Daily Archives: July 5, 2020

In a building several stories tall with numerous rooms, hundreds of obstacles and thousands of places to inspect, the several dozen robots move as one cohesive unit. They spread out in a search pattern to thoroughly check the entire building while simultaneously splitting tasks so as to not waste time doubling back on their own paths or re-checking places other robots have already visited.

Such cohesion would be difficult for human controllers to achieve, let alone for an artificial controller to compute in real-time.

If a control problem has three or four robots that live in a world with only a handful of rooms, and if the collaborative task is specified by simple logic rules, there are state-of-the-art tools that can compute an optimal solution that satisfies the task in a reasonable amount of time, saidMichael M. Zavlanos, the Mary Milus Yoh and Harold L. Yoh, Jr. Associate Professor of Mechanical Engineering and Materials Science at Duke University.

And if you dont care about the best solution possible, you can solve for a few more rooms and more complex tasks in a matter of minutes, but still only a dozen robots tops, Zavlanos said. Any more than that, and current algorithms are unable to overcome the sheer volume of possibilities in finding a solution.

In anew paper published online on April 29in The International Journal of Robotics Research, Zavlanos and his recent PhD graduate student, Yiannis Kantaros, who is now a postdoctoral researcher at the University of Pennsylvania, propose a new approach to this challenge called STyLuS*, for large-Scale optimal Temporal Logic Synthesis, that can solve problems massively larger than what current algorithms can handle, with hundreds of robots, tens of thousands of rooms and highly complex tasks, in a small fraction of the time.

To understand the basis of the new approach, one must first understand linear temporal logic, which is not nearly as scary as it sounds. Suppose you wanted to program a handful of robots to collect mail from a neighborhood and deliver it to the post office every day. Linear temporal logic is a way of writing down the commands needed to complete this task.

For example, these commands might include to visit each house in sequential order, return back to the post office and then wait for someone to retrieve the collected mail before setting out again. While this might be easy to say in English, its more difficult to express mathematically. Linear temporal logic can do so by using its own symbols which, although might look like Klingon to the common observer, theyre extremely useful for expressing complex control problems.

The term linear is used because points in time have a unique successor based on discrete linear model of time, and temporal refers to the use of operators such as until, next, eventually and always, said Kantaros. Using this mathematical formalism, we can build complex commands such as visit all the houses except house two, visit houses three and four in sequential order, and wait until youve been to house one before going to house five.

To find robot controllers that satisfy such complex tasks, the location of each robot is mapped into a discrete data point called a node. Then, from each node, there exist multiple other nodes that are a potential next step for the robot.

A traditional controller searches through each one of these nodes and the potential paths to take between them before figuring out the best way to navigate its way through. But as the number of robots and locations to visit increase, and as the logic rules to follow become more sophisticated, the solution space becomes incredibly large in a very short amount of time.

A simple problem with five robots living in a world with ten houses could contain millions of nodes, capturing all possible robot locations and behaviors towards achieving the task. This requires a lot of memory to store and processing power to search through.

To skirt around this issue, the researchers propose a new method that, rather than constructing these incredibly large graphs in their entirety, instead creates smaller approximations with a tree structure. At every step of the process, the algorithm randomly selects one node from the large graph, adds it to the tree, and rewires the existing paths between the nodes in the tree to find more direct paths from start to finish.

This means that as the algorithm progresses, this tree that we incrementally grow gets closer and closer to the actual graph, which we never actually construct, said Kantaros. Since our incremental graph is much smaller, it is easy to store in memory. Moreover, since this graph is a tree, graph search, which otherwise has exponential complexity, becomes very easy because now we only need to trace the sequence of parent nodes back to the root to find the desired path.

