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The ACFR has been conducting research in autonomous, remote sensing and developing robotics and intelligent software for the environment and agriculture community over the last 10 years.

  • Salah Sukkarieh - Project Lead
  • James Underwood - Senior Research Fellow, Sensors, Mapping and Autonomous Perception  
  • Robert Fitch - Senior Research Fellow, Manipulation, Planning and Optimisation
  • Nasir Ahsan - Research Fellow, Detection and Classification 
  • Wolfram Martens - Research Fellow, Perception and Planning
  • Asher Bender - Research Fellow, Crop Modelling and Decision Support
  • Mark Calleija - Systems Engineer and Operations
  • Vsevolod Vlaskine - Software Team Leader
  • Vinny Do - Software Engineer
  • Andrey Sokolov - Software Engineer
  • Suchet Bargoti - PhD student, Perception, Detection and Classification
  • Alex Wendel - PhD student, Perception, Detection and Classification
  • Mikkel Kragh Hansen - Visiting PhD student, Perception and Obstacle Detection
  • Timothy Patten - PhD student, Multi-robot planning
  • Steven Potiris - PhD student, Weed detection and mitigation
  • John Gardenier - PhD student, Livestock health monitoring
  • Samuel Orn - Visiting Master's student, 3D Tree Canopy Modelling
  • Madeleine Stein - Visiting Master's student, Image Based Fruit Detection

We have a range of projects, using Unmanned Air and Ground Vehicles, with the latest updates listed below.

Meet SwagBot – our latest farming robot. SwagBot proved successful in its first field test. SwagBot successfully demonstrated the ability to operate in the rugged cattle station environment. Future trials will focus on applying research toward autonomous farm activities including monitoring and interacting with plants and animals.

Mary O'Kane reflects on Trends to inform smart choices in the June edition of FOCUS. (See pages 11-13)

New Scientist video featuring ACFR robots.

Salah Sukkarieh will be presenting the latest Farms of the Future work from the Australian Centre for Field Robotics, at The Science & Research Breakfast Seminar hosted by The NSW Chief Scientist & Engineer, Professor Mary O'Kane, on Wednesday 11 May 2016. Details of the invitation are attached:

Members of the House of Representatives Standing Committee on Agriculture and Industry visited the ACFR technical laboratory on 14 April 2016, to hear about the latest innovations around robotics in agriculture.  The committee were briefed on the current research and how it is directly related to aiding farmers and growers, such as sensory and imaging processes to improve apple growing, the RIPPA robot which can target and destroy weeds in crops, and UAVs for identifying problem weeds in the Australian outback. This visit was part of the federal parliament public hearing on agricultural innovation. More information about the hearing can be found at http://sydney.edu.au/news-opinion/news/2016/04/12/federal-parliament-public-hearing-on-agricultural-innovation-at-.html

 

On April 6th 2016 RIPPA ran its first endurance trial and completed almost 22 hours of continuous operation using only battery and solar power. This was a major accomplishment and testament that the RIPPA design and ACFR Ag robots are focused on being a real solution to the farmer. The run began at 0530, 1 hour before sunrise and completed at 0317 the next morning, 9 hours after sunset. For the duration, RIPPA roved autonomously up and down the spinach crop rows imaging the leaves. RIPPA then waited until solar sufficiently charged the batteries and at 1000 it began where it left off and continued roving up and down the rows. The irrigation created muddy and uneven terrain at the row ends, which was no problem for RIPPA as you can see in the video. A fantastic effort from the ACFR team.

Thanks to Horticulture Innovation Australia and to Ed Fagan for hosting and supporting us at his farm.

