Category Archives: Robotics

NEW YORK, June 30, 2022 /PRNewswire/ -- The global Crop Harvesting Robots Marketsize is expected to grow by USD 1455.57billion at a CAGR of 28.36% as per Technavio. Thereport extensively coverscrop harvesting robots marketsegmentation by product (fruit and vegetable harvesting robots and grain harvesting robots) and geography (North America, Europe, APAC, South America, and MEA).

Read the 120-page report with TOC on "Crop Harvesting Robots Market Analysis Report byProduct (Fruit and vegetable harvesting robots and Grain harvesting robots) and Geography (North America, Europe, APAC, South America, and MEA), and the Segment Forecasts".Buy the Sample Report Now!

The market is driven by the increasing focus on farm mechanization. In addition, the growing popularity of solar-powered crop harvesting robots is anticipated to boost the growth of the crop harvesting robots market.

Machines are steadily replacing people in agriculture in various countries and have significantly reshaped the agricultural landscape. The productivity of the farm has a direct relationship with the efficient utilization of farm implements. Crop harvesting robots have been gaining popularity as an effective solution for harvesting across farms, greenhouses, and nurseries. They have widely been used as an effective solution for labor shortage problems during the harvesting season in various countries.

Some of the crop harvesting robots commonly used are lettuce harvesters, cucumber harvesters, wheat harvesters, barley harvesters, and sweet pepper harvesters. Thus, the increasing focus on farm mechanization will drive the demand for crop harvesting, thereby driving market growth.

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Crop Harvesting Robots Market Scope

Report Coverage

Details

Page number

120

Base year

2020

Forecast period

2021-2025

Growth momentum & CAGR

Accelerate at a CAGR of 28.36%

Market growth 2021-2025

$ 1455.57 billion

Market structure

Fragmented

YoY growth (%)

26.16

Regional analysis

North America, Europe, APAC, South America, and MEA

Performing market contribution

North America at 39%

Key consumer countries

US, Germany, UK, China, and France

Competitive landscape

Leading companies, Competitive strategies, Consumer engagement scope

Key companies profiled

Agrobot, Cerescon BV, Clearpath Robotics Inc., Dogtooth Technologies Ltd., Energid Technologies Corp., FFRobotics, Green Robot Machinery Pvt. Ltd., Harvest Automation, Harvest CROO Robotics, and Torguga Agricultural Technologies Inc.

Market dynamics

Parent market analysis, Market growth inducers and obstacles, Fast-growing and slow-growing segment analysis, COVID 19 impact and recovery analysis and future consumer dynamics, Market condition analysis for forecast period

Customization purview

If our report has not included the data that you are looking for, you can reach out to our analysts and get segments customized.

Table of Contents

1 Executive Summary

2 Market Landscape

3 Market Sizing

4 Five Forces Analysis

5 Market Segmentation by Product

6 Customer landscape

7 Geographic Landscape

8 Drivers, Challenges, and Trends

9 Vendor Landscape

10 Vendor Analysis

11 Appendix

About Technavio

Technavio is a leading global technology research and advisory company. Their research and analysis focuses on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions.

With over 500 specialized analysts, Technavio's report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavio's comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

Contacts

Technavio Research

Jesse Maida

Media & Marketing Executive

US: +1 844 364 1100

UK: +44 203 893 3200

Email:media@technavio.com

Website:www.technavio.com/

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Global Crop Harvesting Robots Market| Increasing focus on farm mechanization to boost market growth| Evolving Opportunities with Abundant Robotics...

As a family of robots, the IRB 1200 comes in two variants which can handle a wide range of applications. Both IRB 1200 variants can be mounted at any angle and come with IP 40 protection as standard. Both variants are available with Food Grade Lubrication, SafeMove, Foundry Plus 2 and Clean Room option. The 700 mm reach variant can carry a payload of up to 7 kg, while the longer reach 900 mm variant can carry up to 5 kg of payload.

Food Grade Lubrication and Clean Room optionThis Food Grade Lubrication (NSF H1) option includes Clean Room ISO class 3 and IP67 protection rating which ensure uncompromising safety and hygiene for food and beverage applications. The Clean Room ISO class 3 design eliminates the potential for contamination of the food handling area. This option provides smooth surfaces and makes it easy to keep clean and maintain in environments such as CNC machine tending and material handling in the food industry. This results in features that allow for 15% smaller cells with 10% shorter cycle times.

CompactThe IRB 1200 has no offset in axis two, which results in a longer stroke than other small robots and it, meaning it can be placed very close to the work piece and still be functional.

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IRB 1200 | ABB Robotics - Industrial Robots Portfolio | ABB Robotics

SafeMove, ABBs safety solution, provides greater flexibility, space savings, and cutting edge commissioning tools for higher productivity at a lower total cost of investment. All this, combined with unsurpassed safety, enables closer collaboration between robots and factory workers while reducing total investment by up to 30%.