It had been long accepted that growing trees could not be used to search the space of possible solutions for these types of robot control problems. But in the paper, Zavlanos and Kantaros show that they can make it work by implementing two clever tricks. First, the algorithm chooses the next node to add based on information about the task at hand, which allows the tree to quickly approximate a good solution to the problem. Second, even though the algorithm grows trees, it can still detect cycles in the original graph space that capture solutions to such temporal logic tasks.

The researchers show that this method will always find an answer if there is one, and it will always eventually find the best one possible. They also show that this method can arrive at that answer exponentially fast. Working with a problem of 10 robots searching through a 50-by-50 grid space 250 houses to pick up mail current state-of-the-art algorithms take 30 minutes to find an optimal solution.

STyLuS* does it in about 20 seconds.

We have even solved problems with 200 robots that live on a 100-by-100 grid world, which is far too large for todays algorithms to handle, said Zavlanos. While there currently arent any systems that use 200 robots to do something like deliver packages, there might be in the future. And they would need a control framework like STyLuS* to be able to deliver them while satisfying complex logic-based rules.

This research was supported by the Air Force Office of Scientific Research (FA95501910169) and the Office of Naval Research (N000141812374).

CITATION: STyLuS*: A Temporal Logic Optimal Control Synthesis Algorithm for Large-Scale Multi-Robot Systems, Yiannis Kantaros, Michael M Zavlanos. The International Journal of Robotics Research, vol. 39, no. 7, pp. 812-836, June 2020. DOI: 10.1177/0278364920913922

(C) Duke University

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Duke researchers propose new means for controlling hundreds of robots - WRAL Tech Wire

A report on Robotic Surgery market compiled by Brand Essence Market Research provides a succinct analysis regarding the values and trends existing in the current business scenario. The study also offers a brief summary of market valuation, market size, regional outlook and profit estimations of the industry. Furthermore, the report examines the competitive sphere and growth strategies of leading players in the Robotic Surgery market.

In 2018, the GlobalRobotic Surgery Marketsize was xx million US$ and it is expected to reach xx million US$ by the end of 2025, with a CAGR of xx% during 2019-2025.

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Key playersof the Robotic Surgery market are Intuitive Surgical, Inc., Auris Surgical Robotics, Medtech SA (Sub. Zimmer Biomet), Accuracy Inc., Smith & Nephew plc, Stryker Corporation., Corindus Vascular Robotics, Renishaw plc., Mazor Robotics, TransEnterix Surgical, Inc.

Robotic Surgery Market Segmentation:

Application: Urology,Gynecology,General Surgery,Thoracic Surgery,Cardiac Surgery,Neurosurgery,Others

Region Coverage (Regional Production, Demand & Forecast by Countries etc.):North America (U.S., Canada, Mexico)Europe (Germany, U.K., France, Italy, Russia, Spain etc.)Asia-Pacific (China, India, Japan, Southeast Asia etc.)South America (Brazil, Argentina etc.)Middle East & Africa (Saudi Arabia, South Africa etc.)

Table of Contents

1 Report Overview1.1 Study Scope1.2 Key Market Segments1.3 Players Covered1.4 Market Analysis by Type1.4.1 Global Robotic Surgery Market Size Growth Rate by Type (2014-2025)1.4.2 Topical Products1.4.3 Botulinum1.4.4 Dermal Fillers1.4.5 Chemical Peels1.4.6 Microabrasion Equipment1.4.7 Laser Surfacing Treatments1.5 Market by Application1.5.1 Global Robotic Surgery Market Share by Application (2014-2025)1.5.2 Hospitals1.5.3 Dermatology Clinics1.6 Study Objectives1.7 Years Considered

2 Global Growth Trends2.1 Robotic Surgery Market Size2.2 Robotic Surgery Growth Trends by Regions2.2.1 Robotic Surgery Market Size by Regions (2014-2025)2.2.2 Robotic Surgery Market Share by Regions (2014-2019)2.3 Industry Trends2.3.1 Market Top Trends2.3.2 Market Drivers2.3.3 Market Opportunities

