TECHNOLOGY AT WORK

February 2015
Citi GPS: Global Perspectives & Solutions
49
The coupling of advanced sensors and
actuators in industrial robots to create
autonomous robots will have profound
impacts on employment
The Auto industry is already using big data
and improved sensors to substitute for
human workers
Autonomous Robots
Industrial robots are an exciting opportunity, however, it is in the coupling of the
advanced sensors and actuators in industrial robots to machine learning algorithms
to create autonomous robots that the most profound impact upon employment will
be found. Many tasks have remained non-automatable by virtue of the difficulty of
encoding the tacit knowledge we hold about how to interact with and manipulate our
physical environment. For example, when navigating in a vehicle, we draw upon a
rich knowledge of the automotive environment: we recall landmarks, interpret road
signs and account for recent changes due to construction or snowfall in order to
determine where we are in space. We might even use deep knowledge of culture
and society in order to inform our judgment: for example, a dirt track is unlikely to
lead to a supermarket, and a bus is most probably heading to or from a major
settlement. However, in recent times, this complex tacit knowledge has not
prevented the automation of driving: the Google self-driving car was licensed to
drive in the US state of Nevada in 2012.
The explanation for how human tacit knowledge was sidestepped is firstly found in the
availability of increasingly instrumented vehicles. Mass-production vehicles, such as
the Nissan LEAF, contain on-board computers and advanced telecommunication
equipment that render the car as potentially a fly-by-wire robot. 65 Advances in sensor
technology mean that vehicles are likely to soon be augmented with even more
advanced suites of sensors. These will permit an algorithmic vehicle controller to
monitor its environment to a degree that exceeds the capabilities of any human driver:
they are not subject to distraction, have the ability to simultaneously look both
forwards and backwards, and can natively integrate camera, GPS and LIDAR data.
The big data provided by these improved sensors are offering a substitute to human
tacit knowledge. Firstly, many modern vehicles offer Advanced Driver Assistance
Systems (ADAS) that draw upon sensor data to provide adaptive cruise control,
automated braking and even automated parallel parking. Further, the use of sensors
to create three-dimensional maps of road networks has allowed for the automation
of navigation; Google's driverless cars use an array of sensors to gather inchprecision
readings of its environment costing over $150,000. 66 On-board algorithms
can then compare a vehicle's current environment against prior maps stored on the
vehicle in order to determine its location. Modern approaches store maps that
characterise the different appearance of the environment throughout all the
changing seasons (e.g. after snowfall). 67 Machine learning techniques have also
been developed to identify unexpected changes to the road network, such as those
due to road construction. 68 Many auto manufacturers are expecting to be able to
offer autonomous vehicles between 2020 and 2025. 69
Given their superior sensing capabilities, algorithms are thus potentially safer and
more effective drivers than humans. This is no trivial contribution: road fatalities are
within the top 10 global causes of death, with human error responsible for more
than 90% of traffic accidents. 70 There are further potential contributions from
autonomous vehicles: 20% of carbon dioxide (CO 2 ) emissions on the road are due
to inappropriate accelerations, while 2% of US GDP is wasted because of
congestion. If robotic vehicles join the Internet of Things, we can envisage a
networked fleet of vehicles whose inter-communication and decentralised planning
may be able to tackle these problems.
65 A fly-by-wire robot is a robot that is controllable by a remote computer.
66 Guizzo (2011).
67 Churchill and Newman (2012).
68 Mathibela, Osborne, Posner, and Newman (2012).
69 Citi GPS (2014).
70 ibid
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