First introduced in 2008, SafeMove performs safety certified monitoring of robot motion, tool and standstill supervision as well as speed limitation.

Working hand-in-hand with our customers to develop innovative robot safety technologies, ABB introduces SafeMove. It allows for the creation of more efficient and flexible production scenarios, and integrates safety fieldbus connectivity into ABBs IRC5 robot controller family.

Supported robots and controllers - Able to support all robot mounting angles, SafeMove is available for use with the majority of ABBs robot and controllers.

Collaboration - With SafeMove the tools to facilitate collaboration between man and machine are a reality.

RobotStudio - Safety configurations are now faster and more efficient. Taking full advantage of our premier offline programming tool, RobotStudio.

Faster commissioning times - SafeMove includes a set of easy-to-use tools for fast setup, validation and commissioning.

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SafeMove - Industrial Robot Controllers | ABB Robotics

Robotics is the overlapping of the computer science and engineering fields. Its dedicated to the designing, manufacturing, and use of autonomous, semi-autonomous, and non-autonomous robots.

The study of the field aims to create machines that are intelligent and dexterous, allowing them to assist humans in a wide variety of scenarios, from scientific experimentation and medical to life-threatening circumstances.

See below to learn all about the global robotics market:

The global service robotics market was estimated at $34.4 billion in 2020. Its expected to maintain a Compound Annual Growth Rate (CAGR) of 21.2% over the forecast period from 2020 to 2026, reaching $110.4 billion by the end of it.

By type, the hardware segment of the global market is projected to have a CAGR of 20.7% over the analysis period, while the software segment is forecast to have a 22.8% CAGR over the same analysis period.

Regionally, the global robotics market is segmented as follows:

By industry vertical, the health care industry is one of the leading users of robotics in the global market.

Other notable industry verticals include:

Robots have been around for decades in factories and warehouses in their most rudimentary form as automated machines. With the growth of hardware and software advancements in the field of robotics, estimates suggest a total count of 20 million robots by 2030.

Similarly, a McKinsey report estimates a decline of 18% in physical and manual labor and 28% in basic cognitive skills-based labor by 2030, which could make room for more jobs with expertise in STEM and socioemotional skills, in addition to major advances in health care and defense technology.

The rapid rise of robots means that were witnessing a major shift in the types of jobs that make up our workforce while many jobs and job roles are being eliminated, new ones are being created, and the vast majority of them are designed to improve AI or to use the results of AI (i.e., perform judgment), says Toby McClean, member of the Forbes Technology Council.

These new jobs will focus on more complex problems requiring higher-level critical thinking and analytical skills, and they will be needed across industries.

The field of robotics is vast and can be categorized in numerous ways depending on specific features. Most commonly, robots are categorized by their autonomy levels and the tasks theyre capable of performing

Autonomous robots are capable of acting and performing tasks without needing human control or supervision. While not strictly necessary, autonomous robots are becoming increasingly more dependent on artificial intelligence (AI) to navigate their surroundings independently.

Semi-autonomous robots are less independent than their fully autonomous counterparts. Capable of making decisions and performing actions on their own, they need real-time directions in some situations.

Non-autonomous robots are all robots that require external intervention to operate. This includes remote-controlled robots that must receive directions in real-time from a human in order to act.

When divided into more task-specific categories, robots can be found in a variety of categories, such as:

Articulated robots, or robotic arms, are robots with rotary joints meant to emulate the human arm, usually on a bigger scale. They contain anywhere from two to 10 rotary joints, with varying degrees of motion and dexterity.

While they tend to overlap in function and ability with other types, humanoid robots are distinctly recognizable with their human-like forms. Their capabilities, levels of intelligence, and autonomy depend on their functional purpose and whether its meant to interact with people in an environment or its simply for research purposes.

Collaborative robots, or cobots, are designed to function alongside humans. Cobots are capable of safely working in environments shared with humans, allowing them to increase productivity. Theyre also used in minimizing the risks of manual and dangerous tasks.

Soft robots are one of the latest robotics inventions that replace solid materials with soft alternatives. These use pressurized gas or liquid to maintain their shape and move around. They vary in their degree of autonomy, but are more flexible than the average robot, making them ideal for handling delicate objects or accessing narrow spaces.

Robotics continues to be one of the most revolutionary inventions, due to their contribution to a wide variety of fields, ranging from medicine and health care to manufacturing and high-risk work.

Some notable benefits of robotics include:

Robotics solutions tend to be customizable to meet the specific demands of the business or manufacturer it serves:

Amazon Robotics, formerly known as Kiva Systems, is a software developer and manufacturer of machinery used in Amazons warehouses and fulfillment centers. Based in Massachusetts, its main product is mobile robotic order fulfillment systems.