3 Market Share by Key Players3.1 Robotic Surgery Market Size by Manufacturers3.1.1 Global Robotic Surgery Revenue by Manufacturers (2014-2019)3.1.2 Global Robotic Surgery Revenue Market Share by Manufacturers (2014-2019)3.1.3 Global Robotic Surgery Market Concentration Ratio (CR5 and HHI)3.2 Robotic Surgery Key Players Head office and Area Served3.3 Key Players Robotic Surgery Product/Solution/Service3.4 Date of Enter into Robotic Surgery Market3.5 Mergers & Acquisitions, Expansion Plans

Read More:https://industrystatsreport.com/Lifesciences-and-Healthcare/Global-Research-on-Robotic-Surgery-Market/Summary

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Robotic Surgery Market Strategies and Insight Driven Transformation 2020-2025 - Jewish Life News

Robotic Mapping and Navigation Market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors. Business strategies of the key players and the new entering market industries are studied in detail. Well explained SWOT analysis, revenue share and contact information are shared in this report analysis.

Robotic Mapping and Navigation Market is growing at a High CAGR during the forecast period 2020-2026. The increasing interest of the individuals in this industry is that the major reason for the expansion of this market.

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Top Key Vendors of this Market are:

Swisslog (KUKA), Omron Adept, Clearpath Robotics, Vecna, Mobile Industrial Robots, SMP Robotics, Aethon, Locus Robotics, Fetch Robotics, Hi-Tech Robotic Systemz, Amazon Robotics.

Various factors are responsible for the markets growth trajectory, which are studied at length in the report. In addition, the report lists down the restraints that are posing threat to the global Robotic Mapping and Navigation market. It also gauges the bargaining power of suppliers and buyers, threat from new entrants and product substitute, and the degree of competition prevailing in the market. The influence of the latest government guidelines is also analyzed in detail in the report. It studies the Robotic Mapping and Navigation markets trajectory between forecast periods.

The report provides insights on the following pointers:

Market Penetration:Comprehensive information on the product portfolios of the top players in the Robotic Mapping and Navigation market.

Product Development/Innovation:Detailed insights on the upcoming technologies, R&D activities, and product launches in the market.

Competitive Assessment: In-depth assessment of the market strategies, geographic and business segments of the leading players in the market.

Market Development:Comprehensive information about emerging markets. This report analyzes the market for various segments across geographies.

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The report summarized the high revenue that has been generated across locations like, North America, Japan, Europe, Asia, and India along with the facts and figures of Robotic Mapping and Navigation market. It focuses on the major points, which are necessary to make positive impacts on the market policies, international transactions, speculation, and supply demand in the global market.

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Table of Contents

Global Robotic Mapping and Navigation Market Research Report 2020 2026

Chapter 1 Robotic Mapping and Navigation Market Overview

Chapter 2 Global Economic Impact on Industry

Chapter 3 Global Market Competition by Manufacturers

Chapter 4 Global Production, Revenue (Value) by Region

Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions

Chapter 6 Global Production, Revenue (Value), Price Trend by Type

Chapter 7 Global Market Analysis by Application

Chapter 8 Manufacturing Cost Analysis

Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 10 Marketing Strategy Analysis, Distributors/Traders

Chapter 11 Market Effect Factors Analysis

Chapter 12 Global Robotic Mapping and Navigation Market Forecast

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Robotic Mapping and Navigation Market to Witness Robust Expansion by 2026 with Top Key Players like Swisslog (KUKA), Omron Adept, Clearpath Robotics,...

Moravecs Paradox states that while machines are great at doing some things that humans find complex, they are bad at doing other things we find simple. If thats the case, thenwe should really be asking ourselves: "What is the complexity in our business we can focus these machines on to make them optimal creative partners?"

