly becoming more prevalent in our everyday lives which can take a plethora of forms, from industrial applications, to delivery drones to robotic waiters. No matter what the role, these robots require sensors of all sorts to see the environment around them. The combination of cameras, LiDAR and gas sensors, together play a vital role in informing the robot’s unique perspective.
Limiting exposure to radiation is a top priority for nuclear generators, and organizations are looking increasingly to agile mobile robots to keep their workforce safe. Boston Dynamics Spot allows customers to automate inspection tasks and map the presence of radiation on site. Additionally, operators are able to perform manual inspections remotely, either using the tablet controller from a safe distance, or Scout desktop software.
Robots are used for a host of different research topics and applications, from studying human-robot interaction to collecting samples in inhospitable environments to developing new commercial applications of robotics and artificial intelligence. All of these different research goals may require unique capabilities or specifications, but there are some consistent factors for what makes an effective robot in a research context. First and foremost, a research robot needs to serve the research; a robot that is costly or resource-intensive to build, maintain, or deploy takes time, energy, and budget away from a project or lab’s primary goals.
Effective maintenance programs are vital in manufacturing, energy and utilities, and other asset-intensive industries—preventing equipment failures that lead to costly downtime and repairs. Smart organizations have shifted toward predictive maintenance strategies. Instead of periodically checking assets and replacing parts on fixed schedules, companies are now using artificial intelligence (AI) to identify warning signs of impending issues before they cause damage or outages. This approach can help extend asset lifespans, extract the full value of equipment and parts, and avoid unplanned outages.
Spot’s easy mobility, along with a host of other benefits, has made it a popular choice in academia and research institutions. The ready-out-of-the-box quadruped robot helps researchers focus on their objectives, explore tough problems, and experiment with the many ways robotics can positively impact people’s lives. Here are just a few ways that educational and research organizations are working with Spot today.
As a quadruped robot, Spot can operate virtually anywhere a person can—going up and down stairs, indoors or outdoors, and through cramped or confined spaces. While this mobility can be impressive on its own, its real value is in automating sensing and inspection in areas inaccessible with wheeled or tracked robots and drones. You can send Spot into areas designed for people, that are unstructured, or even hazardous, to collect the data you need to understand what's going on in your facility or at your jobsite.
Customers finding bare spots on grocery store shelves or waiting months instead of weeks for a furniture delivery are feeling the ripple effects of supply chain disruptions—with more shortages and delays anticipated as the holiday season approaches.
Much of the merchandise is out there, stalled in cargo ports loaded with shipping containers at unprecedented volumes. Labor shortages have slowed the pace of unloading containers and warehouses to a crawl, and ports on both U.S. coasts have required ships to idle at sea for days until space opens up in the yards. Those delays are straining the supply of cargo containers at a time when demand for goods is hardly slowing down—rather, U.S. imports are projected to rise through the end of the year.
Artificial intelligence is changing the way businesses operate. AI systems demand a continuous flow of repeatable data that’s difficult to collect on physical sites. Today, the health of equipment in facilities is monitored through a mix of fixed sensors and operator rounds. These sensors are limited in perspective, expensive to scale, and can’t adapt to changes. Companies make up the data deficit by spending human resources on tedious data collection. But people are too inconsistent at data collection and too valuable to be feeding an AI system all day. Instead, they should be at the top of the value chain turning data-driven insights into action.