Switching from a manual item scanning model to machine learning (ML), Amazon Robotics stayed under the same brand name. Working with AWS, it used Amazon SageMaker to help its developers prepare, build, train, and deploy their ML models on the fulfillment robots.

Amazon SageMaker doesnt just manage the hosts we use for inferencing. It also automatically adds or removes hosts as needed to support the workload, says Eli Gallaudet, senior software manager, Amazon Robotics.

Amazon SageMaker doesnt just manage the hosts we use for inferencing. It also automatically adds or removes hosts as needed to support the workload.

Amazon Robotics was able to save 50% on inferencing costs, improve computer performances by 40%, and was able to save 20% on its computing costs.

Alliora is a cardboard box manufacturer based in northwestern France. It produces a wide variety of boxes for clients in a range of markets, such as cosmetics, board games, and champagne.

Alliora attempted to introduce automation to the production line with an industrial robot that was difficult to use. However, the need for automation was still important, as the repetitive tasks led some employees to develop musculoskeletal disorders.

Contacting one of its local robotics distributors, Sterkelec, Alliora ended up using the Robotiq Palletizing Solution, which consists of a collaborative robot arm and a vacuum-powered end-effector.

I am not a roboticist. Im not an automatician. But Davy Pourrieau [Sterkelecs product manager] explained it to me right away, and within 45 minutes, I understood how the system worked, says ric Bleau, production director, Alliora.

Using a cobot on one of its production lines, Alliora was able to hire two fewer workers during its high production periods and achieve an overall smoother production line.

Kidd Creek Mine is an underground metal mine located in Ontario, Canada. Owned and run by Glencore, a Swiss multinational company, the mines operations are supported by numerous unmanned aerial services as well as underground inspection specialists.

The workers at the Kidd Creek Mine are used to traditional tools in their work and inspections. They also used basic robots to access prospective mining areas to ensure the safety of the miners.

Working with Boston Dynamics and its four-legged robot, Spot, they had access to more accurate data. The automated robot withstands an array of harsh terrain with no problem.

Having a tool that can quickly, reliably get into everywhere that we need to get information and bring it back to us is invaluable to our business, says Iain McKillip, manager of mine technical services, Kidd Operations.

As engineering-driven mining relies primarily on up-to-date data, Boston Dynamics has enabled Kidd Creek to obtain a continuous stream of data to make decisions concerning worker safety.

Some of the leading providers of robotics solutions and innovations in the in the. market include:

Excerpt from:

The Robotics Market in 2022 - Datamation

DENVER, June 28, 2022--(BUSINESS WIRE)--AMP Robotics Corp. ("AMP"), a pioneer in AI, robotics, and infrastructure for the waste and recycling industry, has expanded its partnership with Waste Connections, Inc. (TSX/NYSE: WCN), its largest customer. Since late 2020, Waste Connections has booked or deployed 50 of AMPs high-speed robotics systems on plastic, fiber, and residue lines, becoming the largest operator of AI-guided robotics in the industry.

This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20220628005328/en/

AMP Robotics has expanded its partnership with Waste Connections, its largest customer. Since late 2020, Waste Connections has booked or deployed 50 of AMPs high-speed robotics systems on plastic, fiber, and residue lines, becoming the largest operator of AI-guided robotics in the industry. (Photo: Business Wire)

"Its gratifying to reach this milestone with a leader in resource recovery like Waste Connections. Weve worked tirelessly to deliver AI and robotics systems that modernize todays recycling operations by increasing productivity, stabilizing costs, and improving the quality and value of recycled commodities," said Matanya Horowitz, founder and CEO of AMP Robotics. "We remain committed to the continuous enhancement of our AI and automation solutions to stay ahead of industry challenges, exceed customer expectations, and support the industrys sustainability and climate goals."

AMPs technology identifies and recovers plastics, cardboard, paper, cans, cartons, and many other containers and packaging types reclaimed for raw material processing. For example, AMP recovers a portfolio of #1-#7 plastics in a variety of different form factors, colors, and opacities with high precision and purity. Polyethylene terephthalate (PET) and high-density polyethylene (HDPE) together account for a majority of recycled plastic demand, with growing interest in polypropylene (PP)a highly recyclable polymer with high demand in food-safe applications and flexibility to accommodate packaging in a variety of shapes and sizes. The ability to precisely separate different plastics and other recyclables with AI-driven sorting is helping recyclers meet the soaring need for sufficient quantities of high-quality recycled content from brands and packaging producers.