In the same way that the engineering progress of robotics faces bottlenecks, so does commercial creativity. Ourbottlenecks are defined by the "iron triangle"that you learn about in management school: higher speed, better quality and lower cost. We are taught that you can only have two of these, at the expense of the third. Our clients expect all three. Machine-learning AI and automation, partnered with strong creativity,now makes this possible.

Data-driven insights: Great creativity comes from the constraints of a tight and accurate brief, based on observed consumer behaviornoton how people say they feel or what they claim they intend to do. (After all, if you want a consumer to lie, just ask them a question.)Machine learning helps refine data sets of extraordinary scale to reveal signals amongst the noise, moments that matter in consumer journeys, and help us understand the actions required to change the way people feel and act. The application of machine-learning AI to data generates invaluable insights, which facilitates more-effective creative.

Optimization and efficiency: The second way is the ability to fail faster, learn faster and fix faster, in order to get your creative expression exactly right. Long gone is the relevance of IPSOS-like testing of 30-secondspots against a set of norms and zero context. Today, we can make, run, iterate and learn. In a recent campaign for a luxury car client, we useda machine-learning AI platform that can makesubtle adaptations to creative based on audience data signals, down to the individual level. We set the creative parameters for the executionimages, sounds, claims and the overall narrativethen let the machine optimize it. The platform developed 438 permutations of the video, three of which significantly outperformed the others. The result was a 42 percent higher view-through rate and 17 percentcost reduction of the next-best action. The nuances in the creative wouldnt necessarily have been apparent to a human, but by embracing AI, the work became more effective.

Outcomes evaluation: The first two examples are tied to traditional media metrics, which are important, but not the ultimate goal. To take it a step further,robots are also able to determine and measure the true attribution of creativity on clients business outcomes, and on the resulting behaviors of individual consumers. With this attribution it is possible to demonstrate how creative work solved those "jobs to be done"in the consumer journey from the brief. And, because it can be measured and its marketing impact is assured, it allows more clients to shift to an outcomes-based pricing model, leaving the inputs-based approach where it belongsin the last decade.

These are just three of the many excitingpossibilities for applying machine-learning AI to commercial creativity.

By embracing the inhumanto handle those complex tasks that distract and slowdown the process, creatives can liberate themselves to becomemore human in pursuit of what was arguably the first, and will be the last, bastion ofhumanityour ability to create.

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Opinion: How the rise of robots can boost the human creative process - AdAge.com

The global Autonomous Mobile Robots Market is carefully researched in the report while largely concentrating on top players and their business tactics, geographical expansion, market segments, competitive landscape, manufacturing, and pricing and cost structures. Each section of the research study is specially prepared to explore key aspects of the global Autonomous Mobile Robots Market. For instance, the market dynamics section digs deep into the drivers, restraints, trends, and opportunities of the global Autonomous Mobile Robots Market. With qualitative and quantitative analysis, we help you with thorough and comprehensive research on the global Autonomous Mobile Robots Market. We have also focused on SWOT, PESTLE, and Porters Five Forces analyses of the global Autonomous Mobile Robots Market.

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Final Autonomous Mobile Robots Report will add the analysis of the impact of COVID-19 on this Market.

Autonomous Mobile Robots Market competition by top manufacturers/Key player Profiled:Swisslog (KUKA), Omron Adept, Clearpath Robotics, Vecna, Mobile Industrial Robots, SMP Robotics, Cimcorp Automation, Aethon, Locus Robotics, Fetch Robotics, Hi-Tech Robotic Systemz, Aviation Industry Corporation, Savioke

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With the slowdown in world economic growth, the Autonomous Mobile Robots industry has also suffered a certain impact, but still maintained a relatively optimistic growth, the past four years, Autonomous Mobile Robots market size to maintain the average annual growth rate of 0.0844717711977 from 120.0 million $ in 2015 to 180.0 million $ in 2020, This Report analysts believe that in the next few years, Autonomous Mobile Robots market size will be further expanded, we expect that by 2024, The market size of the Autonomous Mobile Robots will reach 450.0 million $.

