The use of dynamic sensing strategies to improve detection for a pepper harvesting robot
2018 (English)In: IEEE International Conference on Intelligent Robots and Systems. Proceedings, ISSN 2153-0858, E-ISSN 2153-0866, p. 8286-8293Article in journal (Refereed) Published
Abstract [en]
This paper presents the use of dynamic sensing strategies to improve detection results for a pepper harvesting robot. The algorithm decides if an additional viewpoint is needed and selects the best-fit viewpoint location from a predefined set of locations based on the predicted profitability of such an action. The suggestion of a possible additional viewpoint is based on image analysis for fruit and occlusion level detection, prediction of the expected number of additional targets sensed from that viewpoint, and final decision if choosing the additional viewpoint is beneficial. The developed heuristic was applied on 96 greenhouse images of 30 sweet peppers and resulted in up to 19% improved detection. The harvesting utility cost function decreased by up to 10% compared to the conventional single viewpoint strategy.
Place, publisher, year, edition, pages
IEEE Press, 2018. p. 8286-8293
Keywords [en]
agricultural products, agriculture, feature extraction, greenhouses, industrial robots, least squares approximations, object detection, profitability, robot vision, predicted profitability, viewpoint location, pepper harvesting robot, dynamic sensing strategies, harvesting utility cost function, occlusion level detection, fruit, image analysis, Robot sensing systems, Heuristic algorithms, Prediction algorithms, Cameras, Manipulators
National Category
Robotics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-79429DOI: 10.1109/IROS.2018.8593746ISI: 000458872707068Scopus ID: 2-s2.0-85063009363OAI: oai:DiVA.org:oru-79429DiVA, id: diva2:1388991
Conference
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, Oct. 1-5, 2018.
Funder
EU, Horizon 2020, 644313
Note
This research was supported by the European Commission (SWEEPERGA no. 644313) and by Ben-Gurion University of the Negev throughthe Helmsley Charitable Trust, the Agricultural, Biological and CognitiveRobotics Initiative, the Marcus Endowment Fund, and the Rabbi W. GuntherPlaut Chair in Manufacturing Engineering.
2020-01-282020-01-282022-02-11Bibliographically approved