Story continues

"Were excited to expand our relationship with AMP," said Dan Kurtz, director of recycling for Waste Connections. "Were impressed by the reliability of the robotics systems and the quality of the end product. We look forward to our continued partnership with AMP as we advance our sustainability initiatives."

AMP has more than 230 deployments in more than 80 facilities across three continents.

About AMP Robotics Corp.

AMP Robotics is modernizing the worlds recycling infrastructure by applying AI and automation to increase recycling rates and economically recover recyclables reclaimed as raw materials for the global supply chain. The AMP Cortex high-speed robotics system automates the identification and sorting of recyclables from mixed material streams. The AMP Neuron AI platform continuously trains itself by recognizing different colors, textures, shapes, sizes, patterns, and even brand labels to identify materials and their recyclability. Neuron then guides robots to pick and place the material to be recycled. Designed to run 24/7, all of this happens at superhuman speed with extremely high accuracy. AMP Clarity provides data and material characterization on what recyclables are captured and missed, helping recycling businesses and producers maximize recovery. With deployments across North America, Asia, and Europe, AMPs technology recovers recyclables from municipal collection, precious commodities from electronic scrap, high-value materials from construction and demolition debris, and valuable feedstocks from organic material.

About Waste Connections

Waste Connections is an integrated solid waste services company that provides non-hazardous waste collection, transfer and disposal services, along with resource recovery primarily through recycling and renewable fuels generation. Waste Connections serves more than eight million residential, commercial, and industrial customers in mostly exclusive and secondary markets across 44 states in the U.S. and six provinces in Canada. Waste Connections also provides non-hazardous oilfield waste treatment, recovery, and disposal services in several basins across the U.S., as well as intermodal services for the movement of cargo and solid waste containers in the Pacific Northwest.

Waste Connections views its Environmental, Social and Governance ("ESG") efforts as integral to its business, with initiatives consistent with its objective of long-term value creation. The companys long-term, aspirational ESG targets include the expansion of resource recovery through recyclable commodities. For more information, visit wasteconnections.com/sustainability.

View source version on businesswire.com: https://www.businesswire.com/news/home/20220628005328/en/

Contacts

Carling Spelhaugcarling@amprobotics.com

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AMP Robotics and Waste Connections Reach Recycling Technology Milestone - Yahoo Finance

SPRINGFIELD, Mo. Ozarks Technical Community College received a $345,725grant from the National Science Foundation for its new automation and robotics program.

The program will debut in August at the Robert W. Plaster Center for Advanced manufacturing (PMC). The grant is funded by the NSFs Advanced Technological Education program, which focuses on training technicians in advanced technology fields.

According to a press release, the grant will improve and expand automation and robotics instruction for manufacturing students at OTC and increase the awareness of automation and robotics career opportunities for middle and high school students.

Its not every day that an institution earns a prestigious National Science Foundation grant, said Danelle Maxwell, OTC manufacturing dept. chair. It is gratifying to know that the NSF finds the curriculum and rigor in this new degree pathway worthy of a significant investment.

Students in the program will learn how to operate and maintain automated systems commonly used in manufacturing.

The college has consulted with our industry partners to equip the Plaster Manufacturing Center with the latest machinery and technology, said Robert Randolph, executive director of the PMC. When we send graduates into a career, they will be ready to work from day one because theyve been trained on the most modern equipment. Plus, this grant will allow the college to engage with young students and encourage them to consider manufacturing as a career.

In addition to automation and robotics, the 120,000 square foot, $40 million Plaster Manufacturing Center will provide training in the following disciplines when it opens in August of 2022:

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OTC receives grant for new automation and robotics program - KOLR - OzarksFirst.com

Robotics has made massive advances in recent years. Fields such as Robotics Process Automation (RPA) are being deployed in more and more businesses.

RPA software is needed to combine organizational processes with the actions of the robots and AI inputs. RPA software automates repetitive, labor-intensive, and time-consuming tasks, minimizing or eliminating human involvement to drive faster and more efficient processes across the factory floor. Instead of having dozens of workers in a manufacturing plant, an RPA specialist can program and run robots to perform those duties. Typically, another person is involved to service, maintain, and repair the hardware.

But AI is taking RPA to greater and greater levels of functionality.Here are some of the top trends in AI Robotics:

RPA and AI

The latest trend is for RPA to be integrated with AI. This is an essential element of RPA being able to deal with high-volume, repeatable tasks. By moving these over from humans to robots, these tasks are taken care of in a way that lowers labor costs, workflows are made more efficient, and processes such as those on assembly lines are accelerated.

This is also simplifying the overall field of robotics. Instead of different teams using different software, industrial settings can now combine RPA software and factory automation systems. Historically, the robotics team utilized specific programming languages to deal with the areas such as the kinematics of multi-axis robots. Factory automation technicians used different languages and tools such as Programmable Logic Controllers (PLCs) and shop floor systems. AI is helping to integrate these two worlds and add a greater degree of mobility and autonomy to robots.