Segmentation by Product:AMRs with SLAM, AMRs without SLAM

Segmentation by Application:Hospitals and Healthcare, Manufacturing, Logistics and Warehouse

Competitive Analysis:

Global Autonomous Mobile Robots Market is highly fragmented and the major players have used various strategies such as new product launches, expansions, agreements, joint ventures, partnerships, acquisitions, and others to increase their footprints in this market. The report includes market shares of Autonomous Mobile Robots Market for Global, Europe, North America, Asia-Pacific, South America and Middle East & Africa.

Scope of the Report:The all-encompassing research weighs up on various aspects including but not limited to important industry definition, product applications, and product types. The pro-active approach towards analysis of investment feasibility, significant return on investment, supply chain management, import and export status, consumption volume and end-use offers more value to the overall statistics on the Autonomous Mobile Robots Market. All factors that help business owners identify the next leg for growth are presented through self-explanatory resources such as charts, tables, and graphic images.

Key Questions Answered:

Our industry professionals are working reluctantly to understand, assemble and timely deliver assessment on impact of COVID-19 disaster on many corporations and their clients to help them in taking excellent business decisions. We acknowledge everyone who is doing their part in this financial and healthcare crisis.

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Table of Contents

Report Overview:It includes major players of the global Autonomous Mobile Robots Market covered in the research study, research scope, and Market segments by type, market segments by application, years considered for the research study, and objectives of the report.

Global Growth Trends:This section focuses on industry trends where market drivers and top market trends are shed light upon. It also provides growth rates of key producers operating in the global Autonomous Mobile Robots Market. Furthermore, it offers production and capacity analysis where marketing pricing trends, capacity, production, and production value of the global Autonomous Mobile Robots Market are discussed.

Market Share by Manufacturers:Here, the report provides details about revenue by manufacturers, production and capacity by manufacturers, price by manufacturers, expansion plans, mergers and acquisitions, and products, market entry dates, distribution, and market areas of key manufacturers.

Market Size by Type:This section concentrates on product type segments where production value market share, price, and production market share by product type are discussed.

Market Size by Application:Besides an overview of the global Autonomous Mobile Robots Market by application, it gives a study on the consumption in the global Autonomous Mobile Robots Market by application.

Production by Region:Here, the production value growth rate, production growth rate, import and export, and key players of each regional market are provided.

Consumption by Region:This section provides information on the consumption in each regional market studied in the report. The consumption is discussed on the basis of country, application, and product type.

Company Profiles:Almost all leading players of the global Autonomous Mobile Robots Market are profiled in this section. The analysts have provided information about their recent developments in the global Autonomous Mobile Robots Market, products, revenue, production, business, and company.

Market Forecast by Production:The production and production value forecasts included in this section are for the global Autonomous Mobile Robots Market as well as for key regional markets.

Market Forecast by Consumption:The consumption and consumption value forecasts included in this section are for the global Autonomous Mobile Robots Market as well as for key regional markets.

Value Chain and Sales Analysis:It deeply analyzes customers, distributors, sales channels, and value chain of the global Autonomous Mobile Robots Market.

Key Findings: This section gives a quick look at important findings of the research study.

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Global Covid-19 impact on Autonomous Mobile Robots Market 2020 Global Research Report by Key Drivers Analysis, Competitive Strategies, Company...

Recently published Advanced report on Global Industrial and Collaborative Robots market offers an insightful take on the drivers and restraints present in the market. The report begins with a brief presentation and market summary of Industrial and Collaborative Robots market about the current market landscape, coming market trends, leading market players, product type, application, and region. This report also includes the impact of COVID-19 on the global Industrial and Collaborative Robots market trends, future forecasts, growth opportunities, key end-user industries, and market players. Highlights increase factors of the main segments and sub-segments including market growth, drivers, projections, and structure of the present requirements of the Industrial and Collaborative Robots market.Industrial and Collaborative Robots the market study offers a comprehensive understanding of the value, utilization, price, import, demand, gross margin, product, share, and supply of the market. It describes the future of the market by investigating past details. The report is organized by experienced specialists and market researchers, which gives it very real and honest.

