Rise of the Robots

Australia’s Robotic Future

Dr Airlie Chapman

Melbourne Information, Decision and

Autonomous Systems (MIDAS)

Fundamental research areas:• Networked dynamical systems

• Computational engineering for dynamic

systems

• Human-centric autonomous systems

• Legal and societal implications of

autonomy

Large and complementary group of control, automation and optimisation

researchers (100pp) across multiple Departments in the University of Melbourne

(CIS, EE, ME, Science, Psychology)

Applications:• UAVs and UGVs

• Robotics

• Precision manufacturing

• Smart irrigation channels

• Powertrain control and calibration

• Power systems and microgrids

• Gas turbines

Research sponsors include:

Experimental Facilities

…with Energy:

Transient reciprocating

engine dynamometer

6-axis CNC with large work

piece at ANCA Motion

Network simulators

and rapid prototyping

control units

Motion capture

and robotic

platforms

Simulation capability

includes:

…with industry:

Vibration

control:

…with Flight:

Campus 2 autonomous

systems lab

…coming soon:

Irrigation network control

• Irrigation ~70% of all water use

• Smart wastegate control

– Modelling of irrigation channels

– Decentralised control

– A working “Internet of Things”

• Reported 20-30% water savings

• Recognised with 2008 ATSE Clunies Ross Award

Australia’s Robotic Landscape

• 18th for global automation by the International Federation of

Robotics

• 1st country to automate its ports

• Predicted to deliver $2.2 trillion dividend over the next 15 years

• 1100 companies support the robotic industry

as service business within major companies

or SMEs for niche markets

• Manufacturing robots accounts for 86%

robots (International Federation of Robotics)

• Drivers: Price, innovative applications,

consumer demand

Mobile Robotics

- Vehicles that act independently

Tesla – Autopilot

Roadmap for Mobile Robotics

• Motion control

• Navigation and mapping

• Sensors and predictions

• Emerging Technologies

• Challenges and a path forward

• Examine the current prediction of a system

• Design actions (control) to acquire a desired output

Motion Control

Courtesy: TechXplore

Cruise Control

Toyota Cruise Control

Precision

Volvo Dynamic Steering

Livestock

SwagBot – University of Sydney

Underwater

COTSbot – Queensland University of Technology

Transportation

Haulage train – Rio Tinto, Pilbara

• Plan paths and motions to navigate in its environment

• Assemble a map of the environment to plan over

Navigation and Mapping

Mining

Caterpillar – Haulage

Agriculture

John Deere – Tractor and Planter

Mine Stope Mapping

Emesent – CSIRO’s Data61

Infrastructure Maintenance

UTS Blasting Robot (ABC News)

• Sensors: Gives the ability to see, touch, and hear its

surroundings

• Prediction: Estimation of the state of the world, e.g., Computer

vision, AI, deep neural networks

Sensors and Predictions

Courtesy: Heriot-Watt/Audi

Sensors

Mobile Sensors

Autonomous Crop Interaction

Rippa – University of Sydney

UAV – Unmanned Aerial Vehicles

AgLoop TV

Aerial Monitoring and Inspection

AgLoop TV

Horteye – University of Melbourne

Multicopters Inspection

• Delivery and placement

• One to Many

• Collaborative robotics

Emerging

Delivery and Placement

Project Wing – Alphabet (WSJ)

Multi-Package Delivery

MIDAS Air – University of Melbourne

One to Many - Cooperative Robotics

Kiva Systems – Amazon Robotics

Cooperative Warehousing

Collaborative Construction

Digital Fabrication – ETH Zurich

Collaborative Coverage

MIDAS Networks – University of Melbourne

Challenges and a Path Forward

• Technical challenges: GPS denied environments, cluttered

spaces, battery capacity, communication and computation

limitations

• Promote government frameworks for autonomous systems

• Establish test sites to trial technologies

• Develop robotic clusters of innovation with strong industry links

• Publicity: Safety, accuracy and productivity

• Success: Robots to tool

Thanks.