For stationary robots to work seamlessly with mobile robots, it is essential that they can exchange information accurately and without failure, said Samir Patel, Senior Director, Robotics Engineering at Kawasaki Robotics USA.

Autonomous Operation

Peter Stone, PhD, Executive Director, Sony AI America notes that robots are moving more and more towards operating in open, uncontrolled spaces that are also occupied by people. Much effort is going into creating self-driving cars that are robust and economically viable. But beyond simple vacuum cleaners, AI is needed to take robotics to the next level.

Beyond creating robots that are viable as useful consumer products (other than for entertainment), there are still challenges for AI and robotics, said Stone.

AI will be needed to take into account thousands of parameters and variables occurring in real time. Many of these are changing constantly many times a second.

Neurosymbolic AI

Whats the next big thing in AI and robotics? Stone thinks it will be neurosymbolic AI.

The current AI boom was initiated by the confluence of the data and computation required to enable neural networks to achieve very impressive results on some very challenging tasks.While important research remains on understanding the full capabilities of neural networks, we are now seeing increased interest in 1) understanding their limitations and 2) integrating them with other tried and true AI algorithms, including symbolic and probabilistic methods.

In the coming years, broad exploration will occur in the field of hybrid neurosymbolic approaches towards applications that are beyond the capabilities of any one approach on its own, said Stone. Just as different regions of the human brain are known to operate differently (e.g. cerebellum vs. visual cortex), next-generation AI systems are likely to integrate differently operating modules. Research in this direction will be particularly useful for advances in general-purpose service robots capable of robust perception, communication in natural language, task and motion planning for object manipulation, and natural human-robot interaction across a wide variety of tasks.

Claims Processing

Over time, more and more tasks lend themselves to automation beyond just simple programming. Organizations are leveraging RPA, for example, to automate actions like understanding whats on a screen and completing keystrokes, and identifying and extracting data.

Health care is a good example, where such systems are being used to validate and process patient claims, said Adam Spotton,Head of Data Science,DNSFilter.

Job Candidates

Anyone posting a job opening typically receives hundreds if not thousands of resumes. AI robotics can be used to sift through them, and even find good candidates who may not immediately tick all the boxes. By training AI to note similar qualifications and other traits, better candidates are proposed and those that might be missed otherwise are given attention.

Amazon has been known to use RPA to find priority candidates for job positions by scanning and selecting resumes, said Spotton. I expect RPA to be a significant AI automation trend moving forward, across industries.

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5 Top Trends in AI Robotics in 2022 - Datamation

Mentions of robotics within the filings of companies in the tech sector rose 26% between the final quarter of 2021 and the first quarter of 2022.

In total, the frequency of sentences related to robotics between April 2021 and March 2022 was 2% lower than in 2016 when GlobalData, from whom our data for this article is taken, first began to track the key issues referred to in company filings.

When tech companies publish annual and quarterly reports, ESG reports and other filings, GlobalData analyses the text and identifies individual sentences that relate to disruptive forces facing companies in the coming years. Robotics is one of these topics - companies that excel and invest in these areas are thought to be better prepared for the future business landscape and better equipped to survive unforeseen challenges.

To assess whether robotics is featuring more in the summaries and strategies of tech companies, two measures were calculated. Firstly, we looked at the percentage of companies which have mentioned robotics at least once in filings during the past twelve months - this was 35% compared to 21% in 2016. Secondly, we calculated the percentage of total analysed sentences that referred to robotics.

Of the 10 biggest employers in the tech sector, Foxconn was the company which referred to robotics the most between April 2021 and March 2022. GlobalData identified 58 robotics-related sentences in the Taiwan-based company's filings - 0.6% of all sentences. Hitachi mentioned robotics the second most - the issue was referred to in 0.3% of sentences in the company's filings. Other top employers with high robotics mentions included China Mobile, NTT and Teleperformance.

Across all tech companies the filing published in the first quarter of 2022 which exhibited the greatest focus on robotics came from Teradyne. Of the document's 1,462 sentences, 28 (1.9%) referred to robotics.

This analysis provides an approximate indication of which companies are focusing on robotics and how important the issue is considered within the tech sector, but it also has limitations and should be interpreted carefully. For example, a company mentioning robotics more regularly is not necessarily proof that they are utilising new techniques or prioritising the issue, nor does it indicate whether the company's ventures into robotics have been successes or failures.

In the last quarter, tech companies based in Asia were most likely to mention robotics with 0.09% of sentences in company filings referring to the issue. In contrast, companies with their headquarters in Canada mentioned robotics in just 0.02% of sentences.

