ROBOTICS CLUSTER

Recommendations

Info

AI and machine learning technologies and techniques are being used to make robotic systems more intelligent been used in support of decision making, object identification, vision processing, speech translation, navigation, motor control, sensor integration, and other functions, as well as facial and emotion recognition. 6.8.3.2. Deep Learning Deep learning, a class of machine learning techniques, has received much coverage from the technology and business media as of late, and for good reason. The technology, which employs multi-level (“deep”) neural networks to create inferencing systems for pattern and feature detection in large datasets, has proven critical for commercial applications ranging from speech and music recognition, to industrial process control and product recommendation. Deep learning techniques continue to improve, as do results. 6.8.3.3. Research and Investment Deep learning research is robust and ongoing, and investment in the technology is strong. Some of the world’s leading technology firms are now invested in the technology. Google’s purchase of U.K. AI startup DeepMind Technologies in 2014 for an estimated US$400 million exemplifies this. So, too, does Facebook’s launch of its own AI laboratory in 2014, and IBM’s launch of its Watson Group (also in 2014). Both research groups are focused on deep learning methods (among other research topics). IBM also purchased AlchemyAPI, a provider of AI-based text analysis and computer vision cloud services, in March 2015, and has other cognitive computing and deep learning initiatives underway. Microsoft’s Project Adam, the goal of which is to enable software to visually recognize any object, also exploits deep learning techniques. 6.8.3.4. Cloud and Local Deep learning methods are computationally intensive, requiring a great deal of computing resources. Software typically executes on powerful processors running on banks of highly optimized servers. As a result, devices running applications that use deep learning methods must typically access services residing in the cloud. Many efforts are currently underway to provide deep learning capabilities without the necessity of off-device processing in the cloud. 6.8.3.5. Deep Learning and Robotics AI and machine learning technologies and techniques are being used to make robotic systems more intelligent. Many of the capabilities enabled by deep learning methods are critical for robotics systems. Examples include computer vision, facial recognition, and natural language processing. Object recognition is especially important. A number of techniques have been employed to assist robots in recognizing objects, with deep learning methods central to many of them. Commercial companies are now coming to market with deep learning solutions designed specifically for robotics systems. 6.8.4. Robot Operating System (ROS) and Open Source Solutions Developing and deploying robotics systems are difficult, time-consuming, and error-prone, and as a result, robotics innovation and commercialization efforts have been slowed or stalled. To address the problem, ROS, open source system software for robotics, was developed (Quigley et al., 2009). ROS includes software libraries, tools, and a run-time environment specifically designed to ease the burden of creating advanced robotics applications. www.abiresearch.com THE MASSACHUSETTS ROBOTICS CLUSTER 54
