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.
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.
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.
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.
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.
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.
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.
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.
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.
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.