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Filings buzz in the tech sector: 26% increase in robotics mentions in Q1 of 2022 - Verdict

FREMONT, Calif., June 27, 2022 (GLOBE NEWSWIRE) -- Cobalt Robotics, the only company to automate repetitive manual security and facility tasks with an integrated service which unifies state-of-the-art robotics, machine learning software, and expert human oversight, announced today that it has been selected as a winner in the 5th Annual 2022 AI Breakthrough Awards Program taking home the Behavior-based Robotics InnovationAward.

TheAIBreakthroughAwardsprogram performs the deepest evaluations of theAIindustry each year to recognize and highlight the breakthroughAI companies,technologies, products and services from around the world. Over 2,950 nominations were submitted this year, and this is the second win for Cobalt Robotics which was previously honored in 2019.

We are honored to be selected as the winner of the Behavior-based Robotics Innovation Award from the AI Breakthrough Awards Program. This recognition is a testament to our teams continuous innovation and dedication, Mike LeBlanc, Chief Operating Officer of Cobalt Robotics. Hybrid work schedules, remote workers, and new expectations from employees mean that Safety and Security leaders need to rethink how they provide for their employees. Were proud that Cobalt robots have been able to adapt and respond to these issues so quickly.

Cobalts Remote Guarding Service SolutionCobalts Remote Guarding Service Solution includes state-of-the-art robots with over 60+ sensors including day-night cameras, 360-degree cameras, thermal cameras, depth cameras, LIDAR, and badge reading capabilities. Using machine learning, semantic mapping and novelty detection, the robot can independently identify and flag security-relevant anomalies like people, sounds, motion, doors and windows and missing assets. Each robot has a screen for communication between remote security specialists and people on-site.

In the event of an incident, Cobalts security specialists provide human assistance for complex situations. They will triage the incident, contact appropriate personnel and report back to the security team 24/7/365 based on collaboratively established post orders. They can also provide two-way video from the robot to greet employees, request badge credentials and ensure guest check in.

About Cobalt RoboticsCobalt Robotics is the only company to automate repetitive manual security and facility tasks with an integrated service which unifies state-of-the-art robotics, machine learning software, and expert human oversight. Its technology platform and 24/7 dedicated professional security services team improve safety, security and facility workflows with greater efficiency and predictability at substantially reduced costs, while allowing guards to focus on tasks that require judgment, empathy and decision making. Cobalts mobile robotics help companies make their spaces smarter, safer, and more secure, protecting both their employees and intellectual property. Leading enterprises including General Motors and Slack demonstrate that automation in the workplace is a top to bottom priority with Cobalt. Learn more at cobaltrobotics.com.

Media contact:Kerry MetzdorfBig Swing Communications (for Cobalt Robotics)978-463-2575kerry@big-swing.com

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Cobalt Robotics Wins Behavior-based Robotics Innovation Award in the 2022 AI Breakthrough Awards Program - GlobeNewswire

Its Tuesday night. In front of your house sits a large blue bin, full of newspaper, cardboard, bottles, cans, foil take-out trays, and empty yogurt containers. You may feel virtuous, thinking youre doing your part to reduce waste. But after you rinse out that yogurt container and toss it into the bin, you probably dont think much about it ever again.

The truth about recycling in many parts of the United States and much of Europe is sobering. Tomorrow morning, the contents of the recycling bin will be dumped into a truck and taken to the recycling facility to be sorted. Most of the material will head off for processing and eventual use in new products. But a lot of it will end up in a landfill.

So how much of the material that goes into the typical bin avoids a trip to landfill? For countries that do curbside recycling, the numbercalled the recovery rateappears to average around 70 to 90 percent, though widespread data isnt available. That doesnt seem bad. But in some municipalities, it can go as low as 40 percent.

Whats worse, only a small quantity of all recyclables makes it into the binsjust 32 percent in the United States and 10 to 15 percent globally. Thats a lot of material made from finite resources that needlessly goes to waste.

We have to do better than that. Right now, the recycling industry is facing a financial crisis, thanks to falling prices for sorted recyclables as well as policy, enacted by China in 2018, which restricts the import of many materials destined for recycling and shuts out most recyclables originating in the United States.

There is a way to do better. Using computer vision, machine learning, and robots to identify and sort recycled material, we can improve the accuracy of automatic sorting machines, reduce the need for human intervention, and boost overall recovery rates.

My company, Amp Robotics, based in Louisville, Colo., is developing hardware and software that relies on image analysis to sort recyclables with far higher accuracy and recovery rates than are typical for conventional systems. Other companies are similarly working to apply AI and robotics to recycling, including Bulk Handling Systems, Machinex, and Tomra. To date, the technology has been installed in hundreds of sorting facilities around the world. Expanding its use will prevent waste and help the environment by keeping recyclables out of landfills and making them easier to reprocess and reuse.

