Blog from February, 2014

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.

Alligator Weed Detection Using UAV
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.

 

Woody Weed Detection, Classification and Control
"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.

 

Apple Counting and Yield Estimation
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.