TY - GEN
T1 - Mosquito pick-and-place
T2 - 15th IEEE International Conference on Automation Science and Engineering, CASE 2019
AU - Phalen, Henry
AU - Vagdargi, Prasad
AU - Pozin, Michael
AU - Chakravarty, Sumana
AU - Chirikjian, Gregory S.
AU - Iordachita, Iulian
AU - Taylor, Russell H.
N1 - Funding Information:
This work was supported by in part by NIH SBIR grant 1R44AI134500-01 in collaboration with Sanaria, Inc. Rockville, MD, USA and in part by Johns Hopkins University internal funds. Additionally, H. Phalen is supported by the NSF Graduate Research Fellowship under Grant No. DGE- 1746891
Funding Information:
*This work was supported by in part by NIH SBIR grant 1R44AI134500-01 in collaboration with Sanaria, Inc. Rockville, MD, USA and in part by Johns Hopkins University internal funds. Additionally, H. Phalen is supported by the NSF Graduate Research Fellowship under Grant No. DGE-1746891.
Funding Information:
ACKNOWLEDGMENT This work was supported by in part by NIH SBIR grant 1R44AI134500-01 and in part by Johns Hopkins University internal funds. H. Phalen is supported by NSF Grant No. DGE-1746891. The authors would like to thank Amrita Krishnaraj and Jiteng Mu for their preliminary efforts.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - The treatment of malaria is a global health challenge that stands to benefit from the widespread introduction of a vaccine for the disease. A method has been developed to create a live organism vaccine using the sporozoites (SPZ) of the parasite Plasmodium falciparum (Pf), which are concentrated in the salivary glands of infected mosquitoes. Current manual dissection methods to obtain these PfSPZ are not optimally efficient for large-scale vaccine production. We demonstrate the automation of a key step in this production process, the picking and placing of mosquitoes from a staging apparatus into a dissection assembly. This unit test of a robotic mosquito pick-and-place system is performed using a custom-designed micro-gripper attached to a four degree of freedom (4-DOF) robot under the guidance of a computer vision system. Mosquitoes are autonomously grasped from a mesh platform and pulled to a pair of notched dissection blades to remove the head of the mosquito, allowing access to the salivary glands. Placement into these blades is adapted based on output from computer vision to accommodate for the unique anatomy and orientation of each grasped mosquito. In this pilot test of the system on 50 mosquitoes, we demonstrate a 100% grasping accuracy and a 90% accuracy in placing the mosquito with its neck within the blade notches such that the head can be removed. This is a promising result for this difficult and non-standard pick-and-place task. An analysis of the failure cases provides insights for improvements to be implemented as this robotic pick-and-place system is integrated into a larger automated mosquito dissection system under development.
AB - The treatment of malaria is a global health challenge that stands to benefit from the widespread introduction of a vaccine for the disease. A method has been developed to create a live organism vaccine using the sporozoites (SPZ) of the parasite Plasmodium falciparum (Pf), which are concentrated in the salivary glands of infected mosquitoes. Current manual dissection methods to obtain these PfSPZ are not optimally efficient for large-scale vaccine production. We demonstrate the automation of a key step in this production process, the picking and placing of mosquitoes from a staging apparatus into a dissection assembly. This unit test of a robotic mosquito pick-and-place system is performed using a custom-designed micro-gripper attached to a four degree of freedom (4-DOF) robot under the guidance of a computer vision system. Mosquitoes are autonomously grasped from a mesh platform and pulled to a pair of notched dissection blades to remove the head of the mosquito, allowing access to the salivary glands. Placement into these blades is adapted based on output from computer vision to accommodate for the unique anatomy and orientation of each grasped mosquito. In this pilot test of the system on 50 mosquitoes, we demonstrate a 100% grasping accuracy and a 90% accuracy in placing the mosquito with its neck within the blade notches such that the head can be removed. This is a promising result for this difficult and non-standard pick-and-place task. An analysis of the failure cases provides insights for improvements to be implemented as this robotic pick-and-place system is integrated into a larger automated mosquito dissection system under development.
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U2 - 10.1109/COASE.2019.8842860
DO - 10.1109/COASE.2019.8842860
M3 - Conference contribution
AN - SCOPUS:85072947971
T3 - IEEE International Conference on Automation Science and Engineering
SP - 12
EP - 17
BT - 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019
PB - IEEE Computer Society
Y2 - 22 August 2019 through 26 August 2019
ER -