Automated crop harvesting has rightfully captured many imaginations due to its tremendous potential in food production and green farming on a global scale. Professor Dan Zhang at York University’s Lassonde School of Engineering, Department of Mechanical Engineering, has excelled in this area of research. Today, with student Zhongxing Yang, the two researchers offer a hugely inventive green advancement in robotic harvesting.
Their latest research is based on a hybrid harvester, a robotic structure with a hybrid harvester that uses solar and wind energy. The machine is designed in such a way that it can lie flat under stormy conditions, then return to a working configuration after the storm.
“This robot could harvest solar/wind clean energy and is storm-safe through lie-flat posture, and it can be used as a radar antenna as well,” Zhang elaborates. “This improves the efficiency of energy collection and operational safety of the green industry.”
This research was funded by the Natural Sciences & Engineering Research Council of Canada (NSERC). The results were presented at a conference on materials science and engineering, then published in 2018 as part of the IOP Conference Series, under the title “Hybrid Harvester 3-RPS Robotic Parallel Manipulator.”
Primer on robots for agricultural purposes
Robots deployed for agricultural purposes are mainly used at the harvesting stage, although drones for preemptive weed control and even animal management and care (such as automatic milking) are other growth areas, according to Online Sciences.
Robots in agriculture have the potential to save farmers time and money. A few noteworthy advantages of robots in agriculture are:
- Speed, precision and efficiency: Since they don’t tire or become ill, robots can offer full field capacity. Compared to human harvesters, they make fewer errors and work more rapidly.
- New jobs: Harvesting robots may replace human harvesters/operators, but they can conversely create jobs for human workers who fix agricultural robots.
- Chemical safety and reduction: Robots can protect human workers from the harmful effects of handling chemicals by hand and, through a system of high spraying, they can reduce up to 80 per cent of a farm’s use of pesticides.
- Less impact on environment: In cases such as fruit picking, robots can reduce the environmental impact. (Source: Online Sciences.)
Zhang is a leader in robotics and automation
This is Zhang’s area of expertise. A Tier 1 York Research Chair in Advanced Robotics & Mechatronics and the Kaneff Research Chair in Advanced Robotics & Mechatronics in the Department of Mechanical Engineering, he is an internationally renowned expert in the areas of parallel robotic machines and their applications in manufacturing systems.
His research interests include robotics and mechatronics; high-performance parallel robotic machine development; sustainable/green manufacturing systems; micro/nano manipulation and MEMS devices (sensors); micro mobile robots and control of multi-robot co-operation; web-based remote manipulation; and rehabilitation and rescue robots.
Team sought to develop an algorithm for harvesting machine in outdoor working conditions
Using sophisticated calculations, Zhang and Yang worked to develop an algorithm that would allow the crop-harvesting robot to adapt to changing weather conditions and meet the dust-proof, load capacity and geometric requirements for outdoor working conditions.
Zhang and Yang were successful in designing a reconfigurable hybrid harvester that enables a parallel solar tracker to collect solar and wind energy at the same time, and has a lie-flat feature that protects the machine from storms and an enlarged workspace achieved by reconfiguration.
“Reconfiguration is vital. A minimum platform height algorithm has been successfully developed and demonstrated to find all eligible orientations of the platform,” Zhang explains. “The design is very efficient in terms of energy collection and it’s safe.”
Looking ahead to his future work and concurrent work in the field, Zhang says that artificial intelligence will be included in the design and will make the harvesting robot smarter. “A machine-learning algorithm will be developed to recognize the loading condition on the solar panel,” he says. “This could save the sensor cost of the model and help to promote the harvesters to the commercial market.”
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By Megan Mueller, senior manager, research communications, Office of the Vice-President Research & Innovation, York University, email@example.com