Abstract
Agricultural robots are subject to a much harsher environment than those in the factory or lab and control strategies need to take this into account while maintaining a low cycle time. Three control strategies were tested on Vegebot, a lettuce-picking robot, in both simulation and on the real robot. Between a fast open loop that was vulnerable to environmental noise and a slow but robust visual servoing technique, a Learned Open Loop strategy was tested where the robot learned from successful picks to pick at an intermediate speed. This reduced the projected cycle time from 31 s to 17.2 s, a 45% reduction.
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Acknowledgments
This project was possible thanks to EPSRC Grant EP/L015889/1, the Royal Society ERA Foundation Translation Award (TA160113), EPSRC Doctoral Training Program ICASE AwardRG84492 (cofunded by G’s Growers), EPSRC Small Partnership AwardRG86264 (in collaboration with G’s Growers), and the BBSRC Small Partnership GrantRG81275. Special thanks to G Growers, George Walker and Josie Hughes for their invaluable assistance.
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Birrell, S., Iida, F. (2021). Simulation, Learning and Control Methods to Improve Robotic Vegetable Harvesting. In: Siciliano, B., Laschi, C., Khatib, O. (eds) Experimental Robotics. ISER 2020. Springer Proceedings in Advanced Robotics, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-030-71151-1_17
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DOI: https://doi.org/10.1007/978-3-030-71151-1_17
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