AMP Robotics

Before I explain how AI will improve recycling, lets look at how recycled materials were sorted in the past and how theyre being sorted in most parts of the world today.

When recycling began in the 1960s, the task of sorting fell to the consumernewspapers in one bundle, cardboard in another, and glass and cans in their own separate bins. That turned out to be too much of a hassle for many people and limited the amount of recyclable materials gathered.

In the 1970s, many cities took away the multiple bins and replaced them with a single container, with sorting happening downstream. This single stream recycling boosted participation, and it is now the dominant form of recycling in developed countries.

Moving the task of sorting further downstream led to the building of sorting facilities. To do the actual sorting, recycling entrepreneurs adapted equipment from the mining and agriculture industries, filling in with human labor as necessary. These sorting systems had no computer intelligence, relying instead on the physical properties of materials to separate them. Glass, for example, can be broken into tiny pieces and then sifted and collected. Cardboard is rigid and lightit can glide over a series of mechanical camlike disks, while other, denser materials fall in between the disks. Ferrous metals can be magnetically separated from other materials; magnetism can also be induced in nonferrous items, like aluminum, using a large eddy current.

By the 1990s, hyperspectral imaging, developed by NASA and first launched in a satellite in 1972, was becoming commercially viable and began to show up in the recycling world. Unlike human eyes, which mostly see in combinations of red, green, and blue, hyperspectral sensors divide images into many more spectral bands. The technologys ability to distinguish between different types of plastics changed the game for recyclers, bringing not only optical sensing but computer intelligence into the process. Programmable optical sorters were also developed to separate paper products, distinguishing, say, newspaper from junk mail.

So today, much of the sorting is automated. These systems generally sort to 80 to 95 percent puritythat is, 5 to 20 percent of the output shouldnt be there. For the output to be profitable, however, the purity must be higher than 95 percent; below this threshold, the value drops, and often its worth nothing. So humans manually clean up each of the streams, picking out stray objects before the material is compressed and baled for shipping.

Despite all the automated and manual sorting, about 10 to 30 percent of the material that enters the facility ultimately ends up in a landfill. In most cases, more than half of that material is recyclable and worth money but was simply missed.

Weve pushed the current systems as far as they can go. Only AI can do better.

Getting AI into the recycling business means combining pick-and-place robots with accurate real-time object detection. Pick-and-place robots combined with computer vision systems are used in manufacturing to grab particular objects, but they generally are just looking repeatedly for a single item, or for a few items of known shapes and under controlled lighting conditions.Recycling, though, involves infinite variability in the kinds, shapes, and orientations of the objects traveling down the conveyor belt, requiring nearly instantaneous identification along with the quick dispatch of a new trajectory to the robot arm.

AI-based systems guide robotic arms to grab materials from a stream of mixed recyclables and place them in the correct bins. Here, a tandem robot system operates at a Waste Connections recycling facility [top], and a single robot arm [bottom] recovers a piece of corrugated cardboard. The United States does a pretty good job when it comes to cardboard: In 2021, 91.4 percent of discarded cardboard was recycled, according to the American Forest and Paper Association.AMP Robotics

My company first began using AI in 2016 to extract empty cartons from other recyclables at a facility in Colorado; today, we have systems installed in more than 25 U.S. states and six countries. We werent the first company to try AI sorting, but it hadnt previously been used commercially. And we have steadily expanded the types of recyclables our systems can recognize and sort.

AI makes it theoretically possible to recover all of the recyclables from a mixed-material stream at accuracy approaching 100 percent, entirely based on image analysis. If an AI-based sorting system can see an object, it can accurately sort it.

Consider a particularly challenging material for todays recycling sorters: high-density polyethylene (HDPE), a plastic commonly used for detergent bottles and milk jugs. (In the United States, Europe, and China, HDPE products are labeled as No. 2 recyclables.) In a system that relies on hyperspectral imaging, batches of HDPE tend to be mixed with other plastics and may have paper or plastic labels, making it difficult for the hyperspectral imagers to detect the underlying objects chemical composition.

An AI-driven computer-vision system, by contrast, can determine that a bottle is HDPE and not something else by recognizing its packaging. Such a system can also use attributes like color, opacity, and form factor to increase detection accuracy, and even sort by color or specific product, reducing the amount of reprocessing needed. Though the system doesnt attempt to understand the meaning of words on labels, the words are part of an items visual attributes.

We at AMP Robotics have built systems that can do this kind of sorting. In the future, AI systems could also sort by combinations of material and by original use, enabling food-grade materials to be separated from containers that held household cleaners, and paper contaminated with food waste to be separated from clean paper.

