Automated Planning, Dominant Orientation Templates, Differential Evolution, Locally Adaptive Regression Kernels, Elastic Band Path Optimization, Open Motion Planning, Policy Learning, SLAM,
With an ever increasing labor shortage and cost for harvesting fruits and vegetables, opportunities are ripe (get it?) for the introduction of mechanization solutions to assist farming operations better prepare for labor resourcing needs, more effectively predict harvesting costs, and scale more efficiently, resulting in greater yields and productivity. But while labor challenges are growing, decreasing costs in hardware along with a maturing set of robust, software algorithms are enabling a “tipping point” for the practical introduction of task-specific robotics to assist in such harvesting activities, specifically for the harvesting of bell peppers as is the focus of this work.
Current research efforts in the harvesting of bell peppers, and other short-plant commodities such as strawberries, rely upon all-in-one harvesting machines which are bulky, prohibitively expensive, and largely dependent upon specialized environments, such as greenhouses with predictive lighting and plant configurations. Further challenges hinder this single-point-of-failure approach, such as short power supplies along with expensive and time consuming repairs. This research project suggests a better way.
This project postulates that the widespread adoption of the mechanization of bell pepper harvesting will only become attainable with cheaper, more reliable robotic solutions which can easily scale to accommodate farming operations both large and small while supporting outdoor horticulture. Accordingly, at its core, this project proposes the development of two task-specific robots, a “harvester” to identify and collect bell peppers from plants and a “runner” to return harvested bell peppers to an aggregation point and to re-supply the harvester with power. The primary objective in the development of the robots is for them to be light, cheap and fast, using emerging software algorithms and techniques, such as person following, point cloud analysis, flexible object identification, and automated planning, for effective team coordination and robust pepper identification and harvesting in unstructured, outdoor horticulture. For scalability, the project will exhibit the ability to accommodate additional harvesters and runners into the harvesting plan, thus facilitating a dynamic team for increased productivity.
This objective will demonstrate that low-cost, robotic bell pepper harvesting is attainable, providing a practical alternative or augmentation to ever increasing labor challenges while better enabling farming operations to have predictable control over production.
The first series of posts associated with this project will be the review of current multi-agent project development methodologies, the selection of the methodology – or more likely mix of methodologies – for this project, and a breakdown of the requirements, accordingly.
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