NRI: Small: FRAIL-bots: Fragile cRop hArvest-aIding mobiLe robots
Mechanizing the hand harvesting of fresh market fragile crops constitutes one of the biggest challenges to the sustainability of the U.S. fruit and vegetable industry. Depending on the commodity, labor contributes up to 60% of the variable production cost and recent labor shortages have led to significant loss of production. Conventional mechanical and robotic harvesters have not successfully replaced the judgment, dexterity, and speed of experienced farm workers, at a competing cost. As an intermediate to complete mechanization, mechanical labor aids such as conveyor belts and mobile platforms have been introduced to increase worker productivity, by reducing the round-trip walking time to carry the harvested produce to the loading stations. However, the adoption of such labor aids has been very slow. They require large initial investment; field-to-field transport logistics are problematic due to their size, which also restricts their usage in large planar fields; they require skilled human operators, and changes in work practices; and productivity is increased at the cost of ergonomics.
The main objective of this project is to lay the scientific and technical foundations for developing teams of inexpensive, relatively small, harvest-aiding mobile robots. These co-bots will support human pickers by supplying them with empty containers and by transporting containers filled with harvested crops to unloading stations. The collective operation of these co-bots is envisioned to offer the services of an alternative, easy-to-deploy fast and robust transport system, which increases productivity, safety, and ergonomic metrics using as few robots as possible.
Intellectual merit. An innovative approach will be developed, which models the coupled operations of manual harvesting and crop and container transport, as the machine production and materials transport functions of a Flexible Manufacturing System (FMS). Based on this paradigm, stochastic models of the harvesting activities and robot fleet operations will be developed using formalism amenable to numerical optimization. Predicting the spatiotemporal distribution of future transport requests is essential for optimal robot dispatching. Unlike the FMS paradigm where machine production is modeled using probabilistic distributions, in this work the developed stochastic, manual-harvesting model – modulated by crop yield and human work patterns – will be integrated into a model predictive dispatcher that will dynamically match the fleet capacity with pickers’ current and predicted transportation demand. Since human operators are involved in all types of agricultural production systems, extensions of this approach could be utilized for optimizing agricultural field logistics of labor-intensive specialty crops as well as highly mechanized commodity crops.
Broader impact. The proposed robotic transport system aspires to offer financial benefits for U.S. fruit and vegetable farmers, market advantages for SMEs building advanced agricultural equipment, as well as increased safety for farmworkers. Agriculture is often – wrongly – perceived by pupils and students as a ‘low tech’ field. The truth is that agricultural equipment is getting more ‘mechatronic’ and farm management information systems are getting more complex. Hence, a new generation of researchers, engineers, and professionals will be needed to design, build, and operate future complex agricultural production systems. This educational and cultural challenge will be addressed by including K-12 and college students in the research activities of the project, and by incorporating its key findings in graduate curricula. Finally, the exposure of growers and unskilled, low-income farm laborers to robotic technology through the planned experiments and dissemination activities could help increase their openness to technology, and to the education and training required to utilize it.
Frailbots in Action