Training a neural network to detect objects in the recycling stream is not easy. It is at least several orders of magnitude more challenging than recognizing faces in a photograph, because there can be a nearly infinite variety of ways that recyclable materials can be deformed, and the system has to recognize the permutations.

Its hard enough to train a neural network to identify all the different types of bottles of laundry detergent on the market today, but its an entirely different challenge when you consider the physical deformations that these objects can undergo by the time they reach a recycling facility. They can be folded, torn, or smashed. Mixed into a stream of other objects, a bottle might have only a corner visible. Fluids or food waste might obscure the material.

We train our systems by giving them images of materials belonging to each category, sourced from recycling facilities around the world. My company now has the worlds largest data set of recyclable material images for use in machine learning.

Using this data, our models learn to identify recyclables in the same way their human counterparts do, by spotting patterns and features that distinguish different materials. We continuously collect random samples from all the facilities that use our systems, and then annotate them, add them to our database, and retrain our neural networks. We also test our networks to find models that perform best on target material and do targeted additional training on materials that our systems have trouble identifying correctly.

In general, neural networks are susceptible to learning the wrong thing. Pictures of cows are associated with milk packaging, which is commonly produced as a fiber carton or HDPE container. But milk products can also be packaged in other plastics; for example, single-serving milk bottles may look like the HDPE of gallon jugs but are usually made from an opaque form of the PET (polyethylene terephthalate) used for water bottles. Cows dont always mean fiber or HDPE, in other words.

There is also the challenge of staying up to date with the continual changes in consumer packaging. Any mechanism that relies on visual observation to learn associations between packaging and material types will need to consume a steady stream of data to ensure that objects are classified accurately.

But we can get these systems to work. Right now, our systems do really well on certain categoriesmore than 98 percent accuracy on aluminum cansand are getting better at distinguishing nuances like color, opacity, and initial use (spotting those food-grade plastics).

Now thatAI-basedsystems are ready to take on your recyclables, how might things change? Certainly, they will boost the use of robotics, which is only minimally used in the recycling industry today. Given the perpetual worker shortage in this dull and dirty business, automation is a path worth taking.

AI can also help us understand how well todays existing sorting processes are doing and how we can improve them. Today, we have a very crude understanding of the operational efficiency of sorting facilitieswe weigh trucks on the way in and weigh the output on the way out. No facility can tell you the purity of the products with any certainty; they only audit quality periodically by breaking open random bales. But if you placed an AI-powered vision system over the inputs and outputs of relevant parts of the sorting process, youd gain a holistic view of what material is flowing where. This level of scrutiny is just beginning in hundreds of facilities around the world, and it should lead to greater efficiency in recycling operations. Being able to digitize the real-time flow of recyclables with precision and consistency also provides opportunities to better understand which recyclable materials are and are not currently being recycled and then to identify gaps that will allow facilities to improve their recycling systems overall.

Sorting Robot Picking Mixed PlasticsAMP Robotics

But to really unleash the power of AI on the recycling process, we need to rethink the entire sorting process. Today, recycling operations typically whittle down the mixed stream of materials to the target material by removing nontarget materialthey do a negative sort, in other words. Instead, using AI vision systems with robotic pickers, we can perform a positive sort. Instead of removing nontarget material, we identify each object in a stream and select the target material.

To be sure, our recovery rate and purity are only as good as our algorithms. Those numbers continue to improve as our systems gain more experience in the world and our training data set continues to grow. We expect to eventually hit purity and recovery rates of 100 percent.

The implications of moving from more mechanical systems to AI are profound. Rather than coarsely sorting to 80 percent purity and then manually cleaning up the stream to 95 percent purity, a facility can reach the target purity on the first pass. And instead of having a unique sorting mechanism handling each type of material, a sorting machine can change targets just by a switch in algorithm.

The use of AI also means that we can recover materials long ignored for economic reasons. Until now, it was only economically viable for facilities to pursue the most abundant, high-value items in the waste stream. But with machine-learning systems that do positive sorting on a wider variety of materials, we can start to capture a greater diversity of material at little or no overhead to the business. Thats good for the planet.

We are beginning to see a few AI-based secondary recycling facilities go into operation, with Amps technology first coming online in Denver in late 2020. These systems are currently used where material has already passed through a traditional sort, seeking high-value materials missed or low-value materials that can be sorted in novel ways and therefore find new markets.

Thanks to AI, the industry is beginning to chip away at the mountain of recyclables that end up in landfills each yeara mountain containing billions of tons of recyclables representing billions of dollars lost and nonrenewable resources wasted.

This article appears in the July 2022 print issue as AI Takes a Dumpster Dive .

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AI-Guided Robots Are Ready to Sort Your Recyclables - IEEE Spectrum