 

 

 

 

A new three year program of high tech R&D for orchard management has begun, with the use of our Shrimp robot to acquire data from mango, avocado and macadamia orchards.

http://www.abc.net.au/news/2016-02-04/mapping-australias-tree-crops/7137014

The data includes lidar, vision, thermal, hyperspectral, soil conductivity and natural gamma, demonstrating that there are many ways to view the humble tree:

 

RIPPA has just had its first ever field trial on a spinach crop at Mulyan farms in Cowra, NSW. We had RIPPA driving up and down the rows autonomously using satellite based corrections to within 4cm precision. You can see RIPPA and VIIPA in action on the WIN News Central West Facebook page here:

https://www.facebook.com/431557963627074/videos/845773805538819/

Here's a video showing the first outdoor test of our new precision ground vehicle RIPPA™ (Robot for Intelligent Perception and Precision Application). VIIPA™ (Variable Injection Intelligent Precision Applicator) is shown autonomously shooting weeds at high speed using a directed micro dose of liquid. The first on-farm trial will be in Cowra late October, 2015!

With its comprehensive array of sensors, and ability to precisely and repeatably scan the field, Ladybird is well suited as a scientific research tool to measure crop phenotypes. We're working with the South Australian Research and Development Institute (SARDI) to test this application.

 

James Underwood gave a talk about autonomous information systems for tree crops, at the APAL speed updating session, alongside the National Horticulture Convention on the Gold Coast in June 2015. All the talks are available here.

 

This video shows the Ladybird performing targeted spot spray in real time. In this example, we show real-time results, first in the lab and then on a commercial vegetable orchard in Cowra, NSW, Australia. Ladybird detects the locations of seedlings in 3D using a stereo camera, then fires a small and controllable volume of spray at each target. Coupled with algorithms shown in previous videos for automatic weed detection, this technology can be used to deliver tiny amounts of herbicide exactly where it's needed, anywhere on the farm, allowing a herbicide volume reduction to only 0.01% compared with conventional blanket spraying applications.

This video demonstrates the use of a reconfigurable rover for crop row monitoring.

The Ladybird robot and the Agricultural Robotics team at ACFR, The University of Sydney would like to wish everyone a safe and happy holiday period!

Here’s a demonstration of concept weeding methods using the robotic manipulator on our Ladybird robot. We’ll be doing some field trials early 2015!

We've just returned from another successful trip to the farm. Ladybird scanned corn to detect different varieties of weeds within the crop and beetroot just prior to harvest for yield monitoring and to evaluate the performance of different seed spacings. With harvest occurring all around us, it was great to see Ladybird operating autonomously alongside traditional farm equipment, showing that high-tech autonomous systems can easily coexist with current methods. The farm of the future is nearer than you might think.

 


2-6 February 2015
Sydney, Australia
http://www.acfr.usyd.edu.au/education/ssar2015.shtml

Applications due: 8 December 2014 (extended)
General enquiries: ssar2015@acfr.usyd.edu.au


NEW: Preliminary program now listed at http://www.acfr.usyd.edu.au/education/ssar2015.shtml

The IEEE RAS Summer School on Agricultural Robotics (SSAR 2015) is a new summer school to be held at The University of Sydney, Australia over five days during the southern hemisphere summer, from 2-6 February 2015. SSAR 2015 is supported in part by the IEEE Robotics and Automation Society and The University of Sydney.

Agricultural robotics is an area of growing interest with the potential to bring about profound economic and social benefits. The School aims to promote robotics research that will enable safe, efficient, and economical production in agriculture and horticulture. The School will consist of presentations by world experts covering a broad range of topics in agricultural robotics, hands-on activities that encourage deep learning, and collaboration activities including a student poster session as well as several social events. Attendance is open to graduate students, postdocs, academics, and industry practitioners.

THEMES
The main technical objective of the School is to cover the motivation driving research in agricultural robotics, existing projects and results, and open research problems in key areas of agricultural robotics. Underlying research topics include systems design of outdoor platforms, perception in semi-structured outdoor environments, planning and control for single and multiple robot systems, and manipulators for harvesting and weeding.