ROS usage among robotics researchers is widespread and increasing rapidly. ROS is now considered standard technology among robotics researchers, but equally important, the next generation of robotics engineers will be versed in ROS. Industry support for ROS is also gaining, with a commercial robots and automation products based on ROS entering, and succeeding, in the market. By 2015, more than 70 commercial robotic systems used Robot Operating System is becoming foundational software for commercial robotics systems. ROS (see Figure 18, below), including many produced by Massachusetts robotics cluster companies. ROS is becoming foundational software for all manner of actuated devices, ranging from service robots to industrial manipulators, and on to consumer systems, autonomous vehicles, and more. Figure 18: Commercial Robots Supporting ROS (Sources: Open Source Robotics Foundation, ABI Research) 80 70 71 60 50 48 Robots 40 30 29 35 20 16 10 0 3 2010 2011 2012 2013 2014 2015 Year www.abiresearch.com THE MASSACHUSETTS ROBOTICS CLUSTER 55
Page 1 and 2:
www.abiresearch.com THE MASSACHUSET
Page 3 and 4:
TABLE OF CONTENTS 1. CONTRIBUTORS..
Page 5 and 6:
6.8.3.3. Research and Investment...
Page 7 and 8:
9. EVALUATION AND IMPLEMENTATION ME
Page 9 and 10:
1. CONTRIBUTORS ABI Research would
Page 11 and 12: 2.4. MASSACHUSETTS COLLABORATIVE RO
Page 13 and 14: • Universities and Labs: Massachu
Page 15 and 16: 3. INTRODUCTION Although substantia
Page 17 and 18: 4. ROBOTS AND ROBOTICS TECHNOLOGIES
Page 19 and 20: 5. ROBOTICS AS AN INNOVATION DRIVER
Page 21 and 22: 6. THE GLOBAL ROBOTICS AND INTELLIG
Page 23 and 24: 6.2. INDUSTRIAL ROBOTICS The defini
Page 25 and 26: Figure 3: Drivers for Industrial Ro
Page 27 and 28: Company Spotlight: Soft Robotics So
Page 29 and 30: Figure 6: The Healthcare Robotics S
Page 31 and 32: • Increased Expectations: With th
Page 33 and 34: Table 4: Representative Massachuset
Page 35 and 36: Figure 8: Projected U.S. DoD Unmann
Page 37 and 38: Key Market: Small Unmanned Ground V
Page 39 and 40: Field robotics initiatives are ofte
Page 41 and 42: Figure 9: Total Worldwide Commercia
Page 43 and 44: • Linear Robots: Also called gant
Page 45 and 46: Company Spotlight: Logistics System
Page 47 and 48: 6.5.1. Strong Growth In 2015, the c
Page 49 and 50: Company Spotlight: Consumer Systems
Page 51 and 52: Massachusetts Spotlight: New Englan
Page 53 and 54: 6.7.1.3. Massachusetts Leads In 201
Page 55 and 56: the end effectors was preprogrammed
Page 57 and 58: type of robotic UGV, and why autono
Page 59 and 60: Error-free, real-time mapping, navi
Page 61: Robotic edge products are fundament
Page 65 and 66: • Primary Robotics Cluster: The f
Page 67 and 68: The number and range of companies i
Page 69 and 70: 7.4. SALARIES FOR ROBOTICS ENGINEER
Page 71 and 72: Figure 23: Worldwide VC and Private
Page 73 and 74: Figure 25: 2015 Global Robotics Inv
Page 75 and 76: Global private investment trends fo
Page 77 and 78: Notable hardware accelerators that
Page 79 and 80: carrying out investigations into ot
Page 81 and 82: The National Security Engineering C
Page 83 and 84: 8. GUIDANCE AND RECOMMENDATIONS If
Page 85 and 86: Massachusetts robotics companies se
Page 87 and 88: 8.2.8. Offer Low-cost Loans for Dev
Page 89 and 90: 8.4.1. Strengthen Cluster Ties The
Page 91 and 92: 9. EVALUATION AND IMPLEMENTATION ME
Page 93 and 94: 9.1.5. Simplify, Limit, and Standar
Page 95 and 96: Figure 31: The Implementation Metho
Page 97 and 98: programs from other internships is
Page 99 and 100: Massachusetts Technology Leadership
Page 101 and 102: 12. APPENDIX B: LIST OF TABLES Tabl
Page 103 and 104: http://lis.csail.mit.edu/new/resear
Page 105 and 106: 14. APPENDIX D: WAGES FOR ROBOTICS
Page 107 and 108: 14.3. SOFTWARE DEVELOPERS, SYSTEMS
Page 109 and 110: 14.5. COMPUTER HARDWARE ENGINEERS*
Page 111 and 112: CONSUMER Consumer - Home Care/Lawn
Page 113 and 114:
17. APPENDIX G: ACRONYMS USED IN TH
Page 115 and 116:
sUGV SW TRI UAS UGS UGV UMS UNCTAD
Page 117 and 118:
Endeavor Robotics Bedford www.endea
Page 119 and 120:
www.abiresearch.com THE MASSACHUSET
Page 121 and 122:
Geartronics Industries 100 Chelmsfo
Page 123:
Published October 18, 2016 ©2016 A
show all

ROBOTICS CLUSTER

Create successful ePaper yourself

Delete template?

Save as template?