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On the basis of product eachTYPESprimarily split into:

Articulated RobotsParallel RobotsSCARA RobotsCylindrical RobotsCartesian RobotsCollaborative Robots

On the basis of product eachAPPLICATIONSprimarily split into:

AutomotiveElectrical and ElectronicsChemical, Rubber and PlasticMetal and MachineryFood, Beverages and Pharmaceuticals

Key Regions split during this report:

North America (the USA, North American country and Mexico)

Europe (Germany, France, UK, Russia and Italy)

Asia-Pacific (India, China, Japan, Korea and Southeast Asia)

South America ( Columbia, Brazil, Argentinaetc.)

The Middle East and continent (Saudi Arabian Peninsula, UAE, Egypt, African country and South Africa)

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Covid-19 Impact On Global Industrial and Collaborative Robots Market Worldwide Overview By Industry Size, Future Trends, Growth Factors - NJ MMA News

Global Defense and Military Security Robots Market: Trends Estimates High Demand by 2027

The Defense and Military Security Robots Market 2020 report includes the market strategy, market orientation, expert opinion and knowledgeable information. The Defense and Military Security Robots Industry Report is an in-depth study analyzing the current state of the Defense and Military Security Robots Market. It provides a brief overview of the market focusing on definitions, classifications, product specifications, manufacturing processes, cost structures, market segmentation, end-use applications and industry chain analysis. The study on Defense and Military Security Robots Market provides analysis of market covering the industry trends, recent developments in the market and competitive landscape.

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The final report will add the analysis of the Impact of Covid-19 in this report Defense and Military Security Robots industry.

Some of The Companies Competing in The Defense and Military Security Robots Market are: QinetiQ Group PLC,Lockheed Martin Corp.,DJI,Cobham PLC,Recon Robotics,Northrop Grumman Corp,Leonardo,Knightscope Inc,Aerovironment Inc.,Kongsberg Gruppen,BAE Systems PLC,Thales SA,Elbit Systems.

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A short overview of the Defense and Military Security Robots market scope:

Global market remuneration

Overall projected growth rate

Industry trends

Competitive scope

Product range

Application landscape

Supplier analysis

Marketing channel trends Now and later

Sales channel evaluation

Market Competition Trend

Market Concentration Rate

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This report provides pin-point analysis for changing competitive dynamics

It provides a forward looking perspective on different factors driving or restraining market growth

It provides a six-year forecast assessed on the basis of how the market is predicted to grow

It helps in understanding the key product segments and their future

It provides pin point analysis of changing competition dynamics and keeps you ahead of competitors

It helps in making informed business decisions by having complete insights of market and by making in-depth analysis of market segments

TABLE OF CONTENT:

Chapter 1:Defense and Military Security Robots Market Overview

Chapter 2: Global Economic Impact on Industry

Chapter 3:Defense and Military Security Robots Market Competition by Manufacturers

Chapter 4: Global Production, Revenue (Value) by Region

Chapter 5: Global Supply (Production), Consumption, Export, Import by Regions

Chapter 6: Global Production, Revenue (Value), Price Trend by Type

Chapter 7: Global Market Analysis by Application

Chapter 8: Manufacturing Cost Analysis

Chapter 9: Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 10: Marketing Strategy Analysis, Distributors/Traders

Chapter 11: Defense and Military Security Robots Market Effect Factors Analysis

Chapter 12: GlobalDefense and Military Security Robots Market Forecast to 2027

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Research On Global Defense and Military Security Robots Market (impact of COVID-19) with Top Players: QinetiQ Group PLC,Lockheed Martin Corp.,DJI and...