The School will include presentations (and opportunities to interact with) representatives from the USDA, GRDC, Horticulture Innovation Australia Limited, and the Cotton Research and Development Corporation.

Please check the website for updates on the detailed technical program.

APPLICATION AND REGISTRATION
Application details can be found on the SSAR 2015 website (http://www.acfr.usyd.edu.au/education/ssar2015.shtml). 
Applications will be processed as received. Spaces are limited so please send your application as soon as possible. 
Applications are due by 8 December 2014.

General enquiries can be addressed to ssar2015@acfr.usyd.edu.au.

We've finished constructing the Ladybird and successfully commissioned it on a commercial veggie farm near Cowra, New South Wales. In two parts, the videos show the construction, automation, data and processing.
 Read more...

In part 1, we show the construction and testing of the vehicle on a commercial vegetable near Cowra, New South Wales. The vehicle can drive autonomously up and down rows of a vegetable farm, gathering data that we think will be useful for growers to manage the farm. The Ladybird is a solar electric powered vehicle, and during our three day trip, we didn't need to charge the vehicle once. 

In part 2, we show some examples of the types of data we obtain and how it can be processed, to provide useful information to growers.

Managing snails with robots

Robert Fitch's presentation in Minlaton (SA) on  “Robotics in agriculture now, and a potential solution for robotic snail management on the YP” was featured in the Yorke Peninsula Country Times newspaper.

The Ladybird has captured the imagination of growers and the public alike, with online news and radio articles featured around Australia and globally. Links to stories here.

 

 

 

We show an end to end system for acquiring high resolution information to support precision agriculture in almond orchards.

The robot drives along the orchard rows autonomously, gathering laser and camera data while passing the trees. Each tree can be automatically identified, and information such as flower and fruit counts is produced. The information can be stored in a database, compared through the season and from one year to the next, mapped and displayed visually.

Our first full motion test of the ladybird outside.

We exercise the whole system for the first time, including translation, rotation and combined manoeuvres, including autonomous row alignment and following.

Meet our newest robot, the Ladybird! A robot we have designed and built for the vegetable industry. In this video, you can see the internal framework and components as it takes its first steps. Stay tuned for more videos as we develop the platform!
 Read more...

We have designed and built this robot as a new research platform to support Australia's vegetable industry. The omnidirectional wheel base allows traversal over most existing farm configurations, treading much more lightly over where existing tractor wheels currently run. In addition to the low weight of the vehicle, the ability to turn each wheel allows precision guidance and manoeuvrability, while minimising damage to the soil. In the undercarriage, the Ladybird carries a variety of optical sensors, including stereo and hyperspectral cameras, and the versatile robot arm enables development in a wide variety of applications, including spraying, weeding, thinning and of course to support harvesting research. We are looking forward to to our first tests on vegetable farm in the coming weeks.

 

We exhibited a selection of our robots at CeBIT, with an emphasis on the future of agriculture.

During our recent field trip to the Yarra Valley, we demonstrated autonomous row following for the trellis structured apple configuration. The system worked reliably, and we used it to gather data for yield prediction for approximately 30 rows of apples.
 Read more...

This video shows Shrimp driving fully autonomously in an apple orchard in the Yarra Valley, Australia. It uses a 360 degree lidar to guide it along the row (no need for GPS).

Unlike Mantis, the 2D lidars on Shrimp are looking sideways to scan the trees, so the 360 degree Velodyne sensor was used instead. To emulate a lower cost 2D lidar, only one of the 64 Velodyne lasers was processed. We used the autonomous system to obtain fruit yield data from approximately 30 rows of the farm without error.

This is a demonstration on our research platform, but the technology could easily be applied to any existing or new farm equipment, enabling smart farm vehicles to act as assistants to farmers.

We are interested in using robotic manipulators for harvesting and weeding applications.

This video from our field lab illustrates our concept for how a robot arm might look in performing a harvesting task.

This video shows some of the data and first processing from our recent trip to a banana plantation.

We gathered data from a banana plantation near Mareeba in the far north of Australia, at the end of 2013. Using Shrimp , we drove up and down rows of the plantation, acquiring 3D maps and image data.

Farmers typically use a system of coloured bags to denote the expected harvest date, which can be detected and mapped by the system. It is also hoped that in the future, growth rates of shoots or 'suckers' can be measured, to predict maturation times of the fruit directly, many months in advance.

We built and tested a prototype robotic aircraft and surveillance system to detect aquatic weeds in inaccessible habitats.

Read the report

Detecting Wheel Cacti
This project examined the role of unmanned aerial vehicles in detecting, classifying and mapping infestations of wheel cactus, Opuntia robusta, over large areas of rangelands in outback Australia.
 Read more...

Wheel cactus which is native and endemic to Mexico has now naturalised in South Australia, New South Wales and Victoria. It is often located in terrain which is difficult to access and monitoring and control by unmanned aerial vehicles and remote sensoring offers significant potential.

Read the report

One of our research ground vehicles, Mantis, was used to successfully demonstrate autonomy at an almond orchard.

The robot uses its forward looking laser to estimate the geometry of the tree foliage in front, enabling it to drive along the row without needing GPS. Additionally, it can detect people out in front, slowing down and coming to a safe halt.

The video shows a conceptual demonstration of how this could be used as a farmer assistance mechanism, whereby the vehicle could accompany a farmer, carrying heavy loads such as buckets of fruit, or towing other forms of equipment. Although demonstrated on one of our Perception Research Ground Vehicles (Mantis), the core technology can easily be applied to existing or new farm machinery.

GPS can be unreliable under canopied environments, due to occlusions between the vehicle and satellites. Therefore, forms of autonomy that require no GPS are likely to be more reliable.

We used a hexacopter to map and classify alligator weeds from an aerial perspective.

In this trial a light weight hexacopter was used to detect alligator weed infestation. The final map product can be opened using Google Earth and can help the weed controllers to locate the infestation.

 

"J3 Cub" the unmanned aerial vehicle (UAV) was used to detect and map various species the wood weed in Northern Queensland.
 Read more...

This trial aimed to provide a weed distribution map over a large area in Northern Queensland. During the trial we have mapped various woody weed including prickly acacia (Acacia nilotica), parkinsonia (Parkinsonia aculeate) and mesquite (Prosopis pallida). The map product can be used by the farmers to plan the control and eradication process.

 

Using camera data, we have developed algorithms to segment individual apples, and then use the apple count to perform apple yield estimation.
 Read more...

Images are collected as "Shrimp" surveys the orchard. The algorithm classify and count apples in each image and provide yield estimation for each row "Shrimp" surveyed. This yield estimation can give the farmer an early indication of potential yield and allows the farmer to refine and optimise the farm operation.

 

Using lidar (laser) data, individual trees can be segmented, counted and mapped, allowing information on the farm to be associated per tree.
 Read more...

As "Shrimp" drives along a row of an orchard, 3D maps are built from laser data. From this, we can segment and recognise individual trees, which is useful for data management. For example, when combined with our yield estimation techniques, it allows us to measure and associate the yield of each individual tree. This can be used to track information, such as yield, over time and it can be used actively, for example, to target autonomous or computer assisted spray trucks with spray programs for each tree.

 

 

 

"Shrimp" was successfully used in a trial at the University of Sydney Dairy, to remotely herd groups of 20 to 150 dairy cows.

The trial aimed to test the response of cows to the presence of a robot, and determine the feasibility of remote or autonomous herding using an unmanned ground vehicle. The cows were calm with the robot in their midst, and were willing to be herded into the dairy at a gentle pace, proving the potential of this technology. The story has captured the public's imagination, with media coverage around the world:Discovery Channel Canada, ABC Rural, BBC News, tested.com, cnet.com, International Business Times, and more.

 

 

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